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Francesco Bartolucci

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2013. "Ranking Scientific Journals via Latent Class Models for Polytomous Item Response," EIEF Working Papers Series 1313, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.

    Mentioned in:

    1. > Economics Profession > Ranking in Economics > Ranking Methodology

Working papers

  1. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2021. "MCMC Conditional Maximum Likelihood for the two-way fixed-effects logit," MPRA Paper 110034, University Library of Munich, Germany.

    Cited by:

    1. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.

  2. Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020. "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper 106150, University Library of Munich, Germany.

    Cited by:

    1. Žikica Lukić & Bojana Milošević, 2024. "A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(5), pages 797-820, October.
    2. Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020. "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper 106150, University Library of Munich, Germany.
    3. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).

  3. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.

    Cited by:

    1. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    2. F. J. Clouth & S. Pauws & F. Mols & J. K. Vermunt, 2022. "A new three-step method for using inverse propensity weighting with latent class analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 351-371, June.

  4. Francesco Bartolucci & Claudia Pigini, 2018. "Partial effects estimation for fixed-effects logit panel data models," Working Papers 431, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    Cited by:

    1. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.
    2. Konstantin A. Kholodilin & Claus Michelsen, 2019. "Zehn Jahre nach dem großen Knall: wie ist es um die Stabilität der internationalen Immobilienmärkte bestellt? [Ten years after a Big Bang: How stable are the international housing markets?]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 5(1), pages 67-87, November.

  5. Silvia Bacci & Francesco Bartolucci & Giulia Bettin & Claudia Pigini, 2017. "A mixture growth model for migrants' remittances: An application to the German Socio-Economic Panel," Mo.Fi.R. Working Papers 145, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.

    Cited by:

    1. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.

  6. Bartolucci, Francesco & Pigini, Claudia, 2015. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," MPRA Paper 67030, University Library of Munich, Germany.

    Cited by:

    1. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2022. "Testing for state dependence in the fixed-effects ordered logit model," MPRA Paper 113890, University Library of Munich, Germany.
    2. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.
    3. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    4. Bartolucci, Francesco & Pigini, Claudia, 2019. "Partial effects estimation for fixed-effects logit panel data models," MPRA Paper 92243, University Library of Munich, Germany.
    5. Konstantin A. Kholodilin & Claus Michelsen, 2019. "Zehn Jahre nach dem großen Knall: wie ist es um die Stabilität der internationalen Immobilienmärkte bestellt? [Ten years after a Big Bang: How stable are the international housing markets?]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 5(1), pages 67-87, November.
    6. Pigini, Claudia & Bartolucci, Francesco, 2022. "Conditional inference for binary panel data models with predetermined covariates," Econometrics and Statistics, Elsevier, vol. 23(C), pages 83-104.
    7. Ravi Bapna & Alok Gupta & Gautam Ray & Shweta Singh, 2023. "Single-Sourcing vs. Multisourcing: An Empirical Analysis of Large Information Technology Outsourcing Arrangements," Information Systems Research, INFORMS, vol. 34(3), pages 1109-1130, September.
    8. Li, Wenhua & Adachi, Tsuyoshi, 2017. "Quantitative estimation of resource nationalism by binary choice logit model for panel data," Resources Policy, Elsevier, vol. 53(C), pages 247-258.
    9. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    10. Bartolucci, Francesco & Pigini, Claudia, 2017. "Granger causality in dynamic binary short panel data models," MPRA Paper 77486, University Library of Munich, Germany.

  7. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    Cited by:

    1. Shun Yu & Xianzheng Huang, 2017. "Random-intercept misspecification in generalized linear mixed models for binary responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 333-359, August.

  8. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.

    Cited by:

    1. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    2. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    3. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    4. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    5. F. J. Clouth & S. Pauws & F. Mols & J. K. Vermunt, 2022. "A new three-step method for using inverse propensity weighting with latent class analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 351-371, June.
    6. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.

  9. Minelli, Liliana & Pigini, Claudia & Chiavarini, Manuela & Bartolucci, Francesco, 2014. "Employment status and perceived health condition: longitudinal data from Italy," MPRA Paper 55788, University Library of Munich, Germany.

    Cited by:

    1. Katarzyna Piwowar-Sulej & Dominika Bąk-Grabowska, 2020. "Non-Permanent Employment and Employees’ Health in the Context of Sustainable HRM with a Focus on Poland," Social Sciences, MDPI, vol. 9(7), pages 1-23, July.
    2. Paolo Emilio Mistrulli & Tommaso Oliviero & Zeno Rotondi & Alberto Zazzaro, 2023. "Job Protection and Mortgage Conditions: Evidence from Italian Administrative Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(6), pages 1211-1237, December.
    3. Donatella Lanari & Giorgio d’Agostino & Luca Pieroni, 2022. "The unintended effects of increasing fixed-term employment on health," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 76(3), pages 17-28, July-Sept.
    4. Akanksha Choudhary & Ashish Singh, 2018. "Effect of intergenerational educational mobility on health of Indian women," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-16, September.
    5. Högnäs, Robin S. & Bijlsma, Maarten J. & Högnäs, Ulf & Blomqvist, Sandra & Westerlund, Hugo & Hanson, Linda Magnusson, 2022. "It's giving me the blues: A fixed-effects and g-formula approach to understanding job insecurity, sleep disturbances, and major depression," Social Science & Medicine, Elsevier, vol. 297(C).
    6. Yuxi Wang & Giovanni Fattore, 2020. "The impact of the great economic crisis on mental health care in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(8), pages 1259-1272, November.
    7. Katarzyna Piwowar-Sulej & Dominika Bąk-Grabowska, 2021. "The Impact of Mandate Contract and Self-Employment on Workers’ Health—Evidence from Poland," IJERPH, MDPI, vol. 18(6), pages 1-20, March.
    8. Duk Youn Cho & Jung-Wan Koo, 2018. "Differences in Metabolic Syndrome Prevalence by Employment Type and Sex," IJERPH, MDPI, vol. 15(9), pages 1-10, August.
    9. Abdallah Y Naser & Ian C K Wong & Cate Whittlesea & Hassan Alwafi & Amjad Abuirmeileh & Zahra Khalil Alsairafi & Fawaz Mohammad Turkistani & Nedaa Saud Bokhari & Maedeh Y Beykloo & Dalal Al-Taweel & M, 2019. "Attitudes and perceptions towards hypoglycaemia in patients with diabetes mellitus: A multinational cross-sectional study," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-16, October.
    10. Silvia Bacci & Claudia Pigini & Marco Seracini & Liliana Minelli, 2017. "Employment Condition, Economic Deprivation and Self-Evaluated Health in Europe: Evidence from EU-SILC 2009–2012," IJERPH, MDPI, vol. 14(2), pages 1-19, February.
    11. Chiara Mussida & Raffaella Patimo, 2021. "Women’s Family Care Responsibilities, Employment and Health: A Tale of Two Countries," Journal of Family and Economic Issues, Springer, vol. 42(3), pages 489-507, September.
    12. Giovanni Piumatti, 2020. "Longitudinal Trends in Self-Rated Health During Times of Economic Uncertainty in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(2), pages 599-633, April.

  10. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.

    Cited by:

    1. Franco Peracchi & Claudio Rossetti, 2019. "A Nonlinear Dynamic Factor Model of Health and Medical Treatment," CSEF Working Papers 524, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    2. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    3. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).

  11. Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2013. "Ranking Scientific Journals via Latent Class Models for Polytomous Item Response," EIEF Working Papers Series 1313, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.

    Cited by:

    1. Bertocchi, Graziella & Gambardella, Alfonso & Jappelli, Tullio & Nappi, Carmela A. & Peracchi, Franco, 2015. "Bibliometric evaluation vs. informed peer review: Evidence from Italy," Research Policy, Elsevier, vol. 44(2), pages 451-466.

  12. Francesco Bartolucci & Federico Belotti & Franco Peracchi, 2013. "Testing for Time-Invariant Unobserved Heterogeneity in Generalized Linear Models for Panel Data," EIEF Working Papers Series 1312, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.

    Cited by:

    1. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Franco Peracchi & Claudio Rossetti, 2019. "A Nonlinear Dynamic Factor Model of Health and Medical Treatment," CSEF Working Papers 524, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    3. González, Maximiliano & Guzmán, Alexander & Téllez, Diego Fernando & Trujillo, María Andrea, 2021. "What you say and how you say it: Information disclosure in Latin American firms," Journal of Business Research, Elsevier, vol. 127(C), pages 427-443.
    4. Ramos-Herrera, María del Carmen & Sosvilla-Rivero, Simón, 2023. "Economic growth and deviations from the equilibrium exchange rate," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 764-786.
    5. Claudia Pigini & Alessandro Pionati & Francesco Valentini, 2023. "Specification testing with grouped fixed effects," Papers 2310.01950, arXiv.org.
    6. Melik Ertugrul & Volkan Demir, 2018. "How Does Unobserved Heterogeneity Affect Value Relevance?," Australian Accounting Review, CPA Australia, vol. 28(2), pages 288-301, June.
    7. Zhang, Qizheng & Qian, Zesen & Wang, Shuo & Yuan, Lingran & Gong, Binlei, 2022. "Productivity drain or productivity gain? The effect of new technology adoption in the oilfield market," Energy Economics, Elsevier, vol. 108(C).
    8. Giuseppe De Arcangelis & Edoardo Di Porto & Gianluca Santoni, 2015. "Migration, Labor Tasks and Production Structure," CSEF Working Papers 390, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    9. Lukáš Čechura & Zdeňka Žáková Kroupová, 2021. "Technical Efficiency in the European Dairy Industry: Can We Observe Systematic Failures in the Efficiency of Input Use?," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    10. Vincenzo Atella & Federico Belotti & Domenico Depalo & Andrea Piano Mortari, 2013. "Measuring spatial effects in presence of institutional constraints: the case of Italian Local Health Authority expenditure," CEIS Research Paper 278, Tor Vergata University, CEIS, revised 08 May 2013.
    11. Bartolucci, Francesco & Bacci, Silvia & Pigini, Claudia, 2017. "Misspecification test for random effects in generalized linear finite-mixture models for clustered binary and ordered data," Econometrics and Statistics, Elsevier, vol. 3(C), pages 112-131.
    12. Sanogo, Tiangboho, 2019. "Does fiscal decentralization enhance citizens’ access to public services and reduce poverty? Evidence from Côte d’Ivoire municipalities in a conflict setting," World Development, Elsevier, vol. 113(C), pages 204-221.
    13. Tiangboho Sanogo, 2017. "Does fiscal decentralization enhance citizens’ access to public services and reduce poverty? Evidence from a conflict setting," Working Papers halshs-01582478, HAL.
    14. Samer Hamidi & Fevzi Akinci, 2016. "Measuring Efficiency of Health Systems of the Middle East and North Africa (MENA) Region Using Stochastic Frontier Analysis," Applied Health Economics and Health Policy, Springer, vol. 14(3), pages 337-347, June.
    15. Jean-François Brun & Tiangboho Sanogo, 2017. "Effect of central transfers on municipalities' own revenue mobilization: Do conflict and local revenue management matter?," CERDI Working papers halshs-01613108, HAL.
    16. Jean-François BRUN & Tiangboho SANOGO, 2017. "Effect of central transfers on municipalities’ own revenue mobilization: Do conflict and local revenue management matter?," Working Papers 201716, CERDI.
    17. Shun Yu & Xianzheng Huang, 2017. "Random-intercept misspecification in generalized linear mixed models for binary responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 333-359, August.
    18. Tiangboho SANOGO, 2017. "Does fiscal decentralization enhance citizens’ access to public services and reduce poverty? Evidence from a conflict setting," Working Papers 201715, CERDI.
    19. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "The existence and persistence of household financial hardship: A Bayesian multivariate dynamic logit framework," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 285-298.

  13. Bacci, Silvia & Bartolucci, Francesco & Pieroni, Luca, 2012. "A causal analysis of mother’s education on birth inequalities," MPRA Paper 38754, University Library of Munich, Germany.

    Cited by:

    1. Salmasi, Luca & Pieroni, Luca, 2015. "Immigration policy and birth weight: Positive externalities in Italian law," Journal of Health Economics, Elsevier, vol. 43(C), pages 128-139.

  14. Bartolucci, Francesco & Giorgio E., Montanari & Pandolfi, Silvia, 2012. "Item selection by an extended Latent Class model: An application to nursing homes evaluation," MPRA Paper 38757, University Library of Munich, Germany.

    Cited by:

    1. Pieroni, Luca & d'Agostino, Giorgio & Bartolucci, Francesco, 2013. "Identifying corruption through latent class models: evidence from transition economies," MPRA Paper 43981, University Library of Munich, Germany.
    2. Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2018. "Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 172-193, April.
    3. Giorgio d’Agostino & Luca Pieroni, 2019. "Modelling Corruption Perceptions: Evidence from Eastern Europe and Central Asian Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 311-341, February.

  15. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.

    Cited by:

    1. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    2. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    3. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    4. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    5. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    6. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    7. Esther Acquah & Lorenzo Carbonari & Alessio Farcomeni & Giovanni Trovato, 2021. "Institutions and Economic Development: New Measurements and Evidence," Working Paper series 21-15, Rimini Centre for Economic Analysis.
    8. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    9. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    10. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 273-300, June.
    11. Ulf Böckenholt & Blakeley McShane, 2014. "Comments on: Latent Markov models: a review of the general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 469-472, September.
    12. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    13. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    14. Lorenzo Carbonari & Alessio Farcomeni & Cosimo Petracchi & Giovanni Trovato, 2024. "Macroprudential Policies and Credit Volatility," Working Paper series 24-16, Rimini Centre for Economic Analysis.
    15. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.
    16. Antonio Punzo & Salvatore Ingrassia & Antonello Maruotti, 2021. "Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions," Statistical Papers, Springer, vol. 62(3), pages 1519-1555, June.
    17. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2017. "Rectangular latent Markov models for time-specific clustering," Working Papers tecipa-589, University of Toronto, Department of Economics.
    18. Marino, Maria Francesca & Alfó, Marco, 2016. "Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 193-209.
    19. Ingmar Visser & Maarten Speekenbrink, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 478-483, September.
    20. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    21. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.
    22. Lekkas, Peter & Paquet, Catherine & Howard, Natasha J. & Daniel, Mark, 2017. "Illuminating the lifecourse of place in the longitudinal study of neighbourhoods and health," Social Science & Medicine, Elsevier, vol. 177(C), pages 239-247.
    23. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
    24. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    25. Hans Jørn Juhl & Morten H. J. Fenger & John Thøgersen, 2017. "Will the Consistent Organic Food Consumer Step Forward? An Empirical Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(3), pages 519-535.
    26. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    27. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.
    28. Luca Brusa & Francesco Bartolucci & Fulvia Pennoni, 2023. "Tempered expectation-maximization algorithm for the estimation of discrete latent variable models," Computational Statistics, Springer, vol. 38(3), pages 1391-1424, September.

  16. Francesco Bartolucci & Valentina Nigro, 2007. "Maximum likelihood estimation of an extended latent markov model for clustered binary panel data," CEIS Research Paper 96, Tor Vergata University, CEIS.

    Cited by:

    1. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
    2. Bartolucci, Francesco & Montanari, Giorgio E. & Pandolfi, Silvia, 2015. "Three-step estimation of latent Markov models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 287-301.
    3. Bartolucci, Francesco & Lupparelli, Monia, 2012. "Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data," MPRA Paper 40588, University Library of Munich, Germany.

  17. Francesco Bartolucci† & Valentina Nigro, 2007. "A dynamic model for binary panel data with unobserved heterogeneity admitting a Vn-consistent conditional estimator," CEIS Research Paper 97, Tor Vergata University, CEIS.

    Cited by:

    1. Sven Schreiber & Miriam Beblo, 2016. "Leisure and Housing Consumption after Retirement: New Evidence on the Life-Cycle Hypothesis," SOEPpapers on Multidisciplinary Panel Data Research 849, DIW Berlin, The German Socio-Economic Panel (SOEP).
    2. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.

Articles

  1. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2024. "MCMC conditional maximum likelihood for the two-way fixed-effects logit," Econometric Reviews, Taylor & Francis Journals, vol. 43(6), pages 379-404, July.
    See citations under working paper version above.
  2. Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022. "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(1), February.
    See citations under working paper version above.
  3. Pigini, Claudia & Bartolucci, Francesco, 2022. "Conditional inference for binary panel data models with predetermined covariates," Econometrics and Statistics, Elsevier, vol. 23(C), pages 83-104.

    Cited by:

    1. Stéphane Bonhomme & Kevin Dano & Bryan S. Graham, 2023. "Identification in a binary choice panel data model with a predetermined covariate," CeMMAP working papers 17/23, Institute for Fiscal Studies.

  4. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.

    Cited by:

    1. David Aristei & Manuela Gallo, 2021. "Financial Knowledge, Confidence, and Sustainable Financial Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.

  5. Federica Bianchi & Francesco Bartolucci & Stefano Peluso & Antonietta Mira, 2020. "Longitudinal networks of dyadic relationships using latent trajectories: evidence from the European interbank market," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 711-739, August.

    Cited by:

    1. Carolina E S Mattsson & Teodoro Criscione & Frank W Takes, 2022. "Circulation of a digital community currency," Papers 2207.08941, arXiv.org, revised Jun 2023.
    2. Diego P. Guisande & Maretno Agus Harjoto & Andreas G. F. Hoepner & Conall O’Sullivan, 2024. "Ethics and Banking: Do Banks Divest Their Kind?," Journal of Business Ethics, Springer, vol. 192(1), pages 191-223, June.

  6. Silvia Bacci & Francesco Bartolucci & Giulia Bettin & Claudia Pigini, 2019. "A latent class growth model for migrants’ remittances: an application to the German Socio‐Economic Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1607-1632, October.

    Cited by:

    1. Etilé, Fabrice & Frijters, Paul & Johnston, David W. & Shields, Michael A., 2021. "Measuring resilience to major life events," LSE Research Online Documents on Economics 112526, London School of Economics and Political Science, LSE Library.
    2. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.

  7. Bartolucci, Francesco & Bacci, Silvia & Mira, Antonietta, 2018. "On the role of latent variable models in the era of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 165-169.

    Cited by:

    1. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.

  8. Francesco Bartolucci & Misbah T. Choudhry & Enrico Marelli & Marcello Signorelli, 2018. "GDP dynamics and unemployment changes in developed and developing countries," Applied Economics, Taylor & Francis Journals, vol. 50(31), pages 3338-3356, July.

    Cited by:

    1. de Mendonça, Helder Ferreira & de Oliveira, Diego S.P., 2019. "Firms' confidence and Okun's law in OECD countries," Economic Modelling, Elsevier, vol. 78(C), pages 98-107.
    2. Woo, Jaejoon, 2023. "Revisiting Okun's law in South Korea: Asymmetries, crises, and structural changes," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    3. Munyanyi, Musharavati Ephraim & Awaworyi Churchill, Sefa, 2022. "Foreign aid and energy poverty: Sub-national evidence from Senegal," Energy Economics, Elsevier, vol. 108(C).
    4. Mahmoud Kraim & Tamat Sarmidi & Fathin Faizah & Norlin Khalid, 2023. "A sectoral specification of Okun’s law in oil-producing countries: evidence from panel ARDL model," Economic Change and Restructuring, Springer, vol. 56(4), pages 2385-2404, August.
    5. Liu, Zhen & Ngo, Thanh Quang & Saydaliev, Hayot Berk & He, Huiyuan & Ali, Sajid, 2022. "How do trade openness, public expenditure and institutional performance affect unemployment in OIC countries? Evidence from the DCCE approach," Economic Systems, Elsevier, vol. 46(4).
    6. Asma Raies, 2023. "Sustainable Employment in Developing and Emerging Countries: Testing Augmented Okun’s Law in Light of Institutional Quality," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    7. Shabir Mohsin Hashmi & Ali Gul Khushik & Muhammad Akram Gilal & Zhao Yongliang, 2021. "The Impact of GDP and Its Expenditure Components on Unemployment Within BRICS Countries: Evidence of Okun’s Law From Aggregate and Disaggregated Approaches," SAGE Open, , vol. 11(2), pages 21582440211, June.
    8. Marina Checa-Olivas & Bladimir de la Hoz-Rosales & Rafael Cano-Guervos, 2021. "The Impact of Employment Quality and Housing Quality on Human Development in the European Union," Sustainability, MDPI, vol. 13(2), pages 1-12, January.
    9. Mihaela Simionescu, 2021. "Italexit and the Impact of Immigrants from Italy on the Italian Labor Market," JRFM, MDPI, vol. 14(1), pages 1-14, January.
    10. Jorge Chica‐Olmo & Marina Checa‐Olivas, 2021. "Spatial impact of factors influencing the achievement of the Europa2020 employment targets," Papers in Regional Science, Wiley Blackwell, vol. 100(3), pages 633-649, June.
    11. Fan Hu & Zidong An, 2024. "The Buffering Effect of Higher Education Expansion on the Okun’s Law in China," Hacienda Pública Española / Review of Public Economics, IEF, vol. 250(3), pages 89-107, September.
    12. Scalamonti, Francesco, 2024. "The foreign investments-growth nexus in underdeveloped countries: the state-of-art of research analysing a selected and recent empirical literature (2020-2022)," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

  9. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.

    Cited by:

    1. Franco Peracchi & Claudio Rossetti, 2019. "A Nonlinear Dynamic Factor Model of Health and Medical Treatment," CSEF Working Papers 524, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    2. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2022. "Testing for state dependence in the fixed-effects ordered logit model," MPRA Paper 113890, University Library of Munich, Germany.
    3. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.

  10. Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2018. "Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 172-193, April.

    Cited by:

    1. Simone Del Sarto & Michela Gnaldi, 2022. "Spare time use: profiles of Italian Millennials (beyond the media hype)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1403-1428, December.
    2. Robitzsch, Alexander, 2020. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45, Center for Open Science.

  11. Bartolucci, Francesco & Marino, Maria Francesca & Pandolfi, Silvia, 2018. "Dealing with reciprocity in dynamic stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 86-100.

    Cited by:

    1. Marino, Maria Francesca & Pandolfi, Silvia, 2022. "Hybrid maximum likelihood inference for stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    2. Chabert-Liddell, Saint-Clair & Barbillon, Pierre & Donnet, Sophie & Lazega, Emmanuel, 2021. "A stochastic block model approach for the analysis of multilevel networks: An application to the sociology of organizations," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

  12. Francesco Bartolucci & Alessio Farcomeni & Luisa Scaccia, 2017. "A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 952-978, December.

    Cited by:

    1. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    2. Jules L. Ellis & Klaas Sijtsma, 2023. "A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 387-412, June.

  13. Bartolucci, Francesco & Bacci, Silvia & Pigini, Claudia, 2017. "Misspecification test for random effects in generalized linear finite-mixture models for clustered binary and ordered data," Econometrics and Statistics, Elsevier, vol. 3(C), pages 112-131.

    Cited by:

    1. Antonello Maruotti & Pierfrancesco Alaimo Di Loro, 2023. "CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    2. Kruse, René-Marcel & Silbersdorff, Alexander & Säfken, Benjamin, 2022. "Model averaging for linear mixed models via augmented Lagrangian," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).

  14. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.

    Cited by:

    1. Blanc-Blocquel, Augusto & Ortiz-Gracia, Luis & Oviedo, Rodolfo, 2024. "Efficient likelihood estimation of Heston model for novel climate-related financial contracts valuation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 225(C), pages 430-445.
    2. Prateek Bansal & Vahid Keshavarzzadeh & Angelo Guevara & Shanjun Li & Ricardo A Daziano, 2022. "Designed quadrature to approximate integrals in maximum simulated likelihood estimation [Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariat," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 301-321.
    3. Bianconcini, Silvia & Cagnone, Silvia, 2023. "The dimension-wise quadrature estimation of dynamic latent variable models for count data," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

  15. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    See citations under working paper version above.
  16. Francesco Bartolucci & Giovanni S F Bruno & Olga Demidova & Marcello Signorelli, 2017. "Job satisfaction and compensating wage differentials: Evidence from Russia," CESifo Economic Studies, CESifo Group, vol. 63(3), pages 333-351.

    Cited by:

    1. Dubnovitskaya, Anastasia, 2021. "Who is satisfied with their pay? Evidence from the Russian Longitudinal Monitoring Survey," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 49-69.

  17. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2016. "Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 146-179, April.
    See citations under working paper version above.
  18. Francesco Bartolucci & Monia Lupparelli, 2016. "Pairwise Likelihood Inference for Nested Hidden Markov Chain Models for Multilevel Longitudinal Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 216-228, March.

    Cited by:

    1. Montanari, Giorgio E. & Doretti, Marco & Bartolucci, Francesco, 2017. "A multilevel latent Markov model for the evaluation of nursing homes' performance," MPRA Paper 80691, University Library of Munich, Germany.
    2. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    3. Bartolucci, Francesco & Bacci, Silvia & Mira, Antonietta, 2018. "On the role of latent variable models in the era of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 165-169.
    4. Ruijin Lu & Tonja R. Nansel & Zhen Chen, 2023. "A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 288-308, April.
    5. Xia, Ye-Mao & Tang, Nian-Sheng, 2019. "Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 190-211.
    6. Bartolucci, Francesco & Marino, Maria Francesca & Pandolfi, Silvia, 2015. "Composite likelihood inference for hidden Markov models for dynamic networks," MPRA Paper 67242, University Library of Munich, Germany.
    7. Giorgio Eduardo Montanari & Marco Doretti & Maria Francesca Marino, 2022. "Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 457-485, June.

  19. F. Bartolucci & R. Bellio & A. Salvan & N. Sartori, 2016. "Modified Profile Likelihood for Fixed-Effects Panel Data Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1271-1289, August.

    Cited by:

    1. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Kunz, Johannes S. & Staub, Kevin E. & Winkelmann, Rainer, 2017. "Estimating Fixed Effects: Perfect Prediction and Bias in Binary Response Panel Models, with an Application to the Hospital Readmissions Reduction Program," IZA Discussion Papers 11182, Institute of Labor Economics (IZA).
    3. Gao, Wei & Bergsma, Wicher & Yao, Qiwei, 2017. "Estimation for dynamic and static panel probit models with large individual effects," LSE Research Online Documents on Economics 65165, London School of Economics and Political Science, LSE Library.
    4. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    5. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.
    6. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    7. Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2021. "Predicting Individual Effects in Fixed Effects Panel Probit Models," SoDa Laboratories Working Paper Series 2021-05, Monash University, SoDa Laboratories.
    8. Alexander Chudik & M. Hashem Pesaran & Jui‐Chung Yang, 2018. "Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 816-836, September.
    9. Lai, Hung-pin & Kumbhakar, Subal C., 2019. "Technical and allocative efficiency in a panel stochastic production frontier system model," European Journal of Operational Research, Elsevier, vol. 278(1), pages 255-265.
    10. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    11. Al-Sadoon, Majid M. & Li, Tong & Pesaran, M. Hashem, 2012. "An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects," IZA Discussion Papers 7054, Institute of Labor Economics (IZA).
    12. Giuliana Cortese & Nicola Sartori, 2016. "Integrated likelihoods in parametric survival models for highly clustered censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 382-404, July.
    13. Pigini, Claudia & Bartolucci, Francesco, 2022. "Conditional inference for binary panel data models with predetermined covariates," Econometrics and Statistics, Elsevier, vol. 23(C), pages 83-104.
    14. Riccardo Lucchetti & Claudia Pigini, 2018. "Dynamic panel probit: finite-sample performance of alternative random-effects estimators," Working Papers 426, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    15. Riccardo (Jack) Lucchetti & Claudia Pigini, 2020. "Choice of solutions to the initial-conditions problem in dynamic panel probit models," Working Papers 2020:27, Department of Economics, University of Venice "Ca' Foscari".

  20. Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2016. "Item selection by latent class-based methods: an application to nursing home evaluation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 245-262, June.

    Cited by:

    1. Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2018. "Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 172-193, April.

  21. Francesco Bartolucci & Alessio Farcomeni, 2015. "A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates," Biometrics, The International Biometric Society, vol. 71(1), pages 80-89, March.

    Cited by:

    1. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    2. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    3. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    4. Maria Marino & Marco Alfó, 2015. "Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 483-502, December.
    5. Joan Gil & Paolo Li Donni & Eugenio Zucchelli, 2018. "Uncontrolled diabetes and health care utilisation: a bivariate Latent Markov model approach," UB School of Economics Working Papers 2018/382, University of Barcelona School of Economics.
    6. Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2019. "Dynamic discrete mixtures for high frequency prices," Discussion Papers 19/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    7. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    8. Maruotti, Antonello & Punzo, Antonio, 2017. "Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 475-496.
    9. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.
    10. Marino, Maria Francesca & Alfó, Marco, 2016. "Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 193-209.

  22. Bartolucci, Francesco & Montanari, Giorgio E. & Pandolfi, Silvia, 2015. "Three-step estimation of latent Markov models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 287-301.

    Cited by:

    1. Montanari, Giorgio E. & Doretti, Marco & Bartolucci, Francesco, 2017. "A multilevel latent Markov model for the evaluation of nursing homes' performance," MPRA Paper 80691, University Library of Munich, Germany.
    2. Giorgio E. Montanari & Silvia Pandolfi, 2018. "Evaluation of long-term health care services through a latent Markov model with covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 151-173, March.
    3. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 273-300, June.
    4. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    5. Catania, Leopoldo & Di Mari, Roberto, 2021. "Hierarchical Markov-switching models for multivariate integer-valued time-series," Journal of Econometrics, Elsevier, vol. 221(1), pages 118-137.
    6. Di Mari, Roberto & Bakk, Zsuzsa & Oser, Jennifer & Kuha, Jouni, 2023. "A two-step estimator for multilevel latent class analysis with covariates," LSE Research Online Documents on Economics 119994, London School of Economics and Political Science, LSE Library.
    7. Giorgio Eduardo Montanari & Marco Doretti & Maria Francesca Marino, 2022. "Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 457-485, June.

  23. Francesco Bartolucci, 2015. "A comparison between the g-index and the h-index based on concentration," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2708-2710, December.

    Cited by:

    1. Ratikant Bhaskar & Okey Peter Onyia & Dharen Kumar Pandey & S. Ananda, 2023. "Navigating the complexities of financial services marketing through a bibliometric analysis of the Journal of Financial Services Marketing (2009–2022)," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 724-747, December.
    2. Lucio Bertoli-Barsotti, 2016. "Normalizing the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 645-655, February.

  24. Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2015. "Ranking scientific journals via latent class models for polytomous item response data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 1025-1049, October.

    Cited by:

    1. Erich Battistin & Marco Ovidi, 2021. "Rising Stars," DISCE - Working Papers del Dipartimento di Economia e Finanza def106, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    2. Cristiano Varin & Manuela Cattelan & David Firth, 2016. "Statistical modelling of citation exchange between statistics journals," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 1-63, January.
    3. Erich Battistin & Marco Ovidi, 2022. "Rising Stars: Expert Reviews and Reputational Yardsticks in the Research Excellence Framework," Economica, London School of Economics and Political Science, vol. 89(356), pages 830-848, October.

  25. Bartolucci, Francesco & Belotti, Federico & Peracchi, Franco, 2015. "Testing for time-invariant unobserved heterogeneity in generalized linear models for panel data," Journal of Econometrics, Elsevier, vol. 184(1), pages 111-123.
    See citations under working paper version above.
  26. Bartolucci Francesco & Murphy Thomas Brendan, 2015. "A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(4), pages 193-203, December.

    Cited by:

    1. Fogliato Riccardo & Oliveira Natalia L. & Yurko Ronald, 2021. "TRAP: a predictive framework for the Assessment of Performance in Trail Running," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 129-143, June.
    2. Silvia Bacci & Francesco Bartolucci & Giulia Bettin & Claudia Pigini, 2017. "A mixture growth model for migrants' remittances: An application to the German Socio-Economic Panel," Mo.Fi.R. Working Papers 145, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    3. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.
    4. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.

  27. Marco Alfó & Francesco Bartolucci, 2015. "Latent variable models for the analysis of socio-economic data," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 151-154, August.

    Cited by:

    1. W. Hölzl & S. Kaniovski & Y. Kaniovski, 2019. "Exploring the dynamics of business survey data using Markov models," Computational Management Science, Springer, vol. 16(4), pages 621-649, October.

  28. Pandolfi, Silvia & Bartolucci, Francesco & Friel, Nial, 2014. "A generalized multiple-try version of the Reversible Jump algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 298-314.

    Cited by:

    1. Mike K. P. So & Wing Ki Liu & Amanda M. Y. Chu, 2018. "Bayesian Shrinkage Estimation Of Time-Varying Covariance Matrices In Financial Time Series," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 369-404, December.
    2. Xin Luo & Håkon Tjelmeland, 2019. "A multiple-try Metropolis–Hastings algorithm with tailored proposals," Computational Statistics, Springer, vol. 34(3), pages 1109-1133, September.

  29. Bacci, Silvia & Bartolucci, Francesco, 2014. "Mixtures of equispaced normal distributions and their use for testing symmetry with univariate data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 262-272.

    Cited by:

    1. Lyubchich, Vyacheslav & Wang, Xingyu & Heyes, Andrew & Gel, Yulia R., 2016. "A distribution-free m-out-of-n bootstrap approach to testing symmetry about an unknown median," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 1-9.

  30. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
    See citations under working paper version above.
  31. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.

    Cited by:

    1. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    2. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    3. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    4. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    5. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    6. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    7. Esther Acquah & Lorenzo Carbonari & Alessio Farcomeni & Giovanni Trovato, 2021. "Institutions and Economic Development: New Measurements and Evidence," Working Paper series 21-15, Rimini Centre for Economic Analysis.
    8. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    9. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    10. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 273-300, June.
    11. Ulf Böckenholt & Blakeley McShane, 2014. "Comments on: Latent Markov models: a review of the general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 469-472, September.
    12. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    13. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    14. Lorenzo Carbonari & Alessio Farcomeni & Cosimo Petracchi & Giovanni Trovato, 2024. "Macroprudential Policies and Credit Volatility," Working Paper series 24-16, Rimini Centre for Economic Analysis.
    15. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.
    16. Antonio Punzo & Salvatore Ingrassia & Antonello Maruotti, 2021. "Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions," Statistical Papers, Springer, vol. 62(3), pages 1519-1555, June.
    17. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2017. "Rectangular latent Markov models for time-specific clustering," Working Papers tecipa-589, University of Toronto, Department of Economics.
    18. Ingmar Visser & Maarten Speekenbrink, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 478-483, September.
    19. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    20. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.
    21. Lekkas, Peter & Paquet, Catherine & Howard, Natasha J. & Daniel, Mark, 2017. "Illuminating the lifecourse of place in the longitudinal study of neighbourhoods and health," Social Science & Medicine, Elsevier, vol. 177(C), pages 239-247.
    22. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    23. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    24. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.
    25. Luca Brusa & Francesco Bartolucci & Fulvia Pennoni, 2023. "Tempered expectation-maximization algorithm for the estimation of discrete latent variable models," Computational Statistics, Springer, vol. 38(3), pages 1391-1424, September.

  32. Bartolucci, Francesco & Bacci, Silvia & Gnaldi, Michela, 2014. "MultiLCIRT: An R package for multidimensional latent class item response models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 971-985.

    Cited by:

    1. Michael Brusco & Hans-Friedrich Köhn & Douglas Steinley, 2015. "An Exact Method for Partitioning Dichotomous Items Within the Framework of the Monotone Homogeneity Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 949-967, December.
    2. Genge, Ewa & Bartolucci, Francesco, 2019. "Are attitudes towards immigration changing in Europe? An analysis based on bidimensional latent class IRT models," MPRA Paper 94672, University Library of Munich, Germany.
    3. Favaro, Donata & Sciulli, Dario & Bartolucci, Francesco, 2020. "Primary-school class composition and the development of social capital," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    4. Chiara Dal Bianco & Omar Paccagnella & Roberta Varriale, 2016. "A multilevel latent class analysis of the purchasing channels among European consumers," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 293-309, December.
    5. Ping Chen & Chun Wang, 2021. "Using EM Algorithm for Finite Mixtures and Reformed Supplemented EM for MIRT Calibration," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 299-326, March.
    6. Michela Gnaldi & Simone Del Sarto, 2018. "Variable Weighting via Multidimensional IRT Models in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 1139-1156, April.
    7. Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2013. "Ranking Scientific Journals via Latent Class Models for Polytomous Item Response," EIEF Working Papers Series 1313, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.
    8. Silvia Bacci & Michela Gnaldi, 2015. "A classification of university courses based on students’ satisfaction: an application of a two-level mixture item response model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 927-940, May.
    9. Ewa Genge, 2021. "LC and LC-IRT Models in the Identification of Polish Households with Similar Perception of Financial Position," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    10. Francesco Bartolucci & Alessio Farcomeni & Luisa Scaccia, 2017. "A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 952-978, December.
    11. Leonard Paas & Tammo Bijmolt & Jeroen Vermunt, 2015. "Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 249-262, August.
    12. Ewa Genge & Francesco Bartolucci, 2022. "Are attitudes toward immigration changing in Europe? An analysis based on latent class IRT models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 235-271, June.
    13. Luca Brusa & Francesco Bartolucci & Fulvia Pennoni, 2023. "Tempered expectation-maximization algorithm for the estimation of discrete latent variable models," Computational Statistics, Springer, vol. 38(3), pages 1391-1424, September.
    14. Michela Gnaldi, 2017. "A multidimensional IRT approach for dimensionality assessment of standardised students’ tests in mathematics," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1167-1182, May.

  33. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.

    Cited by:

    1. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Pei Wang & Erin L. Abner & Changrui Liu & David W. Fardo & Frederick A. Schmitt & Gregory A. Jicha & Linda J. Van Eldik & Richard J. Kryscio, 2023. "Estimating random effects in a finite Markov chain with absorbing states: Application to cognitive data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(3), pages 304-321, August.
    3. Xia, Ye-Mao & Tang, Nian-Sheng & Gou, Jian-Wei, 2016. "Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 259-275.
    4. William H. Greene & Mark N. Harris & Rachel J. Knott & Nigel Rice, 2021. "Specification and testing of hierarchical ordered response models with anchoring vignettes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 31-64, January.
    5. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
    6. Silvia Bianconcini, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 466-468, September.
    7. Joan Gil & Paolo Li Donni & Eugenio Zucchelli, 2018. "Uncontrolled diabetes and health care utilisation: a bivariate Latent Markov model approach," UB School of Economics Working Papers 2018/382, University of Barcelona School of Economics.
    8. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 273-300, June.
    9. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    10. Bartolucci, Francesco & Bacci, Silvia & Pigini, Claudia, 2017. "Misspecification test for random effects in generalized linear finite-mixture models for clustered binary and ordered data," Econometrics and Statistics, Elsevier, vol. 3(C), pages 112-131.
    11. Cagnone, Silvia & Bartolucci, Francesco, 2013. "Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data," MPRA Paper 51037, University Library of Munich, Germany.
    12. Francesca Bassi, 2016. "Dynamic segmentation with growth mixture models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 263-279, June.
    13. Giorgio E. Montanari & Marco Doretti, 2019. "Ranking Nursing Homes’ Performances Through a Latent Markov Model with Fixed and Random Effects," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 307-326, November.
    14. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 117-143, March.
    15. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
    16. Giovanni Piumatti, 2020. "Longitudinal Trends in Self-Rated Health During Times of Economic Uncertainty in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(2), pages 599-633, April.

  34. F. Bartolucci & G. Montanari & S. Pandolfi, 2012. "Dimensionality of the Latent Structure and Item Selection Via Latent Class Multidimensional IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 782-802, October.

    Cited by:

    1. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    2. Michela Gnaldi & Simone Del Sarto, 2018. "Time Use Habits of Italian Generation Y: Dimensions of Leisure Preferences," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 1187-1203, August.
    3. Michael Brusco & Hans-Friedrich Köhn & Douglas Steinley, 2015. "An Exact Method for Partitioning Dichotomous Items Within the Framework of the Monotone Homogeneity Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 949-967, December.
    4. Michela Gnaldi & Simone Del Sarto, 2018. "Variable Weighting via Multidimensional IRT Models in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 1139-1156, April.
    5. Simone Del Sarto & Michela Gnaldi, 2022. "Spare time use: profiles of Italian Millennials (beyond the media hype)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1403-1428, December.
    6. Silvia Bacci & Michela Gnaldi, 2015. "A classification of university courses based on students’ satisfaction: an application of a two-level mixture item response model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 927-940, May.

  35. Francesco Bartolucci, 2012. "On a possible decomposition of the h‐index," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(10), pages 2126-2127, October.

    Cited by:

    1. Francesco Bartolucci, 2015. "A comparison between the g-index and the h-index based on concentration," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2708-2710, December.

  36. Manuela Chiavarini & Francesco Bartolucci & Alessio Gili & Luca Pieroni & Liliana Minelli, 2012. "Effects of individual and social factors on preterm birth and low birth weight: empirical evidence from regional data in Italy," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(2), pages 261-268, April.

    Cited by:

    1. George Wehby & Juan Gili & Mariela Pawluk & Eduardo Castilla & Jorge López-Camelo, 2015. "Disparities in birth weight and gestational age by ethnic ancestry in South American countries," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 60(3), pages 343-351, March.
    2. Silvia Bacci & Francesco Bartolucci & Manuela Chiavarini & Liliana Minelli & Luca Pieroni, 2014. "Differences in Birthweight Outcomes: A Longitudinal Study Based on Siblings," IJERPH, MDPI, vol. 11(6), pages 1-13, June.
    3. Salmasi, Luca & Pieroni, Luca, 2015. "Immigration policy and birth weight: Positive externalities in Italian law," Journal of Health Economics, Elsevier, vol. 43(C), pages 128-139.

  37. Bartolucci, Francesco & Nigro, Valentina, 2012. "Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data," Journal of Econometrics, Elsevier, vol. 170(1), pages 102-116.

    Cited by:

    1. Miriam Koning & Gerard Mertens & Peter Roosenboom, 2018. "Drivers of institutional change around the world: The case of IFRS," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 49(3), pages 249-271, April.
    2. Pauline Givord & Lionel Wilner, 2015. "When Does the Stepping‐Stone Work? Fixed‐Term Contracts Versus Temporary Agency Work in Changing Economic Conditions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 787-805, August.
    3. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data methods for dynamic heterogeneous agent models," CeMMAP working papers CWP51/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. F. Bartolucci & R. Bellio & A. Salvan & N. Sartori, 2016. "Modified Profile Likelihood for Fixed-Effects Panel Data Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1271-1289, August.
    5. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    6. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.
    7. Miranda, Alfonso & Trivedi, Pravin K., 2020. "Econometric Models of Fertility," GLO Discussion Paper Series 574, Global Labor Organization (GLO).
    8. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    9. Bartolucci, Francesco & Pigini, Claudia, 2019. "Partial effects estimation for fixed-effects logit panel data models," MPRA Paper 92243, University Library of Munich, Germany.
    10. Carlos Fernández-Loría & Maxime C. Cohen & Anindya Ghose, 2023. "Evolution of Referrals over Customers’ Life Cycle: Evidence from a Ride-Sharing Platform," Information Systems Research, INFORMS, vol. 34(2), pages 698-720, June.
    11. Yoshitsugu Kitazawa, 2017. "DFEL-RTN, a set of TSP programs for root-N consistent estimations of dynamic fixed effects logit models," Discussion Papers 81, Kyushu Sangyo University, Faculty of Economics.
    12. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    13. Lionel WILNER, 2019. "The Dynamics of Individual Happiness," Working Papers 2019-18, Center for Research in Economics and Statistics.
    14. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.
    15. Al-Sadoon, Majid M. & Li, Tong & Pesaran, M. Hashem, 2012. "An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects," IZA Discussion Papers 7054, Institute of Labor Economics (IZA).
    16. Pigini, Claudia & Bartolucci, Francesco, 2022. "Conditional inference for binary panel data models with predetermined covariates," Econometrics and Statistics, Elsevier, vol. 23(C), pages 83-104.
    17. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.
    18. Lee Myoung-jae, 2015. "Panel conditional and multinomial logit with time-varying parameters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 317-337, June.
    19. Bartolucci, Francesco & Pigini, Claudia, 2017. "Granger causality in dynamic binary short panel data models," MPRA Paper 77486, University Library of Munich, Germany.
    20. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "The existence and persistence of household financial hardship: A Bayesian multivariate dynamic logit framework," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 285-298.
    21. Yoshitsugu Kitazawa, 2013. "Exploration of dynamic fixed effects logit models from a traditional angle," Discussion Papers 60, Kyushu Sangyo University, Faculty of Economics.
    22. Hugo Kruiniger, 2021. "Root-n-consistent Conditional ML estimation of dynamic panel logit models with fixed effects," Papers 2103.04973, arXiv.org, revised Apr 2021.

  38. Bartolucci, Francesco & Scaccia, Luisa & Farcomeni, Alessio, 2012. "Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4067-4080.

    Cited by:

    1. Gordon Anderson, Alessio Farcomeni, Maria Grazia Pittau and Roberto Zelli, 2019. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development Since 1990," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(1), pages 1-22, March.
    2. Boitani, Andrea & Punzo, Chiara, 2019. "Banks’ leverage behaviour in a two-agent new Keynesian model," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 347-359.
    3. Alan Agresti, 2014. "Two Bayesian/frequentist challenges for categorical data analyses," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 125-132, August.
    4. Guido Consonni & Roberta Paroli, 2017. "Objective Bayesian Comparison of Constrained Analysis of Variance Models," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 589-609, September.
    5. Linda J. Young & M. Kateri & A. Agresti, 2013. "Bayesian inference about odds ratio structure in ordinal contingency tables," Environmetrics, John Wiley & Sons, Ltd., vol. 24(5), pages 281-288, August.
    6. Ntzoufras, Ioannis & Tarantola, Claudia, 2013. "Conjugate and conditional conjugate Bayesian analysis of discrete graphical models of marginal independence," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 161-177.
    7. Lorenza Rossi & Emilio Zanetti Chini, 2017. "Firms' Dynamics and Business Cycle: New Disaggregated Data," DEM Working Papers Series 141, University of Pavia, Department of Economics and Management.
    8. Francesco Bartolucci & Alessio Farcomeni & Luisa Scaccia, 2017. "A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 952-978, December.
    9. Ioannis Ntzoufras & Claudia Tarantola & Monia Lupparelli, 2018. "Probability Based Independence Sampler for Bayesian Quantitative Learning in Graphical Log-Linear Marginal Models," DEM Working Papers Series 149, University of Pavia, Department of Economics and Management.

  39. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2011. "Assessment of School Performance Through a Multilevel Latent Markov Rasch Model," Journal of Educational and Behavioral Statistics, , vol. 36(4), pages 491-522, August.

    Cited by:

    1. Sun-Joo Cho & Amanda P. Goodwin, 2017. "Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 846-870, September.
    2. Montanari, Giorgio E. & Doretti, Marco & Bartolucci, Francesco, 2017. "A multilevel latent Markov model for the evaluation of nursing homes' performance," MPRA Paper 80691, University Library of Munich, Germany.
    3. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    4. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    5. Bartolucci, Francesco & Lupparelli, Monia, 2012. "Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data," MPRA Paper 40588, University Library of Munich, Germany.
    6. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    7. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.
    8. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    9. Yang Liu & Xiaojing Wang, 2020. "Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 274-296, June.
    10. Ruijin Lu & Tonja R. Nansel & Zhen Chen, 2023. "A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 288-308, April.
    11. Giorgio E. Montanari & Marco Doretti, 2019. "Ranking Nursing Homes’ Performances Through a Latent Markov Model with Fixed and Random Effects," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 307-326, November.
    12. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
    13. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    14. Francesca Bassi & Bruno Scarpa, 2015. "Latent class modeling of markers of day-specific fertility," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 263-276, August.
    15. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    16. Giorgio Eduardo Montanari & Marco Doretti & Maria Francesca Marino, 2022. "Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 457-485, June.
    17. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2023. "A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 387-419, August.

  40. Bartolucci, Francesco & Grilli, Leonardo, 2011. "Modeling Partial Compliance Through Copulas in a Principal Stratification Framework," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 469-479.

    Cited by:

    1. Fan Yang & Peng Ding, 2018. "Using survival information in truncation by death problems without the monotonicity assumption," Biometrics, The International Biometric Society, vol. 74(4), pages 1232-1239, December.
    2. von Hinke, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2016. "Genetic markers as instrumental variables," Journal of Health Economics, Elsevier, vol. 45(C), pages 131-148.
    3. Torben Martinussen & Stijn Vansteelandt & Per Kragh Andersen, 2020. "Subtleties in the interpretation of hazard contrasts," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 833-855, October.
    4. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy," Papers 2005.09130, arXiv.org, revised Jun 2020.
    5. Corwin M. Zigler & Thomas R. Belin, 2012. "A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint," Biometrics, The International Biometric Society, vol. 68(3), pages 922-932, September.
    6. Laura Forastiere & Fabrizia Mealli & Tyler J. VanderWeele, 2016. "Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 510-525, April.
    7. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: evidence from an instrumental variable analysis of China's one‐child policy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1615-1635, October.

  41. Francesco Bartolucci & Valentina Nigro, 2010. "A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a √n-Consistent Conditional Estimator," Econometrica, Econometric Society, vol. 78(2), pages 719-733, March.

    Cited by:

    1. Anders Skrondal & Sophia Rabe-Hesketh, 2014. "Handling initial conditions and endogenous covariates in dynamic/transition models for binary data with unobserved heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 211-237, February.
    2. Soyoon Weon & David W. Rothwell, 2020. "Dynamics of Asset Poverty in South Korea," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(2), pages 639-657, July.
    3. Franco Peracchi & Claudio Rossetti, 2019. "A Nonlinear Dynamic Factor Model of Health and Medical Treatment," CSEF Working Papers 524, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    4. French, Declan, 2023. "Exploring household financial strain dynamics," International Review of Financial Analysis, Elsevier, vol. 86(C).
    5. Cavit Pakel & Martin Weidner, 2023. "Bounds on Average Effects in Discrete Choice Panel Data Models," Papers 2309.09299, arXiv.org, revised May 2024.
    6. Bartolucci, Francesco & Nigro, Valentina, 2012. "Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data," Journal of Econometrics, Elsevier, vol. 170(1), pages 102-116.
    7. Pauline Givord & Lionel Wilner, 2015. "When Does the Stepping‐Stone Work? Fixed‐Term Contracts Versus Temporary Agency Work in Changing Economic Conditions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 787-805, August.
    8. cyrine hannafi & Christophe Muller, 2016. "The Poverty-Economic Growth-Health Triangle," EcoMod2016 9587, EcoMod.
    9. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data methods for dynamic heterogeneous agent models," CeMMAP working papers CWP51/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Roy-Chowdhury, V., 2022. "Self-Confidence and Motivated Memory Loss: Evidence from Schools," Cambridge Working Papers in Economics 2213, Faculty of Economics, University of Cambridge.
    11. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2022. "Testing for state dependence in the fixed-effects ordered logit model," MPRA Paper 113890, University Library of Munich, Germany.
    12. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Group Publishing Limited.
    13. Gao, Wei & Bergsma, Wicher & Yao, Qiwei, 2017. "Estimation for dynamic and static panel probit models with large individual effects," LSE Research Online Documents on Economics 65165, London School of Economics and Political Science, LSE Library.
    14. Sven Schreiber & Miriam Beblo, 2016. "Leisure and Housing Consumption after Retirement: New Evidence on the Life-Cycle Hypothesis," SOEPpapers on Multidisciplinary Panel Data Research 849, DIW Berlin, The German Socio-Economic Panel (SOEP).
    15. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    16. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.
    17. Giulia Bettin & Riccardo Lucchetti, 2016. "Steady streams and sudden bursts: persistence patterns in remittance decisions," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(1), pages 263-292, January.
    18. Miranda, Alfonso & Trivedi, Pravin K., 2020. "Econometric Models of Fertility," GLO Discussion Paper Series 574, Global Labor Organization (GLO).
    19. Christopher R. Dobronyi & Fu Ouyang & Thomas Tao Yang, 2023. "Revisiting Panel Data Discrete Choice Models with Lagged Dependent Variables," Papers 2301.09379, arXiv.org, revised Aug 2024.
    20. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    21. Bartolucci, Francesco & Pigini, Claudia, 2019. "Partial effects estimation for fixed-effects logit panel data models," MPRA Paper 92243, University Library of Munich, Germany.
    22. Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2021. "Predicting Individual Effects in Fixed Effects Panel Probit Models," SoDa Laboratories Working Paper Series 2021-05, Monash University, SoDa Laboratories.
    23. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    24. Lionel WILNER, 2019. "The Dynamics of Individual Happiness," Working Papers 2019-18, Center for Research in Economics and Statistics.
    25. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.
    26. Al-Sadoon, Majid M. & Li, Tong & Pesaran, M. Hashem, 2012. "An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects," IZA Discussion Papers 7054, Institute of Labor Economics (IZA).
    27. Konstantin A. Kholodilin & Claus Michelsen, 2019. "Zehn Jahre nach dem großen Knall: wie ist es um die Stabilität der internationalen Immobilienmärkte bestellt? [Ten years after a Big Bang: How stable are the international housing markets?]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 5(1), pages 67-87, November.
    28. Fu Ouyang & Thomas Tao Yang, 2022. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," Papers 2202.12062, arXiv.org, revised Feb 2024.
    29. Pigini, Claudia & Bartolucci, Francesco, 2022. "Conditional inference for binary panel data models with predetermined covariates," Econometrics and Statistics, Elsevier, vol. 23(C), pages 83-104.
    30. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Papers 1909.05560, arXiv.org.
    31. Minjeong Jeon & Sophia Rabe-Hesketh, 2016. "An autoregressive growth model for longitudinal item analysis," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 830-850, September.
    32. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.
    33. Sun-Joo Cho & Sarah Brown-Schmidt & Paul De Boeck & Jianhong Shen, 2020. "Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 154-184, March.
    34. Pigini, Claudia & Presbitero, Andrea F. & Zazzaro, Alberto, 2016. "State dependence in access to credit," Journal of Financial Stability, Elsevier, vol. 27(C), pages 17-34.
    35. Antoine Poulin-Moore & Kerem Tuzcuoglu, 2024. "Forecasting Recessions in Canada: An Autoregressive Probit Model Approach," Staff Working Papers 24-10, Bank of Canada.
    36. Dumitrescu, Ariadna & Zakriya, Mohammed, 2021. "Stakeholders and the stock price crash risk: What matters in corporate social performance?," Journal of Corporate Finance, Elsevier, vol. 67(C).
    37. Li, Wenhua & Adachi, Tsuyoshi, 2017. "Quantitative estimation of resource nationalism by binary choice logit model for panel data," Resources Policy, Elsevier, vol. 53(C), pages 247-258.
    38. Lee Myoung-jae, 2015. "Panel conditional and multinomial logit with time-varying parameters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 317-337, June.
    39. Bo E. Honor'e & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Papers 2005.05942, arXiv.org, revised Dec 2023.
    40. Riccardo Lucchetti & Claudia Pigini, 2018. "Dynamic panel probit: finite-sample performance of alternative random-effects estimators," Working Papers 426, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    41. Fu Ouyang & Thomas Tao Yang, 2020. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," ANU Working Papers in Economics and Econometrics 2020-671, Australian National University, College of Business and Economics, School of Economics.
    42. Chandra Bhat, 2015. "A new spatial (social) interaction discrete choice model accommodating for unobserved effects due to endogenous network formation," Transportation, Springer, vol. 42(5), pages 879-914, September.
    43. Bo E. Honoré & Martin Weidner, 2021. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Working Papers 2021-79, Princeton University. Economics Department..
    44. Fu Ouyang & Thomas Tao Yang, 2020. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," Discussion Papers Series 626, School of Economics, University of Queensland, Australia.
    45. Marco Cosconati & Alessandro Sembenelli, 2016. "Firm Subsidies and the Innovation Output: What Can We Learn by Looking at Multiple Investment Inputs?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 2(1), pages 31-55, March.
    46. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.
    47. Bartolucci, Francesco & Pigini, Claudia, 2017. "Granger causality in dynamic binary short panel data models," MPRA Paper 77486, University Library of Munich, Germany.
    48. Bo E. Honoré & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," CeMMAP working papers CWP38/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    49. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "The existence and persistence of household financial hardship: A Bayesian multivariate dynamic logit framework," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 285-298.
    50. Yoshitsugu Kitazawa, 2013. "Exploration of dynamic fixed effects logit models from a traditional angle," Discussion Papers 60, Kyushu Sangyo University, Faculty of Economics.
    51. Hugo Kruiniger, 2021. "Root-n-consistent Conditional ML estimation of dynamic panel logit models with fixed effects," Papers 2103.04973, arXiv.org, revised Apr 2021.
    52. Sebastian Kocar & Nicholas Biddle, 2023. "The power of online panel paradata to predict unit nonresponse and voluntary attrition in a longitudinal design," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1055-1078, April.
    53. Riccardo (Jack) Lucchetti & Claudia Pigini, 2020. "Choice of solutions to the initial-conditions problem in dynamic panel probit models," Working Papers 2020:27, Department of Economics, University of Venice "Ca' Foscari".

  42. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.

    Cited by:

    1. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.

  43. Francesco Bartolucci & Alessio Farcomeni, 2010. "A note on the mixture transition distribution and hidden Markov models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 132-138, March.

    Cited by:

    1. Alessio Farcomeni & Luca Greco, 2015. "S-estimation of hidden Markov models," Computational Statistics, Springer, vol. 30(1), pages 57-80, March.
    2. Bolano, Danilo & Berchtold, André, 2016. "General framework and model building in the class of Hidden Mixture Transition Distribution models," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 131-145.
    3. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    4. Bernardi, Mauro & Maruotti, Antonello & Petrella, Lea, 2017. "Multiple risk measures for multivariate dynamic heavy–tailed models," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 1-32.
    5. Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    6. Farcomeni, Alessio, 2011. "Hidden Markov partition models," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1766-1770.

  44. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.

    Cited by:

    1. Xinyuan Song & Yemao Xia & Hongtu Zhu, 2017. "Hidden Markov latent variable models with multivariate longitudinal data," Biometrics, The International Biometric Society, vol. 73(1), pages 313-323, March.
    2. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    3. Franco Peracchi & Claudio Rossetti, 2019. "A Nonlinear Dynamic Factor Model of Health and Medical Treatment," CSEF Working Papers 524, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    4. Francesco Bartolucci & Alessio Farcomeni, 2015. "A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates," Biometrics, The International Biometric Society, vol. 71(1), pages 80-89, March.
    5. Mauro Laudicella & Paolo Li Donni, 2022. "The dynamic interdependence in the demand of primary and emergency secondary care: A hidden Markov approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 521-536, April.
    6. Bartolucci, Francesco & Nigro, Valentina, 2012. "Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data," Journal of Econometrics, Elsevier, vol. 170(1), pages 102-116.
    7. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    8. Xia, Ye-Mao & Tang, Nian-Sheng & Gou, Jian-Wei, 2016. "Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 259-275.
    9. Bartolucci, Francesco & Montanari, Giorgio E. & Pandolfi, Silvia, 2015. "Three-step estimation of latent Markov models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 287-301.
    10. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Group Publishing Limited.
    11. Kévin Beaubrun-Diant & Tristan-Pierre Maury, 2011. "Assessing the Interaction between Real Estate and Equity in Households Portfolio Choice," Working Papers halshs-00635582, HAL.
    12. Gao, Wei & Bergsma, Wicher & Yao, Qiwei, 2017. "Estimation for dynamic and static panel probit models with large individual effects," LSE Research Online Documents on Economics 65165, London School of Economics and Political Science, LSE Library.
    13. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    14. Mark, Tanya & Lemon, Katherine N. & Vandenbosch, Mark & Bulla, Jan & Maruotti, Antonello, 2013. "Capturing the Evolution of Customer–Firm Relationships: How Customers Become More (or Less) Valuable Over Time," Journal of Retailing, Elsevier, vol. 89(3), pages 231-245.
    15. De Angelis Luca & Viroli Cinzia, 2017. "A Markov-switching regression model with non-Gaussian innovations: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-22, April.
    16. Silvia Bianconcini, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 466-468, September.
    17. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    18. Joan Gil & Paolo Li Donni & Eugenio Zucchelli, 2018. "Uncontrolled diabetes and health care utilisation: a bivariate Latent Markov model approach," UB School of Economics Working Papers 2018/382, University of Barcelona School of Economics.
    19. Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2019. "Dynamic discrete mixtures for high frequency prices," Discussion Papers 19/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    20. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    21. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.
    22. Bernardi, Mauro & Maruotti, Antonello & Petrella, Lea, 2017. "Multiple risk measures for multivariate dynamic heavy–tailed models," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 1-32.
    23. Michele Bavaro & Federico Tullio, 2023. "Intergenerational mobility measurement with latent transition matrices," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 25-45, March.
    24. Stephane Gregoir; & Tristan-Pierre Maury;, 2012. "On the impact of social housing on the labour position of disabled," Health, Econometrics and Data Group (HEDG) Working Papers 12/22, HEDG, c/o Department of Economics, University of York.
    25. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Papers 1909.05560, arXiv.org.
    26. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.
    27. Liu, Hefei & Song, Xinyuan & Zhang, Baoxue, 2022. "Varying-coefficient hidden Markov models with zero-effect regions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    28. Bartolucci, Francesco & Lupparelli, Monia, 2012. "Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data," MPRA Paper 40588, University Library of Munich, Germany.
    29. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    30. Biørn, Erik & Bjørnsen, Hild-Marte, 2013. "What Motivates Farm Couples to Seek Off-farm Labour? A Logit Analysis of Job Transitions," Memorandum 09/2013, Oslo University, Department of Economics.
    31. Maruotti, Antonello & Punzo, Antonio, 2017. "Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 475-496.
    32. Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    33. Antonello Maruotti, 2015. "Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 84-109, March.
    34. Francesco Bartolucci & Federico Belotti & Franco Peracchi, 2013. "Testing for Time-Invariant Unobserved Heterogeneity in Generalized Linear Models for Panel Data," EIEF Working Papers Series 1312, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.
    35. Catania, Leopoldo & Di Mari, Roberto, 2021. "Hierarchical Markov-switching models for multivariate integer-valued time-series," Journal of Econometrics, Elsevier, vol. 221(1), pages 118-137.
    36. Francesco Bartolucci & Alessio Farcomeni, 2010. "A note on the mixture transition distribution and hidden Markov models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 132-138, March.
    37. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.
    38. Li Donni, Paolo, 2019. "The unobserved pattern of material hardship and health among older Americans," Journal of Health Economics, Elsevier, vol. 65(C), pages 31-42.
    39. Antonio Punzo & Salvatore Ingrassia & Antonello Maruotti, 2021. "Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions," Statistical Papers, Springer, vol. 62(3), pages 1519-1555, June.
    40. Etienne C^ome & Marie Cottrell & Patrice Gaubert, 2015. "Analysis of Professional Trajectories using Disconnected Self-Organizing Maps," Papers 1507.00578, arXiv.org.
    41. Edward Ip & Qiang Zhang & Jack Rejeski & Tammy Harris & Stephen Kritchevsky, 2013. "Partially Ordered Mixed Hidden Markov Model for the Disablement Process of Older Adults," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 370-384, June.
    42. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2017. "Rectangular latent Markov models for time-specific clustering," Working Papers tecipa-589, University of Toronto, Department of Economics.
    43. Geir D. Berentsen & Jan Bulla & Antonello Maruotti & Bård Støve, 2022. "Modelling clusters of corporate defaults: Regime‐switching models significantly reduce the contagion source," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 698-722, June.
    44. E. Beaubrun-Diant, Kevin. & Maury, Tristan-Pierre, 2016. "Home tenure, stock market participation, and composition of the household portfolio," Journal of Housing Economics, Elsevier, vol. 32(C), pages 1-17.
    45. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    46. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.
    47. Luca Merlo & Lea Petrella & Nikos Tzavidis, 2022. "Quantile mixed hidden Markov models for multivariate longitudinal data: An application to children's Strengths and Difficulties Questionnaire scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 417-448, March.
    48. Francesco Lagona & Antonello Maruotti & Fabio Padovano, 2015. "Multilevel multivariate modelling of legislative count data, with a hidden Markov chain," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 705-723, June.
    49. Stéphane Gregoir & Tristan‐Pierre Maury, 2013. "The Impact Of Social Housing On The Labour Market Status Of The Disabled," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1124-1138, September.
    50. Vana-Gür, Laura, 2024. "Multivariate ordinal regression for multiple repeated measurements," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
    51. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.

  45. Francesco Bartolucci & Monia Lupparelli, 2008. "Focused Information Criterion for Capture–Recapture Models for Closed Populations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 629-649, December.

    Cited by:

    1. Gerda Claeskens, 2012. "Focused estimation and model averaging with penalization methods: an overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 272-287, August.
    2. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.

  46. Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 141-157, June.

    Cited by:

    1. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Michela Gnaldi & Simone Del Sarto, 2018. "Time Use Habits of Italian Generation Y: Dimensions of Leisure Preferences," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 1187-1203, August.
    3. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    4. Pieroni, Luca & d'Agostino, Giorgio & Bartolucci, Francesco, 2013. "Identifying corruption through latent class models: evidence from transition economies," MPRA Paper 43981, University Library of Munich, Germany.
    5. Michael Brusco & Hans-Friedrich Köhn & Douglas Steinley, 2015. "An Exact Method for Partitioning Dichotomous Items Within the Framework of the Monotone Homogeneity Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 949-967, December.
    6. Alexander Robitzsch, 2024. "Estimation of Standard Error, Linking Error, and Total Error for Robust and Nonrobust Linking Methods in the Two-Parameter Logistic Model," Stats, MDPI, vol. 7(3), pages 1-21, June.
    7. Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2015. "Ranking scientific journals via latent class models for polytomous item response data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 1025-1049, October.
    8. Genge, Ewa & Bartolucci, Francesco, 2019. "Are attitudes towards immigration changing in Europe? An analysis based on bidimensional latent class IRT models," MPRA Paper 94672, University Library of Munich, Germany.
    9. Vladimir Turetsky & Emil Bashkansky, 2022. "Ordinal response variation of the polytomous Rasch model," METRON, Springer;Sapienza Università di Roma, vol. 80(3), pages 305-330, December.
    10. Bartolucci, Francesco & Bacci, Silvia & Gnaldi, Michela, 2014. "MultiLCIRT: An R package for multidimensional latent class item response models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 971-985.
    11. Ping Chen & Chun Wang, 2021. "Using EM Algorithm for Finite Mixtures and Reformed Supplemented EM for MIRT Calibration," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 299-326, March.
    12. Michela Gnaldi & Simone Del Sarto, 2018. "Variable Weighting via Multidimensional IRT Models in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 1139-1156, April.
    13. Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2013. "Ranking Scientific Journals via Latent Class Models for Polytomous Item Response," EIEF Working Papers Series 1313, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.
    14. Simone Del Sarto & Michela Gnaldi, 2022. "Spare time use: profiles of Italian Millennials (beyond the media hype)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1403-1428, December.
    15. F. Bartolucci & G. Montanari & S. Pandolfi, 2012. "Dimensionality of the Latent Structure and Item Selection Via Latent Class Multidimensional IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 782-802, October.
    16. Silvia Bacci & Bruno Bertaccini & Alessandra Petrucci, 2020. "Beliefs and needs of academic teachers: a latent class analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 597-617, September.
    17. Silvia Bacci & Michela Gnaldi, 2015. "A classification of university courses based on students’ satisfaction: an application of a two-level mixture item response model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 927-940, May.
    18. Ewa Genge, 2021. "LC and LC-IRT Models in the Identification of Polish Households with Similar Perception of Financial Position," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    19. Francesco Bartolucci & Alessio Farcomeni & Luisa Scaccia, 2017. "A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 952-978, December.
    20. Jules L. Ellis & Klaas Sijtsma, 2023. "A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 387-412, June.
    21. Michela Gnaldi & Silvia Bacci & Thiemo Kunze & Samuel Greiff, 2020. "Students’ Complex Problem Solving Profiles," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 469-501, June.
    22. Svend Kreiner & Karl Christensen, 2011. "Item Screening in Graphical Loglinear Rasch Models," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 228-256, April.
    23. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    24. Giorgio d’Agostino & Luca Pieroni, 2019. "Modelling Corruption Perceptions: Evidence from Eastern Europe and Central Asian Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 311-341, February.
    25. Ewa Genge & Francesco Bartolucci, 2022. "Are attitudes toward immigration changing in Europe? An analysis based on latent class IRT models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 235-271, June.
    26. Michela Gnaldi, 2017. "A multidimensional IRT approach for dimensionality assessment of standardised students’ tests in mathematics," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1167-1182, May.

  47. Francesco Bartolucci & Fulvia Pennoni, 2007. "A Class of Latent Markov Models for Capture–Recapture Data Allowing for Time, Heterogeneity, and Behavior Effects," Biometrics, The International Biometric Society, vol. 63(2), pages 568-578, June.

    Cited by:

    1. Danilo Alunni Fegatelli & Luca Tardella, 2016. "Flexible behavioral capture–recapture modeling," Biometrics, The International Biometric Society, vol. 72(1), pages 125-135, March.
    2. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    3. Antonio Acconcia & Maria Carannante & Michelangelo Misuraca & Germana Scepi, 2020. "Measuring Vulnerability to Poverty with Latent Transition Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(1), pages 1-31, August.
    4. Danilo Fegatelli & Luca Tardella, 2013. "Improved inference on capture recapture models with behavioural effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 45-66, March.
    5. Francesco Bartolucci & Monia Lupparelli, 2008. "Focused Information Criterion for Capture–Recapture Models for Closed Populations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 629-649, December.

  48. Francesco Bartolucci & Fulvia Pennoni & Brian Francis, 2007. "A latent Markov model for detecting patterns of criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 115-132, January.

    Cited by:

    1. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    2. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
    3. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    4. Eugene C.X. Ikejemba & Peter C. Schuur, 2018. "Analyzing the Impact of Theft and Vandalism in Relation to the Sustainability of Renewable Energy Development Projects in Sub-Saharan Africa," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    5. Brian Francis & Jiayi Liu, 2015. "Modelling escalation in crime seriousness: a latent variable approach," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 277-297, August.
    6. S. Bacci & S. Pandolfi & F. Pennoni, 2014. "A comparison of some criteria for states selection in the latent Markov model for longitudinal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 125-145, June.
    7. Paas, L.J. & Vermunt, J.K. & Bijmolt, T.H.A., 2007. "Discrete-time discrete-state latent Markov modelling for assessing and predicting household acquisitions of financial products," Other publications TiSEM 5781ab33-6687-4ad5-b57a-3, Tilburg University, School of Economics and Management.
    8. Siem Jan Koopman & André Lucas & Marius Ooms & Kees van Montfort & Victor van der Geest, 2007. "Estimating Systematic Continuous-time Trends in Recidivism using a Non-Gaussian Panel Data Model," Tinbergen Institute Discussion Papers 07-027/4, Tinbergen Institute.
    9. Yu Luo & David A. Stephens & Aman Verma & David L. Buckeridge, 2021. "Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records," Biometrics, The International Biometric Society, vol. 77(1), pages 78-90, March.
    10. Catania, Leopoldo & Di Mari, Roberto, 2021. "Hierarchical Markov-switching models for multivariate integer-valued time-series," Journal of Econometrics, Elsevier, vol. 221(1), pages 118-137.
    11. De Angelis, L & Paas, L.J., 2009. "The dynamic analysis and prediction of stock markets through the latent Markov model," Serie Research Memoranda 0053, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    12. Kelava, Augustin & Kohler, Michael & Krzyżak, Adam & Schaffland, Tim Fabian, 2017. "Nonparametric estimation of a latent variable model," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 112-134.
    13. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    14. Leonard J. Paas & Jeroen K. Vermunt & Tammo H. A. Bijmolt, 2007. "Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 955-974, October.
    15. Luca Brusa & Francesco Bartolucci & Fulvia Pennoni, 2023. "Tempered expectation-maximization algorithm for the estimation of discrete latent variable models," Computational Statistics, Springer, vol. 38(3), pages 1391-1424, September.

  49. Bartolucci, Francesco & Nigro, Valentina, 2007. "Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3470-3483, April.
    See citations under working paper version above.
  50. Francesco Bartolucci & Fulvia Pennoni, 2007. "On the approximation of the quadratic exponential distribution in a latent variable context," Biometrika, Biometrika Trust, vol. 94(3), pages 745-754.

    Cited by:

    1. Bartolucci, Francesco & Nigro, Valentina, 2012. "Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data," Journal of Econometrics, Elsevier, vol. 170(1), pages 102-116.
    2. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.

  51. Bartolucci, Francesco, 2007. "A penalized version of the empirical likelihood ratio for the population mean," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 104-110, January.

    Cited by:

    1. Mahdieh Bayati & Seyed Kamran Ghoreishi & Jingjing Wu, 2021. "Bayesian analysis of restricted penalized empirical likelihood," Computational Statistics, Springer, vol. 36(2), pages 1321-1339, June.
    2. Kévin Beaubrun-Diant & Tristan-Pierre Maury, 2011. "Assessing the Interaction between Real Estate and Equity in Households Portfolio Choice," Working Papers halshs-00635582, HAL.
    3. Min Tsao & Fan Wu, 2014. "Extended empirical likelihood for estimating equations," Biometrika, Biometrika Trust, vol. 101(3), pages 703-710.
    4. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2017. "Empirical likelihood ratio in penalty form and the convex hull problem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 507-529, November.
    5. Thorne Thomas, 2015. "Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(6), pages 575-583, December.
    6. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2016. "Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 315-336, May.
    7. E. Beaubrun-Diant, Kevin. & Maury, Tristan-Pierre, 2016. "Home tenure, stock market participation, and composition of the household portfolio," Journal of Housing Economics, Elsevier, vol. 32(C), pages 1-17.
    8. Xianyang Zhang & Xiaofeng Shao, 2016. "On the coverage bound problem of empirical likelihood methods for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 395-421, March.

  52. Francesco Bartolucci & Luisa Scaccia & Antonietta Mira, 2006. "Efficient Bayes factor estimation from the reversible jump output," Biometrika, Biometrika Trust, vol. 93(1), pages 41-52, March.

    Cited by:

    1. Rufo, M.J. & Martín, J. & Pérez, C.J., 2010. "New approaches to compute Bayes factor in finite mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3324-3335, December.
    2. David I. Hastie & Peter J. Green, 2012. "Model choice using reversible jump Markov chain Monte Carlo," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 309-338, August.
    3. Pandolfi, Silvia & Bartolucci, Francesco & Friel, Nial, 2014. "A generalized multiple-try version of the Reversible Jump algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 298-314.
    4. Shotwell Matthew S & Slate Elizabeth H, 2010. "Bayesian Modeling of Footrace Finishing Times," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-21, July.
    5. N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607, July.
    6. Nicolas Chopin & Christian P. Robert, 2010. "Properties of nested sampling," Biometrika, Biometrika Trust, vol. 97(3), pages 741-755.

  53. Francesco Bartolucci, 2006. "Likelihood inference for a class of latent Markov models under linear hypotheses on the transition probabilities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 155-178, April.

    Cited by:

    1. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
    2. Francesco Bartolucci & Valentina Nigro, 2007. "Maximum likelihood estimation of an extended latent markov model for clustered binary panel data," CEIS Research Paper 96, Tor Vergata University, CEIS.
    3. Mauro Laudicella & Paolo Li Donni, 2022. "The dynamic interdependence in the demand of primary and emergency secondary care: A hidden Markov approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 521-536, April.
    4. Francesco Bartolucci & Fulvia Pennoni & Brian Francis, 2007. "A latent Markov model for detecting patterns of criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 115-132, January.
    5. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    6. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    7. Joan Gil & Paolo Li Donni & Eugenio Zucchelli, 2018. "Uncontrolled diabetes and health care utilisation: a bivariate Latent Markov model approach," UB School of Economics Working Papers 2018/382, University of Barcelona School of Economics.
    8. S. Bacci & S. Pandolfi & F. Pennoni, 2014. "A comparison of some criteria for states selection in the latent Markov model for longitudinal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 125-145, June.
    9. Jörn Dannemann & Hajo Holzmann, 2008. "Likelihood Ratio Testing for Hidden Markov Models Under Non‐standard Conditions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 309-321, June.
    10. Michele Bavaro & Federico Tullio, 2023. "Intergenerational mobility measurement with latent transition matrices," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 25-45, March.
    11. Bartolucci, Francesco & Lupparelli, Monia, 2012. "Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data," MPRA Paper 40588, University Library of Munich, Germany.
    12. Alessio Farcomeni, 2015. "Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1127-1135, December.
    13. Francesco Bartolucci & Fulvia Pennoni, 2007. "A Class of Latent Markov Models for Capture–Recapture Data Allowing for Time, Heterogeneity, and Behavior Effects," Biometrics, The International Biometric Society, vol. 63(2), pages 568-578, June.
    14. Mansnerus, Erika, 2008. "What happens to facts after their construction?: characteristics and functional roles of facts in the dissemination of knowledge across modelling communities," Economic History Working Papers 22504, London School of Economics and Political Science, Department of Economic History.
    15. Catania, Leopoldo & Di Mari, Roberto, 2021. "Hierarchical Markov-switching models for multivariate integer-valued time-series," Journal of Econometrics, Elsevier, vol. 221(1), pages 118-137.
    16. Francesco Bartolucci & Alessio Farcomeni, 2010. "A note on the mixture transition distribution and hidden Markov models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 132-138, March.
    17. Li Donni, Paolo, 2019. "The unobserved pattern of material hardship and health among older Americans," Journal of Health Economics, Elsevier, vol. 65(C), pages 31-42.
    18. Gordon Anderson & Alessio Farcomeni & Grazia Pittau & Roberto Zelli, 2017. "Rectangular latent Markov models for time-specific clustering," Working Papers tecipa-589, University of Toronto, Department of Economics.
    19. Geir D. Berentsen & Jan Bulla & Antonello Maruotti & Bård Støve, 2022. "Modelling clusters of corporate defaults: Regime‐switching models significantly reduce the contagion source," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 698-722, June.
    20. Bartolucci, Francesco & Pennoni, Fulvia & Vittadini, Giorgio, 2015. "Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies," MPRA Paper 66492, University Library of Munich, Germany.
    21. Kelava, Augustin & Kohler, Michael & Krzyżak, Adam & Schaffland, Tim Fabian, 2017. "Nonparametric estimation of a latent variable model," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 112-134.
    22. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    23. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.

  54. Bartolucci, Francesco & Forcina, Antonio, 2006. "A Class of Latent Marginal Models for CaptureRecapture Data With Continuous Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 786-794, June.

    Cited by:

    1. Baffour Bernard & Brown James J. & Smith Peter W.F., 2021. "Latent Class Analysis for Estimating an Unknown Population Size – with Application to Censuses," Journal of Official Statistics, Sciendo, vol. 37(3), pages 673-697, September.
    2. Danilo Alunni Fegatelli & Luca Tardella, 2016. "Flexible behavioral capture–recapture modeling," Biometrics, The International Biometric Society, vol. 72(1), pages 125-135, March.
    3. Forcina, Antonio, 2017. "A Fisher-scoring algorithm for fitting latent class models with individual covariates," Econometrics and Statistics, Elsevier, vol. 3(C), pages 132-140.
    4. Di Consiglio Loredana & Tuoto Tiziana, 2015. "Coverage Evaluation on Probabilistically Linked Data," Journal of Official Statistics, Sciendo, vol. 31(3), pages 415-429, September.
    5. Di Consiglio Loredana & Tuoto Tiziana, 2018. "Population Size Estimation and Linkage Errors: the Multiple Lists Case," Journal of Official Statistics, Sciendo, vol. 34(4), pages 889-908, December.
    6. Paolo Li Donni & Ranjeeta Thomas, 2020. "Latent class models for multiple ordered categorical health data: testing violation of the local independence assumption," Empirical Economics, Springer, vol. 59(4), pages 1903-1931, October.
    7. Janne Petersen & Karen Bandeen-Roche & Esben Budtz-Jørgensen & Klaus Groes Larsen, 2012. "Predicting Latent Class Scores for Subsequent Analysis," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 244-262, April.
    8. Bacci, Silvia & Bartolucci, Francesco & Pieroni, Luca, 2012. "A causal analysis of mother’s education on birth inequalities," MPRA Paper 38754, University Library of Munich, Germany.
    9. Marino, Maria & Donni, Paolo Li & Bavetta, Sebastiano & Cellini, Marco, 2020. "The democratization process: An empirical appraisal of the role of political protest," European Journal of Political Economy, Elsevier, vol. 63(C).
    10. Li Donni, P., 2010. "Risk Preference Heterogeneity And Multiple Demand For Insurance," Health, Econometrics and Data Group (HEDG) Working Papers 10/17, HEDG, c/o Department of Economics, University of York.
    11. Shira Mitchell & Al Ozonoff & Alan M. Zaslavsky & Bethany Hedt-Gauthier & Kristian Lum & Brent A. Coull, 2013. "A Comparison of Marginal and Conditional Models for Capture–Recapture Data with Application to Human Rights Violations Data," Biometrics, The International Biometric Society, vol. 69(4), pages 1022-1032, December.
    12. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    13. Francesco Bartolucci & Fulvia Pennoni, 2007. "A Class of Latent Markov Models for Capture–Recapture Data Allowing for Time, Heterogeneity, and Behavior Effects," Biometrics, The International Biometric Society, vol. 63(2), pages 568-578, June.
    14. Forcina, A. & Dardanoni, V., 2008. "Regression models for multivariate ordered responses via the Plackett distribution," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2472-2478, November.
    15. Orasa Anan & Dankmar Böhning & Antonello Maruotti, 2017. "Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 49-79, March.
    16. Danilo Fegatelli & Luca Tardella, 2013. "Improved inference on capture recapture models with behavioural effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 45-66, March.
    17. Forcina, Antonio, 2008. "Identifiability of extended latent class models with individual covariates," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5263-5268, August.
    18. Francesco Bartolucci & Monia Lupparelli, 2008. "Focused Information Criterion for Capture–Recapture Models for Closed Populations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 629-649, December.
    19. Thandrayen, Joanne & Wang, Yan, 2009. "A latent variable regression model for capture-recapture data," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2740-2746, May.
    20. Dardanoni, V & Li Donni, P, 2008. "Testing For Asymmetric Information In Insurance Markets With Unobservable Types," Health, Econometrics and Data Group (HEDG) Working Papers 08/26, HEDG, c/o Department of Economics, University of York.

  55. Francesco Bartolucci, 2005. "Clustering Univariate Observations via Mixtures of Unimodal Normal Mixtures," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 203-219, September.

    Cited by:

    1. Bacci, Silvia & Bartolucci, Francesco, 2014. "Mixtures of equispaced normal distributions and their use for testing symmetry with univariate data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 262-272.
    2. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.

  56. Bartolucci, F. & Scaccia, L., 2005. "The use of mixtures for dealing with non-normal regression errors," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 821-834, April.

    Cited by:

    1. Saverio Ranciati & Giuliano Galimberti & Gabriele Soffritti, 2019. "Bayesian variable selection in linear regression models with non-normal errors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 323-358, June.
    2. Maria Karlsson & Thomas Laitila, 2014. "Finite mixture modeling of censored regression models," Statistical Papers, Springer, vol. 55(3), pages 627-642, August.
    3. Hu, Hao & Yao, Weixin & Wu, Yichao, 2017. "The robust EM-type algorithms for log-concave mixtures of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 14-26.
    4. Benedetto Lepori & Aldo Geuna & Antonietta Mira, 2018. "Scientific Output of US and European Universities Scales Super-Linearly with Resources," SPRU Working Paper Series 2018-22, SPRU - Science Policy Research Unit, University of Sussex Business School.
    5. Galimberti, Giuliano & Soffritti, Gabriele, 2014. "A multivariate linear regression analysis using finite mixtures of t distributions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 138-150.
    6. Wang, Shangshan & Xiang, Liming, 2017. "Two-layer EM algorithm for ALD mixture regression models: A new solution to composite quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 136-154.
    7. Gabriele Perrone & Gabriele Soffritti, 2023. "Seemingly unrelated clusterwise linear regression for contaminated data," Statistical Papers, Springer, vol. 64(3), pages 883-921, June.
    8. Marilena Furno, 2023. "Computing Finite Mixture Estimators in the Tails," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 267-297, July.
    9. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
    10. Steven Caudill & James Long, 2010. "Do former athletes make better managers? Evidence from a partially adaptive grouped-data regression model," Empirical Economics, Springer, vol. 39(1), pages 275-290, August.
    11. Chee, Chew-Seng & Seo, Byungtae, 2020. "Semiparametric estimation for linear regression with symmetric errors," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    12. Katherine G. Yewell & Steven B. Caudill & Franklin G. Mixon, Jr., 2014. "Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach," Econometrics, MDPI, vol. 2(1), pages 1-19, February.
    13. Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 121-137, June.
    14. Benedetto Lepori & Aldo Geuna & Antonietta Mira, 2019. "Scientific output scales with resources. A comparison of US and European universities," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-18, October.
    15. Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.
    16. Islam, Tanweer ul, 2008. "Normality Testing- A New Direction," MPRA Paper 16452, University Library of Munich, Germany.
    17. Giuliano Galimberti & Gabriele Soffritti, 2020. "Seemingly unrelated clusterwise linear regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 235-260, June.

  57. Francesco Bartolucci & Antonio Forcina, 2005. "Likelihood inference on the underlying structure of IRT models," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 31-43, March.

    Cited by:

    1. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Li Donni, P., 2010. "Risk Preference Heterogeneity And Multiple Demand For Insurance," Health, Econometrics and Data Group (HEDG) Working Papers 10/17, HEDG, c/o Department of Economics, University of York.
    3. Jules Ellis, 2014. "An Inequality for Correlations in Unidimensional Monotone Latent Variable Models for Binary Variables," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 303-316, April.
    4. Rudy Ligtvoet, 2015. "Remarks and a Correction of Ligtvoet’s Treatment of the Isotonic Partial Credit Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 514-515, June.
    5. Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 141-157, June.
    6. Bavetta, Sebastiano & Li Donni, Paolo & Marino, Maria, 2020. "How consistent are perceptions of inequality?," Journal of Economic Psychology, Elsevier, vol. 78(C).
    7. Rudy Ligtvoet, 2022. "Incomplete Tests of Conditional Association for the Assessment of Model Assumptions," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1214-1237, December.
    8. Francesco Bartolucci & Alessio Farcomeni & Luisa Scaccia, 2017. "A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 952-978, December.
    9. Jules L. Ellis & Klaas Sijtsma, 2023. "A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 387-412, June.
    10. Forcina, Antonio, 2008. "Identifiability of extended latent class models with individual covariates," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5263-5268, August.
    11. Svend Kreiner & Karl Christensen, 2011. "Item Screening in Graphical Loglinear Rasch Models," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 228-256, April.
    12. Valentino Dardanoni & Paolo Donni, 2016. "The welfare cost of unpriced heterogeneity in insurance markets," RAND Journal of Economics, RAND Corporation, vol. 47(4), pages 998-1028, November.
    13. Dardanoni, V & Li Donni, P, 2008. "Testing For Asymmetric Information In Insurance Markets With Unobservable Types," Health, Econometrics and Data Group (HEDG) Working Papers 08/26, HEDG, c/o Department of Economics, University of York.

  58. Bartolucci, F. & Scaccia, L., 2004. "Testing for positive association in contingency tables with fixed margins," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 195-210, August.

    Cited by:

    1. Chi Tim Ng & Johan Lim & Kyu S. Hahn, 2011. "Testing stochastic orders in tails of contingency tables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(6), pages 1133-1149, March.
    2. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    3. Alan Agresti, 2014. "Two Bayesian/frequentist challenges for categorical data analyses," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 125-132, August.
    4. Manuela Cazzaro & Roberto Colombi, 2006. "Maximum Likelihood Inference for Log-linear Models Subject to Constraints of Double Monotone Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 177-190, August.
    5. Manuela Cazzaro & Roberto Colombi, 2006. "Maximum Likelihood Inference for Log-linear Models Subject to Constraints of Double Monotone Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 177-190, August.
    6. Linda J. Young & M. Kateri & A. Agresti, 2013. "Bayesian inference about odds ratio structure in ordinal contingency tables," Environmetrics, John Wiley & Sons, Ltd., vol. 24(5), pages 281-288, August.
    7. Kouji Tahata & Takuya Yoshimoto, 2015. "Marginal asymmetry model for square contingency tables with ordered categories," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 371-379, February.
    8. Bartolucci, Francesco & Scaccia, Luisa & Farcomeni, Alessio, 2012. "Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4067-4080.

  59. Bartolucci, F. & De Luca, G., 2003. "Likelihood-based inference for asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 445-449, March.

    Cited by:

    1. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    2. Omori, Yasuhiro & Watanabe, Toshiaki, 2008. "Block sampler and posterior mode estimation for asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2892-2910, February.
    3. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
    4. Nakajima, Jouchi & Omori, Yasuhiro, 2009. "Leverage, heavy-tails and correlated jumps in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2335-2353, April.
    5. Carlos A. Abanto‐Valle & Roland Langrock & Ming‐Hui Chen & Michel V. Cardoso, 2017. "Maximum likelihood estimation for stochastic volatility in mean models with heavy‐tailed distributions," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 394-408, August.
    6. Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020. "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper 106150, University Library of Munich, Germany.
    7. Adam, Timo & Mayr, Andreas & Kneib, Thomas, 2022. "Gradient boosting in Markov-switching generalized additive models for location, scale, and shape," Econometrics and Statistics, Elsevier, vol. 22(C), pages 3-16.
    8. Cagnone, Silvia & Bartolucci, Francesco, 2013. "Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data," MPRA Paper 51037, University Library of Munich, Germany.
    9. Langrock, Roland & MacDonald, Iain L. & Zucchini, Walter, 2012. "Some nonstandard stochastic volatility models and their estimation using structured hidden Markov models," Journal of Empirical Finance, Elsevier, vol. 19(1), pages 147-161.
    10. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.
    11. Roland Langrock & Théo Michelot & Alexander Sohn & Thomas Kneib, 2015. "Semiparametric stochastic volatility modelling using penalized splines," Computational Statistics, Springer, vol. 30(2), pages 517-537, June.

  60. Francesco Bartolucci, 2002. "A recursive algorithm for Markov random fields," Biometrika, Biometrika Trust, vol. 89(3), pages 724-730, August.

    Cited by:

    1. R. Reeves, 2004. "Efficient recursions for general factorisable models," Biometrika, Biometrika Trust, vol. 91(3), pages 751-757, September.
    2. Cai, Bo & Dunson, David B., 2007. "Bayesian Multivariate Isotonic Regression Splines: Applications to Carcinogenicity Studies," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1158-1171, December.
    3. Magnussen, Steen & Reeves, Rob, 2008. "A method for bias-reduction of sample-based MLE of the autologistic model," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 103-111, September.
    4. Rulloni, Valeria, 2014. "Uniqueness condition for an auto-logistic model," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 1-6.
    5. Wanchuang Zhu & Yanan Fan, 2023. "A synthetic likelihood approach for intractable markov random fields," Computational Statistics, Springer, vol. 38(2), pages 749-777, June.
    6. Lim, Johan & Wang, Xinlei & Sherman, Michael, 2007. "An adjustment for edge effects using an augmented neighborhood model in the spatial auto-logistic model," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3679-3688, May.
    7. Cécile Hardouin & Xavier Guyon, 2014. "Recursions on the marginals and exact computation of the normalizing constant for Gibbs processes," Computational Statistics, Springer, vol. 29(6), pages 1637-1650, December.
    8. Daniel A Griffith, 2004. "A Spatial Filtering Specification for the Autologistic Model," Environment and Planning A, , vol. 36(10), pages 1791-1811, October.
    9. Francesco Bartolucci & Alessio Farcomeni, 2010. "A note on the mixture transition distribution and hidden Markov models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 132-138, March.
    10. Nial Friel & Håvard Rue, 2007. "Recursive computing and simulation-free inference for general factorizable models," Biometrika, Biometrika Trust, vol. 94(3), pages 661-672.
    11. Bartolucci, Francesco, 2011. "An alternative to the Baum-Welch recursions for hidden Markov models," MPRA Paper 38778, University Library of Munich, Germany.

  61. Bartolucci F. & Forcina A., 2002. "Extended RC Association Models Allowing for Order Restrictions and Marginal Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1192-1199, December.

    Cited by:

    1. Tamás Rudas & Wicher P. Bergsma, 2004. "On applications of marginal models for categorical data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 15-37.
    2. Forcina, Antonio & Kateri, Maria, 2021. "A new general class of RC association models: Estimation and main properties," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    3. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    4. Alan Agresti, 2014. "Two Bayesian/frequentist challenges for categorical data analyses," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 125-132, August.
    5. Demirhan, Haydar, 2013. "Bayesian estimation of order-restricted and unrestricted association models," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 109-126.
    6. Lui Kung-Jong, 2016. "Testing Equality in Ordinal Data with Repeated Measurements: A Model-Free Approach," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-10, November.
    7. L. Ark & Marcel Croon & Klaas Sijtsma, 2008. "Mokken Scale Analysis for Dichotomous Items Using Marginal Models," Psychometrika, Springer;The Psychometric Society, vol. 73(2), pages 183-208, June.
    8. Carolyn Anderson, 2013. "Multidimensional Item Response Theory Models with Collateral Information as Poisson Regression Models," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 276-303, July.
    9. Gao, Wei & Kuriki, Satoshi, 2006. "Testing marginal homogeneity against stochastically ordered marginals for rxr contingency tables," Journal of Multivariate Analysis, Elsevier, vol. 97(6), pages 1330-1341, July.
    10. G. Iliopoulos & M. Kateri & I. Ntzoufras, 2009. "Bayesian Model Comparison for the Order Restricted RC Association Model," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 561-587, December.
    11. A. Felipe & N. Martín & P. Miranda & L. Pardo, 2018. "Statistical inference in constrained latent class models for multinomial data based on $$\phi $$ ϕ -divergence measures," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 605-636, September.

  62. Francesco Bartolucci & Giovanni De Luca, 2001. "Maximum likelihood estimation of a latent variable time‐series model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(1), pages 5-17, January.

    Cited by:

    1. Michels, Rouven & Ötting, Marius & Langrock, Roland, 2023. "Bettors’ reaction to match dynamics: Evidence from in-game betting," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1118-1127.
    2. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    3. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
    4. Carlos A. Abanto‐Valle & Roland Langrock & Ming‐Hui Chen & Michel V. Cardoso, 2017. "Maximum likelihood estimation for stochastic volatility in mean models with heavy‐tailed distributions," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 394-408, August.
    5. Maddalena Cavicchioli, 2017. "Estimation and asymptotic covariance matrix for stochastic volatility models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 437-452, August.
    6. Cagnone, Silvia & Bartolucci, Francesco, 2013. "Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data," MPRA Paper 51037, University Library of Munich, Germany.
    7. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.
    8. Roland Langrock & Théo Michelot & Alexander Sohn & Thomas Kneib, 2015. "Semiparametric stochastic volatility modelling using penalized splines," Computational Statistics, Springer, vol. 30(2), pages 517-537, June.

  63. Francesco Bartolucci & Antonio Forcina, 2001. "Analysis of Capture-Recapture Data with a Rasch-Type Model Allowing for Conditional Dependence and Multidimensionality," Biometrics, The International Biometric Society, vol. 57(3), pages 714-719, September.

    Cited by:

    1. Shira Mitchell & Al Ozonoff & Alan M. Zaslavsky & Bethany Hedt-Gauthier & Kristian Lum & Brent A. Coull, 2013. "A Comparison of Marginal and Conditional Models for Capture–Recapture Data with Application to Human Rights Violations Data," Biometrics, The International Biometric Society, vol. 69(4), pages 1022-1032, December.
    2. Francesco Bartolucci & Fulvia Pennoni, 2007. "A Class of Latent Markov Models for Capture–Recapture Data Allowing for Time, Heterogeneity, and Behavior Effects," Biometrics, The International Biometric Society, vol. 63(2), pages 568-578, June.
    3. Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 141-157, June.
    4. Danilo Fegatelli & Luca Tardella, 2013. "Improved inference on capture recapture models with behavioural effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 45-66, March.
    5. R. King & S. P. Brooks, 2008. "On the Bayesian Estimation of a Closed Population Size in the Presence of Heterogeneity and Model Uncertainty," Biometrics, The International Biometric Society, vol. 64(3), pages 816-824, September.
    6. Francesco Bartolucci & Antonio Forcina, 2005. "Likelihood inference on the underlying structure of IRT models," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 31-43, March.
    7. Chang Xuan Mao & Cuiying Yang & Yitong Yang & Wei Zhuang, 2017. "Estimating population sizes with the Rasch model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(3), pages 705-716, June.
    8. Elena Stanghellini & Peter G. M. van der Heijden, 2004. "A Multiple-Record Systems Estimation Method that Takes Observed and Unobserved Heterogeneity into Account," Biometrics, The International Biometric Society, vol. 60(2), pages 510-516, June.
    9. Di Cecco Davide & Di Zio Marco & Filipponi Danila & Rocchetti Irene, 2018. "Population Size Estimation Using Multiple Incomplete Lists with Overcoverage," Journal of Official Statistics, Sciendo, vol. 34(2), pages 557-572, June.

  64. Bartolucci, F., 2001. "Developments of the Markov chain approach within the distribution theory of runs," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 107-118, March.

    Cited by:

    1. Cui, Lirong & Kuo, Way & Li, Jinlin & Xie, Min, 2006. "On the dual reliability systems of (n,f,k) and," Statistics & Probability Letters, Elsevier, vol. 76(11), pages 1081-1088, June.

  65. Bartolucci F. & Forcina A. & Dardanoni V., 2001. "Positive Quadrant Dependence and Marginal Modeling in Two-Way Tables With Ordered Margins," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1497-1505, December.

    Cited by:

    1. Tamás Rudas & Wicher P. Bergsma, 2004. "On applications of marginal models for categorical data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 15-37.
    2. Valentino Dardanoni & Mario Fiorini & Antonio Forcina, 2008. "Stochastic Monotonicity in Intergenerational Mobility Tables," Working Paper Series 156, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    3. R. Colombi & A. Forcina, 2016. "Testing order restrictions in contingency tables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(1), pages 73-90, January.
    4. Alan Agresti, 2014. "Two Bayesian/frequentist challenges for categorical data analyses," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 125-132, August.
    5. Lui Kung-Jong, 2016. "Testing Equality in Ordinal Data with Repeated Measurements: A Model-Free Approach," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-10, November.
    6. Bartolucci F. & Forcina A., 2002. "Extended RC Association Models Allowing for Order Restrictions and Marginal Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1192-1199, December.
    7. Manuela Cazzaro & Roberto Colombi, 2006. "Maximum Likelihood Inference for Log-linear Models Subject to Constraints of Double Monotone Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 177-190, August.
    8. Manuela Cazzaro & Roberto Colombi, 2006. "Maximum Likelihood Inference for Log-linear Models Subject to Constraints of Double Monotone Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 177-190, August.
    9. Forcina, A. & Dardanoni, V., 2008. "Regression models for multivariate ordered responses via the Plackett distribution," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2472-2478, November.
    10. Sadao Tomizawa & Nobuko Miyamoto & Kouji Yamamoto, 2006. "Decomposition for polynomial cumulative symmetry model in square contingency tables with ordered categories," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 303-314.
    11. Hailemichael M. Worku & Mark De Rooij, 2017. "Properties of Ideal Point Classification Models for Bivariate Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 308-328, June.
    12. Gordon Anderson & Kinda Hachem, 2013. "Institutions and Economic Outcomes: A Dominance-Based Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 164-182, January.
    13. Zhu, Qiansheng & Lang, Joseph B., 2022. "Test-inversion confidence intervals for estimands in contingency tables subject to equality constraints," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    14. Francesco Bartolucci & Antonio Forcina, 2005. "Likelihood inference on the underlying structure of IRT models," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 31-43, March.
    15. Bartolucci, Francesco & Scaccia, Luisa & Farcomeni, Alessio, 2012. "Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4067-4080.
    16. Gao, Wei & Kuriki, Satoshi, 2006. "Testing marginal homogeneity against stochastically ordered marginals for rxr contingency tables," Journal of Multivariate Analysis, Elsevier, vol. 97(6), pages 1330-1341, July.
    17. A. Forcina & M. Gnaldi & B. Bracalente, 2012. "A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 109-119, March.
    18. Bartolucci, F. & Scaccia, L., 2004. "Testing for positive association in contingency tables with fixed margins," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 195-210, August.

  66. Giorgio Montanari & Francesco Bartolucci, 1998. "On estimating the variance of the systematic sample mean," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 7(2), pages 185-196, August.

    Cited by:

    1. Lorenzo Fattorini & Timothy G. Gregoire & Sara Trentini, 2018. "The Use of Calibration Weighting for Variance Estimation Under Systematic Sampling: Applications to Forest Cover Assessment," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(3), pages 358-373, September.
    2. Giuseppe Espa & Diego Giuliani & Flavio Santi & Emanuele Taufer, 2017. "Model-based variance estimation in two-dimensional systematic sampling," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 265-275, December.

Software components

  1. Francesco Bartolucci, 2014. "CQUAD: Stata module to perform conditional maximum likelihood estimation of quadratic exponential models," Statistical Software Components S457891, Boston College Department of Economics, revised 25 Jul 2015.

    Cited by:

    1. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).

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