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Daniel Peña
(Daniel Pena)

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.

Working papers

  1. García-Ferrer, Antonio & González-Prieto, Ester, 2011. "Exploring ICA for time series decomposition," DES - Working Papers. Statistics and Econometrics. WS ws111611, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Bastos, Guadalupe & García-Martos, Carolina, 2017. "BIAS correction for dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24029, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Matilainen, Markus & Nordhausen, Klaus & Oja, Hannu, 2015. "New independent component analysis tools for time series," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 80-87.

  2. Sánchez, Ismael, 2011. "Densidad de predicción basada en momentos condicionados y máxima entropía : aplicación a la predicción de potencia eólica," DES - Working Papers. Statistics and Econometrics. WS ws111813, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.

  3. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2009. "Comparison of time series with unequal length in the frequency domain," MPRA Paper 15310, University Library of Munich, Germany.

    Cited by:

    1. Lei Jin & Suojin Wang, 2016. "A New Test for Checking the Equality of the Correlation Structures of two time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 355-368, May.
    2. João A. Bastos & Jorge Caiado, 2014. "Clustering financial time series with variance ratio statistics," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2121-2133, December.
    3. Carolina Euán & Hernando Ombao & Joaquín Ortega, 2018. "The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 71-99, April.
    4. Jin, Lei, 2011. "A data-driven test to compare two or multiple time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2183-2196, June.
    5. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    6. Harvill, Jane L. & Ravishanker, Nalini & Ray, Bonnie K., 2013. "Bispectral-based methods for clustering time series," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 113-131.
    7. Jonathan Decowski & Linyuan Li, 2015. "Wavelet-Based Tests for Comparing Two Time Series with Unequal Lengths," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 189-208, March.
    8. Maharaj, Elizabeth Ann & D’Urso, Pierpaolo, 2010. "A coherence-based approach for the pattern recognition of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3516-3537.
    9. E. Otranto, 2008. "Identifying Financial Time Series with Similar Dynamic Conditional Correlation," Working Paper CRENoS 200817, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    10. Jentsch, Carsten & Pauly, Markus, 2012. "A note on using periodogram-based distances for comparing spectral densities," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 158-164.
    11. Goffinet, Etienne & Lebbah, Mustapha & Azzag, Hanane & Loïc, Giraldi & Coutant, Anthony, 2022. "Functional non-parametric latent block model: A multivariate time series clustering approach for autonomous driving validation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    12. Caiado, Jorge & Crato, Nuno, 2009. "Identifying common dynamic features in stock returns," MPRA Paper 15241, University Library of Munich, Germany.
    13. Preuß, Philip & Hildebrandt, Thimo, 2013. "Comparing spectral densities of stationary time series with unequal sample sizes," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1174-1183.
    14. Jorge Caiado & Nuno Crato & Pilar Poncela, 2020. "A fragmented-periodogram approach for clustering big data time series," 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(1), pages 117-146, March.
    15. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    16. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.
    17. Mahmoudi, Mohammad Reza, 2021. "A computational technique to classify several fractional Brownian motion processes," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).

  4. García-Ferrer, Antonio & González-Prieto, Ester, 2008. "A multivariate generalized independent factor GARCH model with an application to financial stock returns," DES - Working Papers. Statistics and Econometrics. WS ws087528, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. St'ephane Chr'etien & Juan-Pablo Ortega, 2011. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Papers 1101.5475, arXiv.org.

  5. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2008. "Estimating and Forecasting GARCH Volatility in the Presence of Outiers," Working Papers. Serie AD 2008-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).

    Cited by:

    1. Sébastien Laurent & Christelle Lecourt & Franz C. Palm, 2016. "Testing for jumps in conditionally Gaussian ARMA-GARCH models, a robust approach," Post-Print hal-01447861, HAL.
    2. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. María José Rodríguez & Esther Ruiz, 2012. "Revisiting Several Popular GARCH Models with Leverage Effect: Differences and Similarities," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 637-668, September.

  6. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Comparison of time series with unequal length," MPRA Paper 6605, University Library of Munich, Germany.

    Cited by:

    1. Lei Jin & Suojin Wang, 2016. "A New Test for Checking the Equality of the Correlation Structures of two time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 355-368, May.
    2. Caiado, Jorge & Crato, Nuno, 2008. "Identifying the evolution of stock markets stochastic structure after the euro," MPRA Paper 6609, University Library of Munich, Germany.
    3. Caiado, Jorge & Crato, Nuno, 2007. "A GARCH-based method for clustering of financial time series: International stock markets evidence," MPRA Paper 2074, University Library of Munich, Germany.
    4. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Is there an identity within international stock market volatilities?," MPRA Paper 2069, University Library of Munich, Germany.

  7. Carnero, María Ángeles, 2004. "Spurious and hidden volatility," DES - Working Papers. Statistics and Econometrics. WS ws042007, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 93-104.
    2. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2006. "Bootstrap prediction for returns and volatilities in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2293-2312, May.

  8. Galeano, Pedro & Tsay, Ruey S., 2004. "Outlier detection in multivariate time series via projection pursuit," DES - Working Papers. Statistics and Econometrics. WS ws044211, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Galeano, Pedro, 2004. "Use of cumulative sums for detection of changepoints in the rate parameter of a poisson process," DES - Working Papers. Statistics and Econometrics. WS ws046816, Universidad Carlos III de Madrid. Departamento de Estadística.

  9. Galeano, Pedro, 2004. "Model selection criteria and quadratic discrimination in ARMA and SETAR time series models," DES - Working Papers. Statistics and Econometrics. WS ws041406, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Pena, Daniel & Rodriguez, Julio, 2005. "Detecting nonlinearity in time series by model selection criteria," International Journal of Forecasting, Elsevier, vol. 21(4), pages 731-748.

  10. Galeano, Pedro, 2004. "Variance changes detection in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS ws041305, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Josua Gösmann & Daniel Ziggel, 2018. "An innovative risk management methodology for trading equity indices based on change points," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 99-109, March.
    2. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.
    3. Dominik Wied, 2017. "A nonparametric test for a constant correlation matrix," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1157-1172, November.
    4. Xu, Ke-Li & Phillips, Peter C.B., 2008. "Adaptive estimation of autoregressive models with time-varying variances," Journal of Econometrics, Elsevier, vol. 142(1), pages 265-280, January.
    5. Herwartz, Helmut & Morales-Arias, Leonardo, 2010. "An empirical analysis of the relationship between US monetary policy and international asset prices," Kiel Working Papers 1581, Kiel Institute for the World Economy (IfW Kiel).
    6. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.

  11. Galeano, Pedro, 2004. "A note on prediction and interpolation errors in time series," DES - Working Papers. Statistics and Econometrics. WS ws042710, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Luis Eduardo Arango & Andrés González & Jhon Jairo León & Luis Fernando Melo, 2006. "Efectos de los cambios en la tasa de intervención del Banco de la República sobre la estructura a plazo," Borradores de Economia 2425, Banco de la Republica.

  12. Redondas, María Dolores, 2003. "Bayesian curve estimation by model averaging," DES - Working Papers. Statistics and Econometrics. WS ws034410, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Rodríguez, Julio, 2008. "A methodology for population projections: an application to Spain," DES - Working Papers. Statistics and Econometrics. WS ws084512, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.

  13. Carnero, María Ángeles, 2003. "Detecting level shifts in the presence of conditional heteroscedasticity," DES - Working Papers. Statistics and Econometrics. WS ws036313, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Galeano, Pedro, 2004. "Use of cumulative sums for detection of changepoints in the rate parameter of a poisson process," DES - Working Papers. Statistics and Econometrics. WS ws046816, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Galeano, Pedro & Tsay, Ruey S., 2004. "Outlier detection in multivariate time series via projection pursuit," DES - Working Papers. Statistics and Econometrics. WS ws044211, Universidad Carlos III de Madrid. Departamento de Estadística.

  14. Carnero, María Ángeles, 2001. "Is stochastic volatility more flexible than garch?," DES - Working Papers. Statistics and Econometrics. WS ws010805, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Broto, Carmen, 2002. "Estimation methods for stochastic volatility models: a survey," DES - Working Papers. Statistics and Econometrics. WS ws025414, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Michel Beine & Charles S. Bos & Sébastien Laurent, 2007. "The Impact of Central Bank FX Interventions on Currency Components," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 154-183.
    3. Rodríguez, Julio, 2003. "A powerful test for conditional heteroscedasticity for financial time series with highly persistent volatilities," DES - Working Papers. Statistics and Econometrics. WS ws036716, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Carnero, María Ángeles, 2001. "Outliers and conditional autoregressive heteroscedasticity in time series," DES - Working Papers. Statistics and Econometrics. WS ws010704, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Pérez, Ana, 2001. "Properties of the sample autocorrelations in autoregressive stochastic volatllity models," DES - Working Papers. Statistics and Econometrics. WS ws011208, Universidad Carlos III de Madrid. Departamento de Estadística.

  15. Galeano, Pedro, 2001. "Multivariate analysis in vector time series," DES - Working Papers. Statistics and Econometrics. WS ws012415, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
    3. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
    4. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
    5. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    6. Druica, Elena & Goschin, Zizi, 2016. "Does Economic Status Matter for the Regional Variation of Malnutrition-Related Diabetes in Romania? Temporal Clustering and Spatial Analyses," MPRA Paper 88831, University Library of Munich, Germany.
    7. Benny Ren & Ian Barnett, 2022. "Autoregressive mixture models for clustering time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 918-937, November.
    8. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," 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. 5(4), pages 323-340, December.
    9. Irene Mariñas-Collado & Ana E. Sipols & M. Teresa Santos-Martín & Elisa Frutos-Bernal, 2022. "Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models," Mathematics, MDPI, vol. 10(15), pages 1-16, July.
    10. Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.
    11. Giovanni De Luca & Paola Zuccolotto, 2017. "Dynamic tail dependence clustering of financial time series," Statistical Papers, Springer, vol. 58(3), pages 641-657, September.
    12. C. Cosculluela-Martínez & R. Ibar-Alonso & G. J. D. Hewings, 2019. "Life Expectancy Index: Age Structure of Population and Environment Evolution," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 507-522, April.
    13. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
    14. Mendes, Beatriz V.M. & Leal, Ricardo P.C. & Carvalhal-da-Silva, Andre, 2007. "Clustering in emerging equity markets," Emerging Markets Review, Elsevier, vol. 8(3), pages 194-205, September.
    15. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    16. Heung-gu Son & Yunsun Kim & Sahm Kim, 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid," Energies, MDPI, vol. 13(9), pages 1-14, May.

  16. Carnero, María Ángeles, 2001. "Outliers and conditional autoregressive heteroscedasticity in time series," DES - Working Papers. Statistics and Econometrics. WS ws010704, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
    2. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    3. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," Working Papers hal-04141344, HAL.
    4. Carnero, María Ángeles, 2003. "Detecting level shifts in the presence of conditional heteroscedasticity," DES - Working Papers. Statistics and Econometrics. WS ws036313, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Olivier Darné & Amélie Charles, 2011. "Large shocks in U.S. macroeconomic time series: 1860-1988," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 5(1), pages 79-100, January.
    6. Charles, Amélie & Darné, Olivier & Pop, Adrian, 2015. "Risk and ethical investment: Empirical evidence from Dow Jones Islamic indexes," Research in International Business and Finance, Elsevier, vol. 35(C), pages 33-56.
    7. Amélie Charles & Olivier Darné, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Post-Print hal-01598141, HAL.
    8. Charles, Amelie & Darne, Olivier, 2006. "Large shocks and the September 11th terrorist attacks on international stock markets," Economic Modelling, Elsevier, vol. 23(4), pages 683-698, July.
    9. Amélie Charles & Olivier Darné, 0. "Econometric history of the growth–volatility relationship in the USA: 1919–2017," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 0, pages 1-24.
    10. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 93-104.
    11. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    12. Amélie Charles & Olivier Darné, 2019. "Volatility estimation for Bitcoin: Replication and robustness," International Economics, CEPII research center, issue 157, pages 23-32.
    13. Muthe Mathias Mwampashi & Christina Sklibosios Nikitopoulos & Otto Konstandatos & Alan Rai, 2020. "Wind Generation and the Dynamics of Electricity Prices in Australia," Research Paper Series 416, Quantitative Finance Research Centre, University of Technology, Sydney.
    14. Charles, Amelie & Darne, Olivier, 2005. "Outliers and GARCH models in financial data," Economics Letters, Elsevier, vol. 86(3), pages 347-352, March.
    15. Yaqoob, Tanzeela & Maqsood, Arfa, 2024. "The potency of time series outliers in volatile models: An empirical analysis of fintech, and mineral resources," Resources Policy, Elsevier, vol. 89(C).
    16. Maixé-Altés, J. Carles & Iglesias, Emma M., 2009. "Domestic monetary transfers and the inland bill of exchange markets in Spain (1775-1885)," Journal of International Money and Finance, Elsevier, vol. 28(3), pages 496-521, April.

  17. Rodríguez, Julio, 2000. "Descriptive measures of multivariate scatter and linear dependence," DES - Working Papers. Statistics and Econometrics. WS 9960, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Luis J. Álvarez & Maria Dolores Gadea & Ana Gómez‐Loscos, 2021. "Inflation comovements in advanced economies: Facts and drivers," The World Economy, Wiley Blackwell, vol. 44(2), pages 485-509, February.
    2. Jorge Gonzalez-Chapela, 2005. "On Measuring Convergence in the Use of Time," MEA discussion paper series 05096, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    3. Dariush Najarzadeh, 2019. "Testing equality of standardized generalized variances of k multivariate normal populations with arbitrary dimensions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 593-623, December.
    4. Iliopoulos, George, 2008. "UMVU estimation of the ratio of powers of normal generalized variances under correlation," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1051-1069, July.
    5. Luis J. Álvarez & Ana Gómez-Loscos & María Dolores Gadea, 2019. "Inflation interdependence in advanced economies," Working Papers 1920, Banco de España.
    6. Giuliodori, Andrea, 2009. "Clustering and classifying images with local and global variability," DES - Working Papers. Statistics and Econometrics. WS ws090101, Universidad Carlos III de Madrid. Departamento de Estadística.

  18. Poncela, Pilar, 2000. "Forecasting with nostationary dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 9959, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.
    2. Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    4. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," DES - Working Papers. Statistics and Econometrics. WS ws081406, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Ortega, Jose Antonio & Poncela, Pilar, 2005. "Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 539-550.
    7. Forni, Mario & Giovannelli, Alessandro & Lippi, Marco & Soccorsi, Stefano, 2016. "Dynamic Factor model with infinite dimensional factor space: forecasting," CEPR Discussion Papers 11161, C.E.P.R. Discussion Papers.
    8. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, vol. 9(8), pages 1-21, July.
    9. Morten {O}rregaard Nielsen & Won-Ki Seo & Dakyung Seong, 2023. "Inference on common trends in functional time series," Papers 2312.00590, arXiv.org, revised May 2024.
    10. Wei-Chun Hsu & Lin Lin & Chen-Yu Li, 2014. "Forecasting automobile sales: the Peña-Box approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(6), pages 568-580, August.
    11. Tu, Yundong & Yao, Qiwei & Zhang, Rongmao, 2020. "Error-correction factor models for high-dimensional cointegrated time series," LSE Research Online Documents on Economics 106994, London School of Economics and Political Science, LSE Library.
    12. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    13. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    14. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    15. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
    16. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    17. Hiroaki Chigira & Taku Yamamoto, 2006. "Forcasting in large cointegrated processes," Hi-Stat Discussion Paper Series d06-169, Institute of Economic Research, Hitotsubashi University.
    18. Korobilis, Dimitris, 2018. "Machine Learning Macroeconometrics A Primer," Essex Finance Centre Working Papers 22666, University of Essex, Essex Business School.
    19. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
    20. Rodríguez, Julio, 2008. "A methodology for population projections: an application to Spain," DES - Working Papers. Statistics and Econometrics. WS ws084512, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021. "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 863-882, December.
    22. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    23. Corona, Francisco & Orraca, Pedro, 2016. "Remittances in Mexico and their unobserved components," DES - Working Papers. Statistics and Econometrics. WS 22674, Universidad Carlos III de Madrid. Departamento de Estadística.
    24. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2011. "Forecasting electricity prices and their volatilities using Unobserved Components," Energy Economics, Elsevier, vol. 33(6), pages 1227-1239.
    25. Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.
    26. M. Pilar Muñoz & Cristina Corchero & F.-Javier Heredia, 2013. "Improving Electricity Market Price Forecasting with Factor Models for the Optimal Generation Bid," International Statistical Review, International Statistical Institute, vol. 81(2), pages 289-306, August.
    27. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    28. Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB-Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    29. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    30. Kosei Fukuda, 2011. "Cointegration rank switching model: an application to forecasting interest rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 509-522, August.

  19. Rodríguez, Julio, 2000. "A powerful portmanteau test of lack of fit for time series," DES - Working Papers. Statistics and Econometrics. WS 10133, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    2. Juan Carlos Escanciano, 2005. "Goodness-of-fit Tests for Linear and Non-linear Time Series Models," Faculty Working Papers 02/05, School of Economics and Business Administration, University of Navarra.
    3. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    4. Carnero, María Ángeles, 2003. "Detecting level shifts in the presence of conditional heteroscedasticity," DES - Working Papers. Statistics and Econometrics. WS ws036313, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Lin, Jen-Wen & McLeod, A.Ian, 2006. "Improved Pena-Rodriguez portmanteau test," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1731-1738, December.
    6. Carnero, María Ángeles, 2004. "Spurious and hidden volatility," DES - Working Papers. Statistics and Econometrics. WS ws042007, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Heni Boubaker, 2015. "Wavelet Estimation of Gegenbauer Processes: Simulation and Empirical Application," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 551-574, December.
    8. Alonso, Andres M. & Maharaj, Elizabeth A., 2006. "Comparison of time series using subsampling," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2589-2599, June.
    9. Rodríguez, Julio, 2003. "A powerful test for conditional heteroscedasticity for financial time series with highly persistent volatilities," DES - Working Papers. Statistics and Econometrics. WS ws036716, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    11. Axel Bücher & Holger Dette & Florian Heinrichs, 2023. "A portmanteau-type test for detecting serial correlation in locally stationary functional time series," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 255-278, July.
    12. Christian Gourieroux & Joann Jasiak, 2021. "Generalized Covariance Estimator," Papers 2107.06979, arXiv.org.
    13. Duchesne, Pierre & Lafaye De Micheaux, Pierre, 2010. "Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 858-862, April.
    14. Serttas, Fatma Ozgu, 2010. "Essays on infinite-variance stable errors and robust estimation procedures," ISU General Staff Papers 201001010800002742, Iowa State University, Department of Economics.
    15. Bouhaddioui, Chafik & Ghoudi, Kilani, 2012. "Empirical processes for infinite variance autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 319-335.
    16. Kwan, Andy C.C. & Sim, Ah-Boon & Wu, Yangru, 2005. "A comparative study of the finite-sample performance of some portmanteau tests for randomness of a time series," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 391-413, February.
    17. Tucker S. McElroy & Anindya Roy, 2022. "Model identification via total Frobenius norm of multivariate spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 473-495, April.
    18. Jan Gooijer, 2008. "Partial sums of lagged cross-products of AR residuals and a test for white noise," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 567-584, November.
    19. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2022. "Data-driven portmanteau tests for time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 675-698, September.
    20. Fahim Afzal & Pan Haiying & Farman Afzal & Asif Mahmood & Amir Ikram, 2021. "Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
    21. Nicolas Pesci & Jean-Philippe Aguilar & Victor James & Fabien Rouillé, 2022. "Inflation Forecasts and European Asset Returns: A Regime-Switching Approach," JRFM, MDPI, vol. 15(10), pages 1-20, October.
    22. Kokoszka, Piotr & Reimherr, Matthew & Wölfing, Nikolas, 2016. "A randomness test for functional panels," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 37-53.

  20. Prieto, Francisco J., 1999. "The kurtosis coeficient and the linear discriminant function," DES - Working Papers. Statistics and Econometrics. WS 6358, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Loperfido, Nicola, 2015. "Vector-valued skewness for model-based clustering," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 230-237.
    2. Salem Reyen & John Miller & Edward Wegman, 2009. "Separating a mixture of two normals with proportional covariances," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(3), pages 297-314, November.
    3. Loperfido, Nicola, 2024. "The skewness of mean–variance normal mixtures," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    4. Loperfido, Nicola, 2014. "A note on the fourth cumulant of a finite mixture distribution," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 386-394.
    5. Loperfido, Nicola, 2018. "Skewness-based projection pursuit: A computational approach," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 42-57.
    6. Loperfido, Nicola, 2013. "Skewness and the linear discriminant function," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 93-99.
    7. Nicola Loperfido, 2023. "Kurtosis removal for data pre-processing," 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. 17(1), pages 239-267, March.

  21. Gil, J. A. & Rodriguez, J., 1999. "Statiscal research in Europe:1985-1997," DES - Working Papers. Statistics and Econometrics. WS 6356, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Giordano, P. & Caputo, P. & Vancheri, A., 2014. "Fuzzy evaluation of heterogeneous quantities: Measuring urban ecological efficiency," Ecological Modelling, Elsevier, vol. 288(C), pages 112-126.

  22. Victor Gómez & Agustin Maravall & Daniel Peña, 1999. "Missing observations in ARIMA models: Skipping strategy versus outlier approach," Working Papers 9701, Banco de España.

    Cited by:

    1. Yusuf Mercan & Benjamin Schoefer & Petr Sedláček, 2024. "A Congestion Theory of Unemployment Fluctuations," American Economic Journal: Macroeconomics, American Economic Association, vol. 16(1), pages 238-285, January.
    2. Carlos Carrillo-Tudela & Ludo Visschers, 2020. "Unemployment and Endogenous Reallocation over the Business Cycle," CESifo Working Paper Series 8288, CESifo.
    3. José Casals & Sonia Sotoca & Miguel Jerez, 2012. "Minimally Conditioned Likelihood for a Nonstationary State Space Model," Documentos de Trabajo del ICAE 2012-04, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    4. Carnero Fernández, María Ángeles & Pérez, Ana, 2014. "Identification of asymmetric conditional heteroscedasticity in the presence of outliers," DES - Working Papers. Statistics and Econometrics. WS ws141912, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Luis Eduardo Arango & Andrés González & John Jairo León & Luis Fernando Melo., 2008. "Cambios de la Tasa de Política y su Efecto en la Estructura a Plazo de Colombia," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 45(132), pages 257-291.
    6. Juan Pedro Muñoz Miguel & Ana Elizabeth García Sipols & Clara Simón de Blas & Francisca Anguita Rodríguez, 2021. "A Model to Evaluate the Effect of Urban Road Pricing on Traffic Speed and Congestion in Madrid City Center and Its Surrounding," Sustainability, MDPI, vol. 13(15), pages 1-23, July.
    7. Yusuf Mercan & Benjamin Schoefer & Petr SedlÃ¡Ä ek, 2020. "A Congestion Theory of Unemployment Fluctuations," Economics Series Working Papers 927, University of Oxford, Department of Economics.
    8. Arash Jamalmanesh & Mahdi Khodaparast Mashhadi & Ahmad Seifi & Mohammad Ali Falahi, 2018. "Prediction of Hydropower Energy Price Using G mes-Maravall Seasonal Model," International Journal of Energy Economics and Policy, Econjournals, vol. 8(2), pages 81-88.
    9. Carlo Mari & Emiliano Mari, 2021. "Gaussian clustering and jump-diffusion models of electricity prices: a deep learning analysis," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1039-1062, December.
    10. Che-Yu Hung & Chien-Chih Wang & Shi-Woei Lin & Bernard C. Jiang, 2022. "An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    11. Yılmaz, Engin, 2015. "Forecasting tourist arrivals to Turkey," MPRA Paper 68616, University Library of Munich, Germany.
    12. Alanya-Beltran, Willy, 2022. "Unit roots in lower-bounded series with outliers," Economic Modelling, Elsevier, vol. 115(C).

  23. Justel, A. & Tsay, Ruey S., 1998. "Detection of outlier patches in autoregressive time series," DES - Working Papers. Statistics and Econometrics. WS 9821, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Tsay, Ruey S. & Pankratz, Alan E., 1998. "Outliers in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS 6285, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Bauer, Marcus & Gather, Ursula & Imhoff, Michael, 1999. "The identification of multiple outliers in online monitoring data," Technical Reports 1999,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

  24. Tsay, Ruey S. & Pankratz, Alan E., 1998. "Outliers in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS 6285, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Anh Nguyen Quoc & Minh Thang Le & Hiep-Hung Pham, 2021. "The Impact of the Third Mission on Teaching and Research Performance: Evidence From Academic Scholars in an Emerging Country," SAGE Open, , vol. 11(4), pages 21582440211, October.

  25. Sánchez, María Jesús, 1997. "The identification of multiple outliers in arima models," DES - Working Papers. Statistics and Econometrics. WS 6220, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Justel, A. & Tsay, Ruey S., 1998. "Detection of outlier patches in autoregressive time series," DES - Working Papers. Statistics and Econometrics. WS 9821, Universidad Carlos III de Madrid. Departamento de Estadística.

  26. Poncela, Pilar, 1997. "Eigenstructure of nonstationary factor models," DES - Working Papers. Statistics and Econometrics. WS 6224, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).

  27. Prieto, Francisco J., 1997. "Robust covariance matrix estimation and multivariate outlier detection," DES - Working Papers. Statistics and Econometrics. WS 10497, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. N. Locantore & J. Marron & D. Simpson & N. Tripoli & J. Zhang & K. Cohen & Graciela Boente & Ricardo Fraiman & Babette Brumback & Christophe Croux & Jianqing Fan & Alois Kneip & John Marden & Daniel P, 1999. "Robust principal component analysis for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(1), pages 1-73, June.

  28. Gómez, Víctor & Maravall, Agustín, 1997. "Missing observations in ARIMA models: skipping strategy versus additive outlier approach," DES - Working Papers. Statistics and Econometrics. WS 10576, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Barry, Boubacar-Sid & Wodon, Quentin, 2007. "Conflict, Growth, and Poverty in Guinea-Bissau," MPRA Paper 11112, University Library of Munich, Germany.
    2. Humberto Lopez & Quentin Wodon & Ian Bannon, 2004. "Rwanda : The Impact of Conflict on Growth and Poverty," World Bank Publications - Reports 11268, The World Bank Group.

  29. Zamar, Rubén, 1996. "A simple diagnostic tool for local prior sensitivity," DES - Working Papers. Statistics and Econometrics. WS 10486, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Passarin Katia, 2004. "Local robustness measures for posterior summaries," Economics and Quantitative Methods qf0405, Department of Economics, University of Insubria.

  30. JUSTEL, Ana & PEÑA , Daniel, 1996. "Bayesian Unmasking in Linear Models," LIDAM Discussion Papers CORE 1996019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Justel, Ana, 1996. "Bayesian unmasking in linear models," DES - Working Papers. Statistics and Econometrics. WS 10458, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Justel, A., 1998. "Heterogeneity and model uncertainty in bayesian regression models," DES - Working Papers. Statistics and Econometrics. WS 6260, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Mohr, Donna L., 2007. "Bayesian identification of clustered outliers in multiple regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3955-3967, May.

  31. Agustín Maravall & Daniel Peña, 1996. "Missing Observations and Additive Outliers in Time Series Models," Working Papers 9612, Banco de España.

    Cited by:

    1. Chin Wen Cheong & Ng Sew Lai & Nurul Afidah Mohmad Yusof & Khor Chia Ying, 2012. "Asymmetric Fractionally Integrated Volatility Modelling of Asian Equity Markets under the Subprime Mortgage Crisis," Journal of Quantitative Economics, The Indian Econometric Society, vol. 10(1), pages 70-84, January.
    2. Mohamed El Hedi Arouri & Jamel Jouini & Nhu Tuyen Le & Duc Khuong Nguyen, 2012. "On the Relationship between World Oil Prices and GCC Stock Markets," Journal of Quantitative Economics, The Indian Econometric Society, vol. 10(1), pages 98-120, January.
    3. Delicado, Pedro, 1995. "Predicción con datos faltantes: aplicación a un caso real," DES - Documentos de Trabajo. Estadística y Econometría. DS 3583, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Pedro Delicado & Ana Justel, 1997. "Forecasting with missing data: Application to a real case," Economics Working Papers 213, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Gomez, Victor & Maravall, Agustin & Pena, Daniel, 1998. "Missing observations in ARIMA models: Skipping approach versus additive outlier approach," Journal of Econometrics, Elsevier, vol. 88(2), pages 341-363, November.
    6. Syed Abul Basher & Stefano Fachin, 2014. "Investigating long-run demand for broad money in the Gulf Arab countries," Middle East Development Journal, Taylor & Francis Journals, vol. 6(2), pages 199-214, July.
    7. Alanya-Beltran, Willy, 2022. "Unit roots in lower-bounded series with outliers," Economic Modelling, Elsevier, vol. 115(C).

  32. Sánchez, Ismael, 1995. "Properties of predictors in overdifferenced nearly nonstationary autoregression," DES - Working Papers. Statistics and Econometrics. WS 10347, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
    2. Alfredo Garcia Hiernaux & Miguel Jerez & José Casals, 2005. "Unit Roots and Cointegrating Matrix Estimation using Subspace Methods," Documentos de Trabajo del ICAE 0512, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.

  33. Justel, Ana & Zamar, Rubén, 1994. "A multivariate Kolmogorov-Smornov test of goodnes of fit," DES - Working Papers. Statistics and Econometrics. WS 3955, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Kathryn Wifvat & John Kumerow & Arkady Shemyakin, 2020. "Copula Model Selection for Vehicle Component Failures Based on Warranty Claims," Risks, MDPI, vol. 8(2), pages 1-15, June.
    2. Katarína Remeňová & Jakub Kintler & Nadežda Jankelová, 2020. "The General Concept of the Revenue Model for Sustainability Growth," Sustainability, MDPI, vol. 12(16), pages 1-12, August.
    3. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
    4. Li, Johnny Siu-Hang & Liu, Yanxin & Chan, Wai-Sum, 2023. "Hedging longevity risk under non-Gaussian state-space stochastic mortality models: A mean-variance-skewness-kurtosis approach," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 96-121.
    5. R. N. Rattihalli, 2023. "A Class of Multivariate Power Skew Symmetric Distributions: Properties and Inference for the Power-Parameter," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1356-1393, August.
    6. Langrené, Nicolas & Warin, Xavier, 2021. "Fast multivariate empirical cumulative distribution function with connection to kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    7. Morteza Amini & Nader Nematollahi, 2017. "Estimation of the Parameters of a Selected Multivariate Population," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(1), pages 13-38, February.
    8. Zuliqar Ali & Ijaz Hussain & Muhammad Faisal & Hafiza Mamona Nazir & Mitwali Abd-el Moemen & Tajammal Hussain & Sadaf Shamsuddin, 2017. "A Novel Multi-Scalar Drought Index for Monitoring Drought: the Standardized Precipitation Temperature Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4957-4969, December.
    9. Chiragiev, Arthur & Landsman, Zinoviy, 2009. "Multivariate flexible Pareto model: Dependency structure, properties and characterizations," Statistics & Probability Letters, Elsevier, vol. 79(16), pages 1733-1743, August.
    10. Torri, Gabriele & Giacometti, Rosella & Paterlini, Sandra, 2018. "Robust and sparse banking network estimation," European Journal of Operational Research, Elsevier, vol. 270(1), pages 51-65.
    11. Zacharias Psaradakis & Marián Vávra, 2015. "A Distance Test of Normality for a Wide Class of Stationary Processes," Birkbeck Working Papers in Economics and Finance 1513, Birkbeck, Department of Economics, Mathematics & Statistics.
    12. Carole Bernard & Oleg Bondarenko & Steven Vanduffel, 2021. "A model-free approach to multivariate option pricing," Review of Derivatives Research, Springer, vol. 24(2), pages 135-155, July.
    13. Jin Zhang & Yuehua Wu, 2002. "Beta Approximation to the Distribution of Kolmogorov-Smirnov Statistic," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(3), pages 577-584, September.
    14. Jean-David Fermanian, 2003. "Goodness of Fit Tests for Copulas," Working Papers 2003-34, Center for Research in Economics and Statistics.
    15. Petrie, Adam & Willemain, Thomas R., 2013. "An empirical study of tests for uniformity in multidimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 253-268.
    16. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    17. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    18. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    19. Petrie, Adam, 2016. "Graph-theoretic multisample tests of equality in distribution for high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 145-158.
    20. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    21. Chunming Zhang & Jimin Ye & Xiaomei Wang, 2023. "A Computational Perspective on Projection Pursuit in High Dimensions: Feasible or Infeasible Feature Extraction," International Statistical Review, International Statistical Institute, vol. 91(1), pages 140-161, April.
    22. Knyazev, Alexander & Lepekhin, Oleg & Shemyakin, Arkady, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 30-53.
    23. Audrius Kabašinskas & Leonidas Sakalauskas & Ingrida Vaičiulytė, 2021. "An Analytical EM Algorithm for Sub-Gaussian Vectors," Mathematics, MDPI, vol. 9(9), pages 1-20, April.
    24. Brockwell, A.E., 2007. "Universal residuals: A multivariate transformation," Statistics & Probability Letters, Elsevier, vol. 77(14), pages 1473-1478, August.
    25. Bellini, Tiziano, 2013. "Integrated bank risk modeling: A bottom-up statistical framework," European Journal of Operational Research, Elsevier, vol. 230(2), pages 385-398.
    26. Jun Wang & Shouhong Zhang & Yiping Guo, 2019. "Analyzing the Impact of Impervious Area Disconnection on Urban Runoff Control Using an Analytical Probabilistic Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1753-1768, March.
    27. Wang, Rui & Purshouse, Robin C. & Fleming, Peter J., 2015. "Preference-inspired co-evolutionary algorithms using weight vectors," European Journal of Operational Research, Elsevier, vol. 243(2), pages 423-441.
    28. Cheng, Qixiu & Lin, Yuqian & Zhou, Xuesong (Simon) & Liu, Zhiyuan, 2024. "Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters," European Journal of Operational Research, Elsevier, vol. 312(1), pages 182-197.
    29. Fermanian, Jean-David, 2005. "Goodness-of-fit tests for copulas," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 119-152, July.
    30. Carolina Gil Marcelino & Carlos Camacho-Gómez & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm," Energies, MDPI, vol. 14(9), pages 1-24, April.
    31. Naaman, Michael, 2021. "On the tight constant in the multivariate Dvoretzky–Kiefer–Wolfowitz inequality," Statistics & Probability Letters, Elsevier, vol. 173(C).
    32. Squalli, Jay, 2017. "Renewable energy, coal as a baseload power source, and greenhouse gas emissions: Evidence from U.S. state-level data," Energy, Elsevier, vol. 127(C), pages 479-488.

  34. Gómez, Víctor & Maravall, Agustín, 1993. "Computing missing values in time series," DES - Working Papers. Statistics and Econometrics. WS 3737, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Victor M. Guerrero & Daniel Peña, 1995. "Linear Combination of Information in Time Series Analysis," Working Papers 9507, Centro de Investigacion Economica, ITAM.
    2. Baragona, Roberto & Battaglia, Francesco & Calzini, Claudio, 2001. "Genetic algorithms for the identification of additive and innovation outliers in time series," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 1-12, July.

  35. Juan, Jesús, 1992. "A simple method to identify significant effects in unreplicated two-level factorial designs," UC3M Working papers. Economics 2818, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Aboukalam, M. A. F. & Al-Shiha, A. A., 2001. "A robust analysis for unreplicated factorial experiments," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 31-46, March.

  36. Yohai, Víctor J., 1991. "The detection of influential subsets in linear regression using an influence matrix," UC3M Working papers. Economics 2798, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Gregorio R. Serrano García, 1994. "Estadísticos para la detección de observaciones anómalas en modelos de elección binaria: una aplicación con datos reales," Documentos de Trabajo del ICAE 9403, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.

  37. Tiao, George C., 1991. "A Note on likelihood estimation of missing values in time series," UC3M Working papers. Economics 2748, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. Gómez, Víctor & Maravall, Agustín, 1993. "Computing missing values in time series," DES - Working Papers. Statistics and Econometrics. WS 3737, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. A. Pérez-González & J. Vilar-Fernández & W. González-Manteiga, 2009. "Asymptotic properties of local polynomial regression with missing data and correlated errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 85-109, March.

Articles

  1. Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.

    Cited by:

    1. Mihnea Constantinescu, 2023. "Sparse Warcasting," Working Papers 01/2023, National Bank of Ukraine.
    2. Matteo Barigozzi & Marc Hallin & Matteo Luciani & Paolo Zaffaroni, 2021. "Inferential Theory for Generalized Dynamic Factor Models," Working Papers ECARES 2021-20, ULB -- Universite Libre de Bruxelles.
    3. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.

  2. Alonso, Andrés M. & Galeano, Pedro & Peña, Daniel, 2020. "A robust procedure to build dynamic factor models with cluster structure," Journal of Econometrics, Elsevier, vol. 216(1), pages 35-52.

    Cited by:

    1. Camacho, Maximo, 2020. "What do international energy prices have in common after taking into account the key drivers?," DES - Working Papers. Statistics and Econometrics. WS 31647, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Zhaoxing Gao & Ruey S. Tsay, 2021. "Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data," Papers 2103.14626, arXiv.org.
    3. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    4. Boudt, Kris & Heyndels, Ewoud, 2024. "Robust interactive fixed effects," Econometrics and Statistics, Elsevier, vol. 29(C), pages 206-223.
    5. Blasques, Francisco & Hoogerkamp, Meindert Heres & Koopman, Siem Jan & van de Werve, Ilka, 2021. "Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1426-1441.
    6. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    7. Massimiliano Caporin & C. Vladimir Rodríguez-Caballero & Esther Ruiz, 2024. "The factor structure of exchange rates volatility: global and intermittent factors," Empirical Economics, Springer, vol. 67(1), pages 31-45, July.
    8. Camacho, Maximo & Lopez-Buenache, German, 2023. "Factor models for large and incomplete data sets with unknown group structure," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1205-1220.
    9. Zhe Sun & Yundong Tu, 2024. "Factors in Fashion: Factor Analysis towards the Mode," Papers 2409.19287, arXiv.org.
    10. Camacho, Maximo & Caro, Angela & Peña, Daniel, 2023. "What drives industrial energy prices?," Economic Modelling, Elsevier, vol. 120(C).

  3. Daniel Peña & Ezequiel Smucler & Victor J. Yohai, 2019. "Forecasting Multiple Time Series With One-Sided Dynamic Principal Components," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1683-1694, October.

    Cited by:

    1. Andriantomanga, Zo, 2023. "The role of survey-based expectations in real-time forecasting of US inflation," MPRA Paper 119904, University Library of Munich, Germany.
    2. Smucler, Ezequiel, 2019. "Consistency of generalized dynamic principal components in dynamic factor models," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    3. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    4. Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.

  4. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.

    Cited by:

    1. Reiko Aoki & Juan P. M. Bustamante & Gilberto A. Paula, 2022. "Local influence diagnostics with forward search in regression analysis," Statistical Papers, Springer, vol. 63(5), pages 1477-1497, October.
    2. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

  5. Pedro Galeano & Daniel Peña, 2019. "Rejoinder on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 363-368, June.

    Cited by:

    1. Reiko Aoki & Juan P. M. Bustamante & Gilberto A. Paula, 2022. "Local influence diagnostics with forward search in regression analysis," Statistical Papers, Springer, vol. 63(5), pages 1477-1497, October.
    2. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

  6. Daniel Peña & Victor J. Yohai, 2016. "Generalized Dynamic Principal Components," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1121-1131, July.

    Cited by:

    1. Marc Hallin & Siegfried Hörmann & Marco Lippi, 2017. "Optimal Dimension Reduction for High-dimensional and Functional Time Series," Working Papers ECARES ECARES 2017-39, ULB -- Universite Libre de Bruxelles.
    2. Andriantomanga, Zo, 2023. "The role of survey-based expectations in real-time forecasting of US inflation," MPRA Paper 119904, University Library of Munich, Germany.
    3. Smucler, Ezequiel, 2019. "Consistency of generalized dynamic principal components in dynamic factor models," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    4. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    5. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    6. Amira Elayouty & Marian Scott & Claire Miller, 2022. "Time-Varying Functional Principal Components for Non-Stationary EpCO $$_2$$ 2 in Freshwater Systems," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 506-522, September.
    7. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2020. "Time-varying general dynamic factor models and the measurement of financial connectedness," LIDAM Reprints ISBA 2020015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Guangbao Guo & Chunjie Wei & Guoqi Qian, 2023. "Sparse online principal component analysis for parameter estimation in factor model," Computational Statistics, Springer, vol. 38(2), pages 1095-1116, June.
    9. Fayed Alshammri & Jiazhu Pan, 2021. "Moving dynamic principal component analysis for non-stationary multivariate time series," Computational Statistics, Springer, vol. 36(3), pages 2247-2287, September.
    10. Juan B'ogalo & Pilar Poncela & Eva Senra, 2020. "Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis," Papers 2007.07561, arXiv.org, revised Aug 2023.
    11. Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.

  7. García-Ferrer, Antonio & González-Prieto, Ester & Peña, Daniel, 2012. "A conditionally heteroskedastic independent factor model with an application to financial stock returns," International Journal of Forecasting, Elsevier, vol. 28(1), pages 70-93.

    Cited by:

    1. Marc Hallin & Simos Meintanis & Klaus Nordhausen, 2024. "Consistent Distribution–Free Affine–Invariant Tests for the Validity of Independent Component Models," Working Papers ECARES 2024-04, ULB -- Universite Libre de Bruxelles.
    2. Jari Miettinen & Katrin Illner & Klaus Nordhausen & Hannu Oja & Sara Taskinen & Fabian J. Theis, 2016. "Separation of Uncorrelated Stationary time series using Autocovariance Matrices," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 337-354, May.

  8. Carnero, M. Angeles & Peña, Daniel & Ruiz, Esther, 2012. "Estimating GARCH volatility in the presence of outliers," Economics Letters, Elsevier, vol. 114(1), pages 86-90.

    Cited by:

    1. Doan, Bao & Papageorgiou, Nicolas & Reeves, Jonathan J. & Sherris, Michael, 2018. "Portfolio management with targeted constant market volatility," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 134-147.
    2. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
    3. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    4. Cioroianu, Iulia & Corbet, Shaen & Larkin, Charles, 2021. "Guilt through association: Reputational contagion and the Boeing 737-MAX disasters," Economics Letters, Elsevier, vol. 198(C).
    5. Manh Ha Nguyen & Olivier Darné, 2018. "Forecasting and risk management in the Vietnam Stock Exchange," Working Papers halshs-01679456, HAL.
    6. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    7. Hotta, Luiz & Trucíos, Carlos, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," Working Papers hal-04141198, HAL.
    9. Liu, Min & Taylor, James W. & Choo, Wei-Chong, 2020. "Further empirical evidence on the forecasting of volatility with smooth transition exponential smoothing," Economic Modelling, Elsevier, vol. 93(C), pages 651-659.
    10. Amélie Charles & Olivier Darné, 2019. "The accuracy of asymmetric GARCH model estimation," Post-Print hal-01943883, HAL.
    11. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    12. Charles, Amélie & Darné, Olivier & Pop, Adrian, 2015. "Risk and ethical investment: Empirical evidence from Dow Jones Islamic indexes," Research in International Business and Finance, Elsevier, vol. 35(C), pages 33-56.
    13. Amélie Charles & Olivier Darné, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Post-Print hal-01598141, HAL.
    14. Abdeljalil Settar & Nadia Idrissi Fatmi & Mohammed Badaoui, 2021. "New Approach in Dealing with the Non-Negativity of the Conditional Variance in the Estimation of GARCH Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(1), pages 55-74, March.
    15. Anupam Dutta & Elie Bouri, 2022. "Outliers and Time-Varying Jumps in the Cryptocurrency Markets," JRFM, MDPI, vol. 15(3), pages 1-7, March.
    16. Charles, Amélie & Darné, Olivier, 2019. "The accuracy of asymmetric GARCH model estimation," International Economics, Elsevier, vol. 157(C), pages 179-202.
    17. Carnero M. Angeles & Pérez Ana, 2021. "Outliers and misleading leverage effect in asymmetric GARCH-type models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-19, February.
    18. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    19. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
    20. Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
    21. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    22. Grané, Aurea & Martín-Barragán, Belén & Veiga, Helena, 2014. "Outliers in multivariate Garch models," DES - Working Papers. Statistics and Econometrics. WS ws140503, Universidad Carlos III de Madrid. Departamento de Estadística.
    23. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    24. Bahram Adrangi & Arjun Chatrath & Kambiz Raffiee, 2023. "S&P 500 volatility, volatility regimes, and economic uncertainty," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1362-1387, October.
    25. Carnero, M. Angeles & Pérez, Ana, 2019. "Leverage effect in energy futures revisited," Energy Economics, Elsevier, vol. 82(C), pages 237-252.
    26. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    27. Wang, Weichen & An, Ran & Zhu, Ziwei, 2024. "Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective," Journal of Econometrics, Elsevier, vol. 239(2).
    28. Patrick F. Patrocinio & Valderio A. Reisen & Pascal Bondon & Edson Z. Monte & Ian M. Danilevicz, 2024. "M-Quantile Estimation for GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2175-2192, June.
    29. Yingying Xu & Donald Lien, 2022. "Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 259-278, March.
    30. Trucíos, Carlos & Hotta, Luiz K., 2016. "Bootstrap prediction in univariate volatility models with leverage effect," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 120(C), pages 91-103.
    31. Huang Zibin & Ibragimov Rustam, 2022. "Equity returns and sentiment," Dependence Modeling, De Gruyter, vol. 10(1), pages 159-176, January.
    32. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.

  9. Miguel Ángel Bermejo & Daniel Peña & Ismael Sánchez, 2011. "Identification of TAR models using recursive estimation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(1), pages 31-50, January.

    Cited by:

    1. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2013. "Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities," Applied Energy, Elsevier, vol. 101(C), pages 363-375.

  10. E. Silva & V. M. Guerrero & D. Peña, 2011. "Temporal disaggregation and restricted forecasting of multiple population time series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(4), pages 799-815, January.

    Cited by:

    1. Víctor M. Guerrero & Francisco Corona, 2018. "Retropolating some relevant series of Mexico's System of National Accounts at constant prices: The case of Mexico City's GDP," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 495-519, November.

  11. Peña, Daniel & Prieto, Francisco J. & Viladomat, Júlia, 2010. "Eigenvectors of a kurtosis matrix as interesting directions to reveal cluster structure," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 1995-2007, October.

    Cited by:

    1. Becquart, Colombe & Archimbaud, Aurore & Ruiz-Gazen, Anne & Prilé, Luka & Nordhausen, Klaus, 2024. "Invariant Coordinate Selection and Fisher Discriminant Subspace Beyond The Case of Two Groups," TSE Working Papers 24-1579, Toulouse School of Economics (TSE).
    2. Sreenivasa Rao Jammalamadaka & Emanuele Taufer & György H. Terdik, 2021. "Asymptotic theory for statistics based on cumulant vectors with applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 708-728, June.
    3. Alashwali, Fatimah & Kent, John T., 2016. "The use of a common location measure in the invariant coordinate selection and projection pursuit," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 145-161.
    4. Loperfido, Nicola, 2014. "A note on the fourth cumulant of a finite mixture distribution," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 386-394.
    5. Klaus Nordhausen & Anne Ruiz-Gazen, 2022. "On the usage of joint diagonalization in multivariate statistics," Post-Print hal-04296111, HAL.
    6. Loperfido, Nicola, 2021. "Some theoretical properties of two kurtosis matrices, with application to invariant coordinate selection," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    7. Loperfido, Nicola, 2018. "Skewness-based projection pursuit: A computational approach," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 42-57.
    8. Loperfido, Nicola, 2013. "Skewness and the linear discriminant function," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 93-99.
    9. Javed, Farrukh & Loperfido, Nicola & Mazur, Stepan, 2024. "The Method of Moments for Multivariate Random Sums," Working Papers 2024:6, Örebro University, School of Business.
    10. Xiaotian Zhu & David R. Hunter, 2019. "Clustering via finite nonparametric ICA 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. 13(1), pages 65-87, March.
    11. Nicola Loperfido, 2023. "Kurtosis removal for data pre-processing," 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. 17(1), pages 239-267, March.

  12. Benito, Monica & Pena, Daniel, 2007. "Detecting defects with image data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6395-6403, August.

    Cited by:

    1. Renaut, Rosemary A. & Hnetynková, Iveta & Mead, Jodi, 2010. "Regularization parameter estimation for large-scale Tikhonov regularization using a priori information," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3430-3445, December.

  13. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2007. "Effects of outliers on the identification and estimation of GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(4), pages 471-497, July.

    Cited by:

    1. Doan, Bao & Papageorgiou, Nicolas & Reeves, Jonathan J. & Sherris, Michael, 2018. "Portfolio management with targeted constant market volatility," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 134-147.
    2. Lisa Crosato & Luigi Grossi, 2019. "Correcting outliers in GARCH models: a weighted forward approach," Statistical Papers, Springer, vol. 60(6), pages 1939-1970, December.
    3. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
    4. Rezitis Anthony N & Stavropoulos Konstantinos S, 2011. "Price Transmission and Volatility in the Greek Broiler Sector: A Threshold Cointegration Analysis," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 9(1), pages 1-37, July.
    5. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    6. Hans DEWACHTER & Deniz ERDEMLIOGLU & Jean-Yves GNABO & Christelle LECOURT, 2013. "The intra-day impact of communication on euro-dollar volatility and jumps," Working Papers of Department of Economics, Leuven ces13.04, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    7. Melike Bildirici & Nilgun Guler Bayazit & Yasemen Ucan, 2020. "Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM," Energies, MDPI, vol. 13(11), pages 1-18, June.
    8. Manh Ha Nguyen & Olivier Darné, 2018. "Forecasting and risk management in the Vietnam Stock Exchange," Working Papers halshs-01679456, HAL.
    9. Vasiliki Chatzikonstanti & Michail Karoglou, 2022. "Can black swans be tamed with a flexible mean‐variance specification?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3202-3227, July.
    10. S. Bhaumik & M. Karanasos & A. Kartsaklas, 2008. "Derivatives Trading and the Volume-Volatility Link in the Indian Stock Market," William Davidson Institute Working Papers Series wp935, William Davidson Institute at the University of Michigan.
    11. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    12. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," Working Papers hal-04141344, HAL.
    13. Broto, Carmen, 2013. "The effectiveness of forex interventions in four Latin American countries," Emerging Markets Review, Elsevier, vol. 17(C), pages 224-240.
    14. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
    15. Carnero Fernández, María Ángeles & Pérez, Ana, 2014. "Identification of asymmetric conditional heteroscedasticity in the presence of outliers," DES - Working Papers. Statistics and Econometrics. WS ws141912, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Veiga, Helena, 2006. "Modelling long-memory volatilities with leverage effect: ALMSV versus FIEGARCH," DES - Working Papers. Statistics and Econometrics. WS ws066016, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Hotta, Luiz & Trucíos, Carlos, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Amélie Charles & Olivier Darné, 2019. "The accuracy of asymmetric GARCH model estimation," Post-Print hal-01943883, HAL.
    19. Sébastien Laurent & Christelle Lecourt & Franz C. Palm, 2016. "Testing for jumps in conditionally Gaussian ARMA-GARCH models, a robust approach," Post-Print hal-01447861, HAL.
    20. Francisco Javier Duque-Pintor & Manuel Jesús Fernández-Gómez & Alicia Troncoso & Francisco Martínez-Álvarez, 2016. "A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series," Energies, MDPI, vol. 9(9), pages 1-10, September.
    21. Grossi, Luigi & Laurini, Fabrizio, 2009. "A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2251-2263, April.
    22. Charles, Amélie & Darné, Olivier & Pop, Adrian, 2015. "Risk and ethical investment: Empirical evidence from Dow Jones Islamic indexes," Research in International Business and Finance, Elsevier, vol. 35(C), pages 33-56.
    23. Amélie Charles & Olivier Darné, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Post-Print hal-01598141, HAL.
    24. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2016. "Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 582-594.
    25. Karanasos, M. & Kartsaklas, A., 2009. "Dual long-memory, structural breaks and the link between turnover and the range-based volatility," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 838-851, December.
    26. Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 551-565.
    27. Charles, Amélie & Darné, Olivier, 2019. "The accuracy of asymmetric GARCH model estimation," International Economics, Elsevier, vol. 157(C), pages 179-202.
    28. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.
    29. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    30. Piotr Fiszeder & Marta Ma³ecka, 2022. "Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(4), pages 939-967, December.
    31. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    32. Sucarrat, Genaro, 2016. "Equation-by-Equation Estimation of Multivariate Periodic Electricity Price Volatility," UC3M Working papers. Economics 23436, Universidad Carlos III de Madrid. Departamento de Economía.
    33. Grané, Aurea & Martín-Barragán, Belén & Veiga, Helena, 2014. "Outliers in multivariate Garch models," DES - Working Papers. Statistics and Econometrics. WS ws140503, Universidad Carlos III de Madrid. Departamento de Estadística.
    34. Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers 202437, University of Pretoria, Department of Economics.
    35. Spierdijk, Laura, 2016. "Confidence intervals for ARMA–GARCH Value-at-Risk: The case of heavy tails and skewness," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 545-559.
    36. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    37. Guanghui Cai & Zhimin Wu & Lei Peng, 2021. "Forecasting volatility with outliers in Realized GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 667-685, July.
    38. Amélie Charles & Olivier Darné, 2019. "Volatility estimation for Bitcoin: Replication and robustness," International Economics, CEPII research center, issue 157, pages 23-32.
    39. Carnero, M. Angeles & Pérez, Ana, 2019. "Leverage effect in energy futures revisited," Energy Economics, Elsevier, vol. 82(C), pages 237-252.
    40. Alex Huang, 2011. "Volatility Modeling by Asymmetrical Quadratic Effect with Diminishing Marginal Impact," Computational Economics, Springer;Society for Computational Economics, vol. 37(3), pages 301-330, March.
    41. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2008. "Estimating and Forecasting GARCH Volatility in the Presence of Outiers," Working Papers. Serie AD 2008-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    42. Broto, Carmen, 2011. "Inflation targeting in Latin America: Empirical analysis using GARCH models," Economic Modelling, Elsevier, vol. 28(3), pages 1424-1434, May.
    43. Sucarrat, Genaro, 2018. "The Log-GARCH Model via ARMA Representations," MPRA Paper 100386, University Library of Munich, Germany.
    44. Carnero, M. Angeles & Peña, Daniel & Ruiz, Esther, 2012. "Estimating GARCH volatility in the presence of outliers," Economics Letters, Elsevier, vol. 114(1), pages 86-90.
    45. Veiga, Helena, 2009. "Wavelet-based detection of outliers in volatility models," DES - Working Papers. Statistics and Econometrics. WS ws090403, Universidad Carlos III de Madrid. Departamento de Estadística.
    46. Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
    47. Bretó, Carles & Veiga, Helena, 2011. "Forecasting volatility: does continuous time do better than discrete time?," DES - Working Papers. Statistics and Econometrics. WS ws112518, Universidad Carlos III de Madrid. Departamento de Estadística.
    48. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
    49. Bhaumik, S. & Karanasos, M. & Kartsaklas, A., 2016. "The informative role of trading volume in an expanding spot and futures market," Journal of Multinational Financial Management, Elsevier, vol. 35(C), pages 24-40.

  14. Daniel Peña & Ismael Sánchez, 2007. "Measuring the Advantages of Multivariate vs. Univariate Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 886-909, November.

    Cited by:

    1. Juan Carlos Pérez-Velasco Pavón, 2009. "Determinantes de la demanda por la denominación promedio de billete: el caso de México," Monetaria, CEMLA, vol. 0(4), pages 523-548, octubre-d.
    2. Juan Díaz Maureira & Gustavo Leyva Jiménez, 2009. "Proyección de la inflación chilena en tiempos difíciles," Monetaria, CEMLA, vol. 0(4), pages 491-522, octubre-d.
    3. Emrah Oral & Gazanfer Unal, 2019. "Modeling and forecasting time series of precious metals: a new approach to multifractal data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-28, December.
    4. Ricardo Gimeno & José Manuel Marqués-Sevillano, 2009. "Incertidumbre y el precio del riesgo en un proceso de convergencia nominal," Monetaria, CEMLA, vol. 0(4), pages 451-489, octubre-d.
    5. Andrés Schneider, 2009. "Regímenes de flotación administrada: un enfoque de cartera," Monetaria, CEMLA, vol. 0(4), pages 549-584, octubre-d.
    6. Shaokang Wang & Han Lin Shang & Leonie Tickle & Han Li, 2024. "Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing," Risks, MDPI, vol. 12(7), pages 1-15, July.

  15. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.

    Cited by:

    1. Shahsuzan Zakaria, 2023. "The Need to go Beyond Deterministic Data Envelopment Analysis (DEA): A Comparative Analysis with Bootstrapping DEA in Risk Management Efficiency Measurements," Information Management and Business Review, AMH International, vol. 15(4), pages 433-446.
    2. João Henrique G. Mazzeu & Gloria González-Rivera & Esther Ruiz & Helena Veiga, 2020. "A bootstrap approach for generalized Autocontour testing Implications for VIX forecast densities," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 971-990, November.
    3. A. R. Nematollahi & A. R. Soltani & M. R. Mahmoudi, 2017. "Periodically correlated modeling by means of the periodograms asymptotic distributions," Statistical Papers, Springer, vol. 58(4), pages 1267-1278, December.
    4. Halkos, George E. & Tzeremes, Nickolaos G., 2011. "A conditional nonparametric analysis for measuring the efficiency of regional public healthcare delivery: An application to Greek prefectures," Health Policy, Elsevier, vol. 103(1), pages 73-82.
    5. Anna Staszewska-Bystrova & Peter Winker, 2016. "Improved bootstrap prediction intervals for SETAR models," Statistical Papers, Springer, vol. 57(1), pages 89-98, March.
    6. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    7. Ernest Fokoue & Bertrand Clarke, 2011. "Bias-variance trade-off for prequential model list selection," Statistical Papers, Springer, vol. 52(4), pages 813-833, November.
    8. Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Wanbo Lu & Rui Ke, 2019. "A generalized least squares estimation method for the autoregressive conditional duration model," Statistical Papers, Springer, vol. 60(1), pages 123-146, February.
    10. Sroka Łukasz, 2022. "Applying Block Bootstrap Methods in Silver Prices Forecasting," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(2), pages 15-29, June.
    11. Songul Cinaroglu, 2024. "Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis," Health Care Management Science, Springer, vol. 27(1), pages 88-113, March.
    12. Nicholas Apergis & Michael L. Polemis, 2016. "Competition and efficiency in the MENA banking region: a non-structural DEA approach," Applied Economics, Taylor & Francis Journals, vol. 48(54), pages 5276-5291, November.

  16. Galeano, Pedro & Pena, Daniel & Tsay, Ruey S., 2006. "Outlier Detection in Multivariate Time Series by Projection Pursuit," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 654-669, June.

    Cited by:

    1. Kemp, GCR & Parente, PMDC & Santos Silva, JMC, 2015. "Dynamic Vector Mode Regression," Economics Discussion Papers 13793, University of Essex, Department of Economics.
    2. Galeano, Pedro, 2004. "Use of cumulative sums for detection of changepoints in the rate parameter of a poisson process," DES - Working Papers. Statistics and Econometrics. WS ws046816, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Francisco Javier Duque-Pintor & Manuel Jesús Fernández-Gómez & Alicia Troncoso & Francisco Martínez-Álvarez, 2016. "A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series," Energies, MDPI, vol. 9(9), pages 1-10, September.
    4. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
    5. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    7. Massimiliano Caporin & C. Vladimir Rodríguez-Caballero & Esther Ruiz, 2024. "The factor structure of exchange rates volatility: global and intermittent factors," Empirical Economics, Springer, vol. 67(1), pages 31-45, July.
    8. Grané, Aurea & Martín-Barragán, Belén & Veiga, Helena, 2014. "Outliers in multivariate Garch models," DES - Working Papers. Statistics and Econometrics. WS ws140503, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Fernandes, Leonardo H.S. & Silva, José W.L. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2023. "Multifractal cross-correlations between green bonds and financial assets," Finance Research Letters, Elsevier, vol. 53(C).
    10. Alonso, Andrés M. & Galeano, Pedro & Peña, Daniel, 2020. "A robust procedure to build dynamic factor models with cluster structure," Journal of Econometrics, Elsevier, vol. 216(1), pages 35-52.
    11. Tadeusz Klecha & Daniel Kosiorowski & Dominik Mielczarek & Jerzy P. Rydlewski, 2018. "New Proposals of a Stress Measure in a Capital and its Robust Estimator," Papers 1802.03756, arXiv.org.
    12. Muler, Nora & Yohai, V´ictor J., 2013. "Robust estimation for vector autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 68-79.
    13. Croux, Christophe & Gelper, Sarah & Mahieu, Koen, 2010. "Robust exponential smoothing of multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2999-3006, December.
    14. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    15. Veiga, Helena, 2009. "Wavelet-based detection of outliers in volatility models," DES - Working Papers. Statistics and Econometrics. WS ws090403, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
    17. Keisuke Yoshihara & Kei Takahashi, 2022. "A simple method for unsupervised anomaly detection: An application to Web time series data," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-25, January.
    18. Nicola Loperfido, 2023. "Kurtosis removal for data pre-processing," 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. 17(1), pages 239-267, March.

  17. Pena, Daniel & Redondas, Dolores, 2006. "Bayesian curve estimation by model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 688-709, February.
    See citations under working paper version above.
  18. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.

    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Ozan Cinar & Ozlem Ilk & Cem Iyigun, 2018. "Clustering of short time-course gene expression data with dissimilar replicates," Annals of Operations Research, Springer, vol. 263(1), pages 405-428, April.
    3. Francesca Di Iorio & Umberto Triacca, 2022. "A comparison between VAR processes jointly modeling GDP and Unemployment rate in France and Germany," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 617-635, September.
    4. Zhaoxing Gao & Ruey S. Tsay, 2021. "Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data," Papers 2103.14626, arXiv.org.
    5. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
    6. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    7. João A. Bastos & Jorge Caiado, 2014. "Clustering financial time series with variance ratio statistics," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2121-2133, December.
    8. Xu Gao & Babak Shahbaba & Hernando Ombao, 2018. "Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 549-579, October.
    9. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
    10. Salles, Andre Assis de & Maria Eduarda, Silva & Paulo, Teles, 2022. "Empirical Evidence of Associations and Similarities between the National Equity Markets Indexes and Crude Oil Prices in the International Market," MPRA Paper 113589, University Library of Munich, Germany.
    11. Carolina Euán & Hernando Ombao & Joaquín Ortega, 2018. "The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 71-99, April.
    12. Jin, Lei, 2011. "A data-driven test to compare two or multiple time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2183-2196, June.
    13. Elizabeth Ann Maharaj & Pierpaolo D’Urso & Don Galagedera, 2010. "Wavelet-based Fuzzy Clustering of Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 231-275, September.
    14. Caiado, Jorge & Crato, Nuno, 2008. "Identifying the evolution of stock markets stochastic structure after the euro," MPRA Paper 6609, University Library of Munich, Germany.
    15. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    16. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Comparison of time series with unequal length," MPRA Paper 6605, University Library of Munich, Germany.
    17. Umberto Triacca, 2016. "Measuring the Distance between Sets of ARMA Models," Econometrics, MDPI, vol. 4(3), pages 1-11, July.
    18. Bertsch, Valentin & Devine, Mel & Sweeney, Conor & Parnell, Andrew C., 2018. "Analysing long-term interactions between demand response and different electricity markets using a stochastic market equilibrium model," Papers WP585, Economic and Social Research Institute (ESRI).
    19. Tyler Roick & Dimitris Karlis & Paul D. McNicholas, 2021. "Clustering discrete-valued time series," 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(1), pages 209-229, March.
    20. Harvill, Jane L. & Ravishanker, Nalini & Ray, Bonnie K., 2013. "Bispectral-based methods for clustering time series," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 113-131.
    21. Maharaj, Elizabeth Ann & D’Urso, Pierpaolo, 2010. "A coherence-based approach for the pattern recognition of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3516-3537.
    22. Alessandro De Gregorio & Stefano Iacus, 2008. "Clustering of discretely observed diffusion processes," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1077, Universitá degli Studi di Milano.
    23. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2009. "Comparison of time series with unequal length in the frequency domain," MPRA Paper 15310, University Library of Munich, Germany.
    24. Jentsch, Carsten & Pauly, Markus, 2012. "A note on using periodogram-based distances for comparing spectral densities," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 158-164.
    25. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    26. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
    27. Caiado, Jorge & Crato, Nuno, 2005. "Discrimination between deterministic trend and stochastic trend processes," MPRA Paper 2076, University Library of Munich, Germany.
    28. Caiado, Jorge & Crato, Nuno, 2007. "Identifying common spectral and asymmetric features in stock returns," MPRA Paper 6607, University Library of Munich, Germany.
    29. Vilar, J.A. & Alonso, A.M. & Vilar, J.M., 2010. "Non-linear time series clustering based on non-parametric forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2850-2865, November.
    30. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
    31. Carmela Iorio & Gianluca Frasso & Antonio D’Ambrosio & Roberta Siciliano, 2023. "Boosted-oriented probabilistic smoothing-spline clustering of series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1123-1140, October.
    32. Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Roohi, Reza, 2019. "A new method to compare the spectral densities of two independent periodically correlated time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 160(C), pages 103-110.
    33. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    34. E. Otranto, 2008. "Clustering Heteroskedastic Time Series by Model-Based Procedures," Working Paper CRENoS 200801, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    35. Caiado, Jorge & Crato, Nuno, 2009. "Identifying common dynamic features in stock returns," MPRA Paper 15241, University Library of Munich, Germany.
    36. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2006. "An interpolated periodogram-based metric for comparison of time series with unequal lengths," MPRA Paper 2075, University Library of Munich, Germany.
    37. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," 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. 5(4), pages 323-340, December.
    38. Irene Mariñas-Collado & Ana E. Sipols & M. Teresa Santos-Martín & Elisa Frutos-Bernal, 2022. "Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models," Mathematics, MDPI, vol. 10(15), pages 1-16, July.
    39. Robert Lund & Hany Bassily & Brani Vidakovic, 2009. "Testing equality of stationary autocovariances," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 332-348, May.
    40. E. Otranto, 2011. "Classification of Volatility in Presence of Changes in Model Parameters," Working Paper CRENoS 201113, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    41. Giulio PALOMBA & Emma SARNO & Alberto ZAZZARO, 2007. "Testing similarities of short-run inflation dynamics among EU countries after the Euro," Working Papers 289, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    42. Jorge Caiado & Nuno Crato & Pilar Poncela, 2020. "A fragmented-periodogram approach for clustering big data time series," 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(1), pages 117-146, March.
    43. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    44. Juan Vilar & José Vilar & Sonia Pértega, 2009. "Classifying Time Series Data: A Nonparametric Approach," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 3-28, April.
    45. Patrick Toman & Nalini Ravishanker & Sanguthevar Rajasekaran & Nathan Lally, 2023. "Online Evidential Nearest Neighbour Classification for Internet of Things Time Series," International Statistical Review, International Statistical Institute, vol. 91(3), pages 395-426, December.
    46. Margherita Gerolimetto & Stefano Magrini, 2022. "Weighting in clustering time series: an application to Covid-19 data," 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(4), pages 4-12, October-D.
    47. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
    48. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.
    49. Tianbo Chen & Ying Sun & Carolina Euan & Hernando Ombao, 2021. "Clustering Brain Signals: a Robust Approach Using Functional Data Ranking," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 425-442, October.
    50. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting," Complexity, Hindawi, vol. 2018, pages 1-21, April.
    51. Mahmoudi, Mohammad Reza, 2021. "A computational technique to classify several fractional Brownian motion processes," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    52. Douzal-Chouakria, Ahlame & Diallo, Alpha & Giroud, Françoise, 2009. "Adaptive clustering for time series: Application for identifying cell cycle expressed genes," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1414-1426, February.
    53. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    54. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Is there an identity within international stock market volatilities?," MPRA Paper 2069, University Library of Munich, Germany.
    55. Zhen Wang & Jicai Ning & Meng Gao, 2024. "Complex Network Model of Global Financial Time Series Based on Different Distance Functions," Mathematics, MDPI, vol. 12(14), pages 1-14, July.
    56. Dette, Holger & Paparoditis, Efstathios, 2008. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Technical Reports 2008,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    57. Heung-gu Son & Yunsun Kim & Sahm Kim, 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid," Energies, MDPI, vol. 13(9), pages 1-14, May.

  19. Pena, Daniel & Rodriguez, Julio, 2005. "Detecting nonlinearity in time series by model selection criteria," International Journal of Forecasting, Elsevier, vol. 21(4), pages 731-748.

    Cited by:

    1. Bertram, Philip & Sibbertsen, Philipp & Stahl, Gerhard, 2011. "About the Impact of Model Risk on Capital Reserves: A Quantitative Analysis," Hannover Economic Papers (HEP) dp-469, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Diego J Pedregal, 2019. "Time series analysis and forecasting with ECOTOOL," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-23, October.
    3. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    4. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Comparison of time series with unequal length," MPRA Paper 6605, University Library of Munich, Germany.
    5. Daniel Ventosa-Santaulària & Carlos Vladimir Rodríguez-Caballero, 2013. "Polynomial Regressions and Nonsense Inference," CREATES Research Papers 2013-40, Department of Economics and Business Economics, Aarhus University.
    6. O'Brien, Edward J., 2008. "A note on spurious nonlinear regression," Economics Letters, Elsevier, vol. 100(3), pages 366-368, September.
    7. Sánchez, Ismael, 2009. "Graphical identification of TAR models," DES - Working Papers. Statistics and Econometrics. WS ws097723, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Jean-Marc Le Caillec, 2022. "Hypothesis Testing Fusion for Nonlinearity Detection in Hedge Fund Price Returns," Post-Print hal-03739132, HAL.

  20. Daniel Pena & Ismael Sanchez, 2005. "Multifold Predictive Validation in ARMAX Time Series Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 135-146, March.

    Cited by:

    1. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    2. Klaus Abberger, 2005. "The Use of Qualitative Business Tendency Surveys for Forecasting Business Investment in Germany," ifo Working Paper Series No.13, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

  21. Galeano, Pedro & Peña, Daniel, 2005. "A note on prediction and interpolation errors in time series," Statistics & Probability Letters, Elsevier, vol. 73(1), pages 71-78, June.
    See citations under working paper version above.
  22. Pena, Daniel & Poncela, Pilar, 2004. "Forecasting with nonstationary dynamic factor models," Journal of Econometrics, Elsevier, vol. 119(2), pages 291-321, April.
    See citations under working paper version above.
  23. Alonso, Andrés M. & Peña, Daniel & Romo, Juan, 2003. "On sieve bootstrap prediction intervals," Statistics & Probability Letters, Elsevier, vol. 65(1), pages 13-20, October.

    Cited by:

    1. Juan Carlos Pérez-Velasco Pavón, 2009. "Determinantes de la demanda por la denominación promedio de billete: el caso de México," Monetaria, CEMLA, vol. 0(4), pages 523-548, octubre-d.
    2. Liew, Venus Khim-Sen, 2008. "An overview on various ways of bootstrap methods," MPRA Paper 7163, University Library of Munich, Germany.
    3. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
    4. Juan Díaz Maureira & Gustavo Leyva Jiménez, 2009. "Proyección de la inflación chilena en tiempos difíciles," Monetaria, CEMLA, vol. 0(4), pages 491-522, octubre-d.
    5. Alonso, Andres M. & Sipols, Ana E., 2008. "A time series bootstrap procedure for interpolation intervals," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1792-1805, January.
    6. Rupasinghe, Maduka & Samaranayake, V.A., 2012. "Asymptotic properties of sieve bootstrap prediction intervals for FARIMA processes," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2108-2114.
    7. Ricardo Gimeno & José Manuel Marqués-Sevillano, 2009. "Incertidumbre y el precio del riesgo en un proceso de convergencia nominal," Monetaria, CEMLA, vol. 0(4), pages 451-489, octubre-d.
    8. Andrés Alonso & Ana Sipols & Silvia Quintas, 2013. "A single-index model procedure for interpolation intervals in time series," Computational Statistics, Springer, vol. 28(4), pages 1463-1484, August.
    9. Andrés Schneider, 2009. "Regímenes de flotación administrada: un enfoque de cartera," Monetaria, CEMLA, vol. 0(4), pages 549-584, octubre-d.
    10. Mahmoudi, Mohammad Reza, 2021. "A computational technique to classify several fractional Brownian motion processes," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    11. Bu Hyoung Lee, 2022. "Bootstrap Prediction Intervals of Temporal Disaggregation," Stats, MDPI, vol. 5(1), pages 1-13, February.

  24. Andrés Alonso & Daniel Peña & Juan Romo, 2003. "Resampling time series using missing values techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 765-796, December.

    Cited by:

    1. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.

  25. Peña, Daniel & Rodríguez, Julio, 2003. "Descriptive measures of multivariate scatter and linear dependence," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 361-374, May.
    See citations under working paper version above.
  26. Pena D. & Rodriguez J., 2002. "A Powerful Portmanteau Test of Lack of Fit for Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 601-610, June.
    See citations under working paper version above.
  27. Justel, Ana & Pena, Daniel, 2001. "Bayesian unmasking in linear models," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 69-84, March.
    See citations under working paper version above.
  28. Pena D. & Prieto F.J., 2001. "Cluster Identification Using Projections," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1433-1445, December.

    Cited by:

    1. Nicola Loperfido, 2010. "Canonical transformations of skew-normal variates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 146-165, May.
    2. Loperfido, Nicola, 2015. "Vector-valued skewness for model-based clustering," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 230-237.
    3. Alashwali, Fatimah & Kent, John T., 2016. "The use of a common location measure in the invariant coordinate selection and projection pursuit," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 145-161.
    4. Loperfido, Nicola, 2014. "A note on the fourth cumulant of a finite mixture distribution," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 386-394.
    5. Jorge M. Arevalillo & Hilario Navarro, 2021. "Skewness-Kurtosis Model-Based Projection Pursuit with Application to Summarizing Gene Expression Data," Mathematics, MDPI, vol. 9(9), pages 1-18, April.
    6. Galeano, Pedro & Tsay, Ruey S., 2004. "Outlier detection in multivariate time series via projection pursuit," DES - Working Papers. Statistics and Econometrics. WS ws044211, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Arthur Pewsey & Eduardo García-Portugués, 2021. "Rejoinder on: Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 76-82, March.
    8. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," 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. 7(2), pages 125-145, June.
    9. Gomes-Gonçalves, Erika & Gzyl, Henryk & Mayoral, Silvia, 2015. "Maxentropic approach to decompound aggregate risk losses," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 326-336.
    10. Loperfido, Nicola, 2021. "Some theoretical properties of two kurtosis matrices, with application to invariant coordinate selection," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    11. Loperfido, Nicola, 2013. "Skewness and the linear discriminant function," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 93-99.
    12. Javed, Farrukh & Loperfido, Nicola & Mazur, Stepan, 2024. "The Method of Moments for Multivariate Random Sums," Working Papers 2024:6, Örebro University, School of Business.
    13. Nicola Loperfido, 2019. "Finite mixtures, projection pursuit and tensor rank: a triangulation," 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 145-173, March.
    14. Peña, Daniel & Prieto, Francisco J. & Viladomat, Júlia, 2010. "Eigenvectors of a kurtosis matrix as interesting directions to reveal cluster structure," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 1995-2007, October.
    15. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    16. Álvarez, Adolfo, 2009. "Recombining dependent data: an Order Statistics," DES - Working Papers. Statistics and Econometrics. WS ws098526, Universidad Carlos III de Madrid. Departamento de Estadística.

  29. Ismael Sanchez & Daniel Pena, 2001. "Properties of Predictors in Overdifferenced Nearly Nonstationary Autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(1), pages 45-66, January.
    See citations under working paper version above.
  30. Peña, Daniel & Prieto, Francisco J., 2000. "The kurtosis coefficient and the linear discriminant function," Statistics & Probability Letters, Elsevier, vol. 49(3), pages 257-261, September.
    See citations under working paper version above.
  31. Juan Gil & Daniel Peña & Julio Rodríguez, 2000. "Statistical research in Europe: 1985–1997," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 9(1), pages 255-281, June.
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  32. N. Locantore & J. Marron & D. Simpson & N. Tripoli & J. Zhang & K. Cohen & Graciela Boente & Ricardo Fraiman & Babette Brumback & Christophe Croux & Jianqing Fan & Alois Kneip & John Marden & Daniel P, 1999. "Robust principal component analysis for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(1), pages 1-73, June.

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    1. Maronna, Ricardo A. & Yohai, Victor J., 2017. "Robust and efficient estimation of multivariate scatter and location," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 64-75.
    2. Boente, Graciela & Rodriguez, Daniela & Sued, Mariela, 2019. "The spatial sign covariance operator: Asymptotic results and applications," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 115-128.
    3. Mingxiang Cao & Ziyang Cheng & Kai Xu & Daojiang He, 2024. "A scale-invariant test for linear hypothesis of means in high dimensions," Statistical Papers, Springer, vol. 65(6), pages 3477-3497, August.
    4. Xu, Yangchang & Xia, Ningning, 2023. "On the eigenvectors of large-dimensional sample spatial sign covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    5. Raymaekers, Jakob & Rousseeuw, Peter, 2019. "A generalized spatial sign covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 94-111.
    6. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    7. Zhang, Jin-Ting & Zhu, Tianming, 2022. "A new normal reference test for linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    8. Dürre, Alexander & Vogel, Daniel, 2016. "Asymptotics of the two-stage spatial sign correlation," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 54-67.
    9. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
    10. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    11. Majumdar, Subhabrata & Chatterjee, Snigdhansu, 2022. "On weighted multivariate sign functions," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    12. Debruyne, Michiel & Hubert, Mia & Van Horebeek, Johan, 2010. "Detecting influential observations in Kernel PCA," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3007-3019, December.
    13. Italo R. Lima & Guanqun Cao & Nedret Billor, 2019. "Robust simultaneous inference for the mean function of functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 785-803, September.
    14. Michael Greenacre & Patrick J. F Groenen & Trevor Hastie & Alfonso Iodice d’Enza & Angelos Markos & Elena Tuzhilina, 2023. "Principal component analysis," Economics Working Papers 1856, Department of Economics and Business, Universitat Pompeu Fabra.
    15. Hervé Cardot & Antoine Godichon-Baggioni, 2017. "Fast estimation of the median covariation matrix with application to online robust principal components analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 461-480, September.
    16. Bali, Juan Lucas & Boente, Graciela, 2014. "Consistency of a numerical approximation to the first principal component projection pursuit estimator," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 181-191.
    17. Boente, Graciela & Parada, Daniela, 2023. "Robust estimation for functional quadratic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    18. Christian Acal & Manuel Escabias & Ana M. Aguilera & Mariano J. Valderrama, 2021. "COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    19. Graciela Boente & Matías Salibian-Barrera, 2015. "S -Estimators for Functional Principal Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1100-1111, September.
    20. Tyler, David E., 2010. "A note on multivariate location and scatter statistics for sparse data sets," Statistics & Probability Letters, Elsevier, vol. 80(17-18), pages 1409-1413, September.
    21. Cevallos-Valdiviezo, Holger & Van Aelst, Stefan, 2019. "Fast computation of robust subspace estimators," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 171-185.
    22. Taskinen, Sara & Koch, Inge & Oja, Hannu, 2012. "Robustifying principal component analysis with spatial sign vectors," Statistics & Probability Letters, Elsevier, vol. 82(4), pages 765-774.
    23. Guangxing Wang & Sisheng Liu & Fang Han & Chong‐Zhi Di, 2023. "Robust functional principal component analysis via a functional pairwise spatial sign operator," Biometrics, The International Biometric Society, vol. 79(2), pages 1239-1253, June.
    24. Pallavi Sawant & Nedret Billor & Hyejin Shin, 2012. "Functional outlier detection with robust functional principal component analysis," Computational Statistics, Springer, vol. 27(1), pages 83-102, March.
    25. Bali, Juan Lucas & Boente, Graciela, 2017. "Robust estimators under a functional common principal components model," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 424-440.
    26. Graciela Boente & Matías Salibián-Barrera, 2021. "Robust functional principal components for sparse longitudinal data," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 159-188, August.
    27. Sudaraka Tholkage & Qi Zheng & Karunarathna B. Kulasekera, 2022. "Conditional Kaplan–Meier Estimator with Functional Covariates for Time-to-Event Data," Stats, MDPI, vol. 5(4), pages 1-17, November.
    28. Gao, Yuan & Shang, Han Lin & Yang, Yanrong, 2019. "High-dimensional functional time series forecasting: An application to age-specific mortality rates," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 232-243.
    29. Paula R. Bouzas & Ana M. Aguilera & Nuria Ruiz-Fuentes, 2012. "Functional Estimation of the Random Rate of a Cox Process," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 57-69, March.
    30. Fraiman, Ricardo & Pateiro-López, Beatriz, 2012. "Quantiles for finite and infinite dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 108(C), pages 1-14.
    31. Kondylis, Athanassios & Hadi, Ali S., 2006. "Derived components regression using the BACON algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 556-569, November.
    32. Dürre, Alexander & Tyler, David E. & Vogel, Daniel, 2016. "On the eigenvalues of the spatial sign covariance matrix in more than two dimensions," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 80-85.
    33. Pesonen, Maiju & Pesonen, Henri & Nevalainen, Jaakko, 2015. "Covariance matrix estimation for left-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 13-25.
    34. B. Barış Alkan, 2016. "Robust Principal Component Analysis Based on Modified Minimum Covariance Determinant in the Presence of Outliers," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 85-94, September.
    35. Berrendero, J.R. & Justel, A. & Svarc, M., 2011. "Principal components for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2619-2634, September.
    36. Italo R. Lima & Guanqun Cao & Nedret Billor, 2019. "M-based simultaneous inference for the mean function of functional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 577-598, June.
    37. Luca Greco & Alessio Farcomeni, 2016. "A plug-in approach to sparse and robust principal component analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 449-481, September.
    38. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    39. Croux, C. & Dehon, C. & Yadine, A., 2010. "The K-Step Spatial Sign Covariance Matrix," Other publications TiSEM b3c069e5-3f34-475a-9c1b-1, Tilburg University, School of Economics and Management.
    40. Ricardo A. Maronna, 2021. "Robust functional principal components for irregularly spaced longitudinal data," Statistical Papers, Springer, vol. 62(4), pages 1563-1582, August.
    41. Zhong, Rou & Liu, Shishi & Li, Haocheng & Zhang, Jingxiao, 2022. "Robust functional principal component analysis for non-Gaussian longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    42. Christoph Hellmayr & Alan E. Gelfand, 2021. "A Partition Dirichlet Process Model for Functional Data Analysis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 30-65, May.
    43. Lovato, Ilenia & Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2020. "Model-free two-sample test for network-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    44. Christian Acal & Ana M. Aguilera & Manuel Escabias, 2020. "New Modeling Approaches Based on Varimax Rotation of Functional Principal Components," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    45. Croux, Christophe & Ruiz-Gazen, Anne, 2005. "High breakdown estimators for principal components: the projection-pursuit approach revisited," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 206-226, July.
    46. Martínez-Hernández, Israel & Genton, Marc G. & González-Farías, Graciela, 2019. "Robust depth-based estimation of the functional autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 66-79.
    47. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).
    48. Seija Sirkiä & Sara Taskinen & Hannu Oja & David Tyler, 2009. "Tests and estimates of shape based on spatial signs and ranks," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(2), pages 155-176.
    49. Dürre, Alexander & Vogel, Daniel & Fried, Roland, 2015. "Spatial sign correlation," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 89-105.
    50. Bali, Juan Lucas & Boente, Graciela, 2015. "Influence function of projection-pursuit principal components for functional data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 173-199.
    51. Lee, Seokho & Shin, Hyejin & Billor, Nedret, 2013. "M-type smoothing spline estimators for principal functions," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 89-100.
    52. Marc Vidal & Mattia Rosso & Ana M. Aguilera, 2021. "Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    53. Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008. "Outlier identification in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1694-1711, January.
    54. Boente, Graciela & Salibián Barrera, Matías & Tyler, David E., 2014. "A characterization of elliptical distributions and some optimality properties of principal components for functional data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 254-264.
    55. Erkuş, Ekin Can & Purutçuoğlu, Vilda, 2021. "Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)," European Journal of Operational Research, Elsevier, vol. 291(2), pages 560-574.
    56. Cuesta-Albertos, Juan Antonio & Fraiman, Ricardo, 2007. "Impartial trimmed k-means for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4864-4877, June.
    57. Dürre, Alexander & Vogel, Daniel & Tyler, David E., 2014. "The spatial sign covariance matrix with unknown location," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 107-117.
    58. Michiel Debruyne & Tim Verdonck, 2010. "Robust kernel principal component analysis and classification," 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. 4(2), pages 151-167, September.
    59. J. L. Scealy & Patrice de Caritat & Eric C. Grunsky & Michail T. Tsagris & A. H. Welsh, 2015. "Robust Principal Component Analysis for Power Transformed Compositional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 136-148, March.
    60. Heredia, María Belén & Prieur, Clémentine & Eckert, Nicolas, 2021. "Nonparametric estimation of aggregated Sobol’ indices: Application to a depth averaged snow avalanche model," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

  33. Gomez, Victor & Maravall, Agustin & Pena, Daniel, 1998. "Missing observations in ARIMA models: Skipping approach versus additive outlier approach," Journal of Econometrics, Elsevier, vol. 88(2), pages 341-363, November.

    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. Carlos Carrillo-Tudela & Ludo Visschers, 2020. "Unemployment and Endogenous Reallocation over the Business Cycle," CESifo Working Paper Series 8288, CESifo.
    3. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
    4. Brunhes-Lesage, V. & Darné, O., 2008. "Why calculate a business sentiment indicator for services?," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 13, pages 21-30, Autumn.
    5. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Working Papers 0112, Banco de España.
    6. Andy Lee & John Yick & Yer Van Hui, 2001. "Sensitivity of the portmanteau statistic in time series modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 691-702.
    7. Alonso, Andres M. & Sipols, Ana E., 2008. "A time series bootstrap procedure for interpolation intervals," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1792-1805, January.
    8. Shigeru Fujita & Christopher J. Nekarda & Garey Ramey, 2007. "The cyclicality of worker flows: new evidence from the SIPP," Working Papers 07-5, Federal Reserve Bank of Philadelphia.
    9. Zudi Lu & Y. Hui, 2003. "L 1 linear interpolator for missing values in time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(1), pages 197-216, March.
    10. Maravall, Agustin, 2006. "An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2167-2190, May.
    11. Andrés Alonso & Ana Sipols & Silvia Quintas, 2013. "A single-index model procedure for interpolation intervals in time series," Computational Statistics, Springer, vol. 28(4), pages 1463-1484, August.

  34. Pena, Daniel & Ruiz-Castillo, Javier, 1998. "The Estimation of Food Expenditures from Household Budget Data in the Presence of Bulk Purchases," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 292-303, July.

    Cited by:

    1. Valero-Gil, Jorge, 2008. "Remittances and the household’s expenditures on health," MPRA Paper 9572, University Library of Munich, Germany.

  35. Justel, Ana & Peña, Daniel & Zamar, Rubén, 1997. "A multivariate Kolmogorov-Smirnov test of goodness of fit," Statistics & Probability Letters, Elsevier, vol. 35(3), pages 251-259, October.
    See citations under working paper version above.
  36. Peña, Daniel & Zamar, Ruben, 1997. "A simple diagnostic tool for local prior sensitivity," Statistics & Probability Letters, Elsevier, vol. 36(2), pages 205-212, December.
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  37. Alvaro Escribano & Daniel Peña, 1994. "Cointegration And Common Factors," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(6), pages 577-586, November.

    Cited by:

    1. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.
    2. Pascual, Roberto, 2000. "Dynamic asymmetries in bid-ask responses to innovations in the trading process," UC3M Working papers. Economics 7271, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Focardi, Sergio M. & Fabozzi, Frank J. & Mitov, Ivan K., 2016. "A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 134-155.
    4. Albacete, Rebeca, 2004. "Econometric modelling for short-term inflation forecasting in the EMU," DES - Working Papers. Statistics and Econometrics. WS ws034309, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).
    6. Joakim Westerlund & Simon Reese & Paresh Narayan, 2017. "A Factor Analytical Approach to Price Discovery," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(3), pages 366-394, June.
    7. Croux, Christophe & Renault, Eric & Werker, Bas, 2004. "Dynamic factor models," Journal of Econometrics, Elsevier, vol. 119(2), pages 223-230, April.
    8. Tu, Yundong & Yao, Qiwei & Zhang, Rongmao, 2020. "Error-correction factor models for high-dimensional cointegrated time series," LSE Research Online Documents on Economics 106994, London School of Economics and Political Science, LSE Library.
    9. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    10. Arino, Miguel A. & Newbold, Paul, 1998. "Computation of the Beveridge-Nelson decomposition for multivariate economic time series," Economics Letters, Elsevier, vol. 61(1), pages 37-42, October.
    11. Poncela, Pilar, 2000. "Forecasting with nostationary dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 9959, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    13. Poncela, Pilar & Senra, Eva, 2002. "Forecasting monthly us consumer price indexes through a disaggregated I(2) analysis," DES - Working Papers. Statistics and Econometrics. WS ws020301, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Huh, Hyeon-seung & Kim, David, 2013. "An empirical test of exogenous versus endogenous growth models for the G-7 countries," Economic Modelling, Elsevier, vol. 32(C), pages 262-272.
    15. Flad, Michael & Jung, Robert C., 2008. "A common factor analysis for the US and the German stock markets during overlapping trading hours," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(5), pages 498-512, December.
    16. Hiroaki Chigira & Taku Yamamoto, 2006. "Forcasting in large cointegrated processes," Hi-Stat Discussion Paper Series d06-169, Institute of Economic Research, Hitotsubashi University.
    17. Hugo Oliveros & Carlos Huertas, 2002. "Desequilibrios Nominales y Reales del Tipo de Cambio en Colombia," Borradores de Economia 220, Banco de la Republica de Colombia.
    18. Katalin Varga & Tibor Szendrei, 2024. "Non-stationary Financial Risk Factors and Macroeconomic Vulnerability for the UK," Papers 2404.01451, arXiv.org.
    19. Poncela, Pilar, 1996. "Pooling information and forecasting with dynamic factor analysis," DES - Working Papers. Statistics and Econometrics. WS 10709, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021. "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 863-882, December.
    21. Carlomagno, Guillermo, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    22. Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.
    23. Helena Chuliá & Montserrat Guillén & Jorge M. Uribe, 2015. "Mortality and Longevity Risks in the United Kingdom: Dynamic Factor Models and Copula-Functions," Working Papers 2015-03, Universitat de Barcelona, UB Riskcenter.
    24. Pham Van Ha & Tom Kompas, 2008. "Productivity and Exchange Rate Dynamics: Supporting the Harrod-Balassa-Samuelson Hypothesis through an ‘Errors in Variables’ Analysis," International and Development Economics Working Papers idec08-03, International and Development Economics.
    25. Poncela, Pilar, 1997. "Eigenstructure of nonstationary factor models," DES - Working Papers. Statistics and Econometrics. WS 6224, Universidad Carlos III de Madrid. Departamento de Estadística.
    26. Martínez, Wilmer & Nieto, Fabio H. & Poncela, Pilar, 2016. "Choosing a dynamic common factor as a coincident index," Statistics & Probability Letters, Elsevier, vol. 109(C), pages 89-98.
    27. Galeano, Pedro, 2001. "Multivariate analysis in vector time series," DES - Working Papers. Statistics and Econometrics. WS ws012415, Universidad Carlos III de Madrid. Departamento de Estadística.

  38. Joseph Kadane & Javier Girón & Daniel Peña & Peter Fishburn & Simon French & D. Lindley & Giovanni Parmigiani & Robert Winkler, 1993. "Several Bayesians: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 2(1), pages 1-32, December.

    Cited by:

    1. R. Winkler & Javier Muñoz & José Cervera & José Bernardo & Gail Blattenberger & Joseph Kadane & Dennis Lindley & Allan Murphy & Robert Oliver & David Ríos-Insua, 1996. "Scoring rules and the evaluation of probabilities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(1), pages 1-60, June.
    2. Insua, Insua Rios & Rios, Jesus & Banks, David, 2009. "Adversarial Risk Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 841-854.

  39. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.

    Cited by:

    1. Gómez, Víctor & Maravall, Agustín, 1993. "Computing missing values in time series," DES - Working Papers. Statistics and Econometrics. WS 3737, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Sánchez, María Jesús, 1997. "The identification of multiple outliers in arima models," DES - Working Papers. Statistics and Econometrics. WS 6220, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," Working Papers hal-04141344, HAL.
    4. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
    5. Andrés Alonso & Daniel Peña & Juan Romo, 2003. "Resampling time series using missing values techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 765-796, December.
    6. Agustín Maravall & Daniel Peña, 1996. "Missing Observations and Additive Outliers in Time Series Models," Working Papers 9612, Banco de España.
    7. Jørgen Lauridsen & Jesus Mur, 2004. "Outliers in Cross-Sectional Regression," ERSA conference papers ersa04p27, European Regional Science Association.
    8. 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.
    9. Francisco JA Cysneiros, 2018. "Symmetric Regression Model for Temporal Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(2), pages 44-45, February.
    10. Justel, A. & Tsay, Ruey S., 1998. "Detection of outlier patches in autoregressive time series," DES - Working Papers. Statistics and Econometrics. WS 9821, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.
    12. Vinicius Q. S. Maior & Francisco José A. Cysneiros, 2018. "SYMARMA: a new dynamic model for temporal data on conditional symmetric distribution," Statistical Papers, Springer, vol. 59(1), pages 75-97, March.
    13. González-Sánchez, Mariano, 2021. "Is there a relationship between the time scaling property of asset returns and the outliers? Evidence from international financial markets," Finance Research Letters, Elsevier, vol. 38(C).
    14. Hamid Louni, 2008. "Outlier detection in ARMA models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 1057-1065, November.
    15. Sánchez, Ismael, 2001. "New in-sample prediction errors in time series with applications," DES - Working Papers. Statistics and Econometrics. WS ws011107, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Victor M. Guerrero & Daniel Peña, 1995. "Linear Combination of Information in Time Series Analysis," Working Papers 9507, Centro de Investigacion Economica, ITAM.
    17. Justel, Ana & Sánchez, María Jesús, 1994. "Grupos atípicos en modelos econométricos," DES - Documentos de Trabajo. Estadística y Econometría. DS 10755, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Baragona, Roberto & Battaglia, Francesco & Calzini, Claudio, 2001. "Genetic algorithms for the identification of additive and innovation outliers in time series," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 1-12, July.
    19. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    20. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.
    21. Bauer, Marcus & Gather, Ursula & Imhoff, Michael, 1999. "The identification of multiple outliers in online monitoring data," Technical Reports 1999,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    22. Min-Hsien Chiang & Ray Yeutien Chou & Li-Min Wang, 2016. "Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 126-144, February.

  40. Daniel Peña, 1987. "Observaciones influyentes en modelos econométricos," Investigaciones Economicas, Fundación SEPI, vol. 11(1), pages 3-24, January.

    Cited by:

    1. Gregorio R. Serrano García, 1994. "Estadísticos para la detección de observaciones anómalas en modelos de elección binaria: una aplicación con datos reales," Documentos de Trabajo del ICAE 9403, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.

  41. Pena, Daniel & Ruiz-Castillo, Javier, 1984. "Robust Methods of Building Regression Models-An Application to the Housing Sector," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 10-20, January.

    Cited by:

    1. Noureddine Kouaissah & Sergio Ortobelli Lozza & Ikram Jebabli, 2022. "Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 833-859, October.
    2. Justel, Ana & Sánchez, María Jesús, 1994. "Grupos atípicos en modelos econométricos," DES - Documentos de Trabajo. Estadística y Econometría. DS 10755, Universidad Carlos III de Madrid. Departamento de Estadística.

  42. Pena, Daniel & Ruiz-Castillo, Javier, 1984. "Distributional aspects of public rental housing and rent control policies in Spain," Journal of Urban Economics, Elsevier, vol. 15(3), pages 350-370, May.

    Cited by:

    1. Kholodilin, Konstantin A., 2024. "Rent control effects through the lens of empirical research: An almost complete review of the literature," Journal of Housing Economics, Elsevier, vol. 63(C).
    2. Weber, Jan Philip & Lee, Gabriel, . "On the Measure of Private Rental Market Regulation Index and its Effect on Housing Rents: Cross Country Evidence," Beiträge zur Immobilienwirtschaft, University of Regensburg, Department of Economics, number 21, August.
    3. Juan S. Mora-Sanguinetti, 2011. "The Regulation of Residential Tenancy Markets in Post-War Western Europe: An Economic Analysis," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 8(1), pages 47-75, June.
    4. Raquel Arévalo-Tomé, 1999. "Construcción de un índice de calidad de la vivienda," Investigaciones Economicas, Fundación SEPI, vol. 23(2), pages 267-280, May.
    5. Ballesteros, Marife M., 2001. "Benefits (and Losses) From Rent Control in the Philippines: An Empirical Study of Metro Manila," Discussion Papers DP 2001-23, Philippine Institute for Development Studies.
    6. David L. Mengle, 1985. "The effect of second generation rent controls on the quality of rental housing," Working Paper 85-05, Federal Reserve Bank of Richmond.
    7. Konstantin A. Kholodilin, 2022. "Rent Control Effects through the Lens of Empirical Research: An almost Complete Review of the Literature," Discussion Papers of DIW Berlin 2026, DIW Berlin, German Institute for Economic Research.
    8. Paolo Buonanno & Daniel Montolio & Josep Raya-Vílchez, 2013. "Housing prices and crime perception," Empirical Economics, Springer, vol. 45(1), pages 305-321, August.
    9. Mora, Juan S., 2008. "The institutions of house tenancy markets in post-war Western Europe : an economic analysis," IFCS - Working Papers in Economic History.WH wp08-11, Universidad Carlos III de Madrid. Instituto Figuerola.
    10. Paul Asabere, 2007. "The Demise of the Rent-controlled Public Housing Programmes of Ghana: The Story of the Tema Development Corporation," Urban Studies, Urban Studies Journal Limited, vol. 44(10), pages 1919-1935, September.

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