IDEAS home Printed from https://ideas.repec.org/e/pbu134.html
   My authors  Follow this author

Martin Burda

Personal Details

First Name:Martin
Middle Name:
Last Name:Burda
Suffix:
RePEc Short-ID:pbu134
http://www.economics.utoronto.ca/mburda
Department of Economics University of Toronto 150 St. George St., 234 Toronto, ON M5S 3G7, Canada
416-978-4479
Terminal Degree:2007 Department of Economics; University of Pittsburgh (from RePEc Genealogy)

Affiliation

(95%) Department of Economics
University of Toronto

Toronto, Canada
http://www.economics.utoronto.ca/
RePEc:edi:deutoca (more details at EDIRC)

(5%) Institut ekonomických studií
Univerzita Karlova v Praze

Praha, Czech Republic
http://ies.fsv.cuni.cz/
RePEc:edi:icunicz (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Martin Burda & Artem Prokhorov, 2013. "Copula Based Factorization in Bayesian Multivariate Infinite Mixture Models," Working Papers tecipa-473, University of Toronto, Department of Economics.
  2. Martin Burda & John M. Maheu, 2012. "Bayesian Adaptively Updated Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models," Working Paper series 46_12, Rimini Centre for Economic Analysis.
  3. Martin Burda & John Maheu, 2011. "Bayesian Adaptive Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models," Working Papers tecipa-438, University of Toronto, Department of Economics.
  4. Martin Burda & Matthew C. Harding & Jerry Hausman, 2008. "A Bayesian mixed logit-probit model for multinomial choice," CeMMAP working papers CWP23/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Martin Burda & Roman Liesenfeld & Jean-Francois Richard, 2008. "Bayesian Analysis of a Probit Panel Data Model with Unobserved Individual Heterogeneity and Autocorrelated Errors," Working Papers tecipa-321, University of Toronto, Department of Economics.

Articles

  1. Burda Martin, 2015. "Constrained Hamiltonian Monte Carlo in BEKK GARCH with Targeting," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 95-113, January.
  2. Martin Burda & Matthew Harding & Jerry Hausman, 2015. "A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 353-376, April.
  3. Burda, Martin & Harding, Matthew, 2014. "Environmental Justice: Evidence from Superfund cleanup durations," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 380-401.
  4. Burda, Martin & Prokhorov, Artem, 2014. "Copula based factorization in Bayesian multivariate infinite mixture models," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 200-213.
  5. Burda Martin & Maheu John M., 2013. "Bayesian adaptively updated Hamiltonian Monte Carlo with an application to high-dimensional BEKK GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 345-372, September.
  6. Martin Burda & Matthew Harding, 2013. "Panel Probit With Flexible Correlated Effects: Quantifying Technology Spillovers In The Presence Of Latent Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 956-981, September.
  7. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
  8. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.

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. Martin Burda & Artem Prokhorov, 2013. "Copula Based Factorization in Bayesian Multivariate Infinite Mixture Models," Working Papers tecipa-473, University of Toronto, Department of Economics.

    Cited by:

    1. Mark J Jensen & John M Maheu, 2012. "Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture," Working Papers tecipa-453, University of Toronto, Department of Economics.
    2. Lu Lu & Sujit Ghosh, 2023. "Nonparametric Estimation of Multivariate Copula Using Empirical Bayes Methods," Mathematics, MDPI, vol. 11(20), pages 1-22, October.
    3. Eddie Anderson & Artem Prokhorov & Yajing Zhu, 2020. "A Simple Estimator of Two‐Dimensional Copulas, with Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1375-1412, December.
    4. Norets, Andriy, 2015. "Bayesian regression with nonparametric heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 409-419.
    5. Dellaportas, Petros & Tsionas, Mike G., 2019. "Importance sampling from posterior distributions using copula-like approximations," Journal of Econometrics, Elsevier, vol. 210(1), pages 45-57.

  2. Martin Burda & John M. Maheu, 2012. "Bayesian Adaptively Updated Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models," Working Paper series 46_12, Rimini Centre for Economic Analysis.

    Cited by:

    1. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    2. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    3. Agudze, Komla M. & Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco, 2022. "Markov switching panel with endogenous synchronization effects," Journal of Econometrics, Elsevier, vol. 230(2), pages 281-298.
    4. Burda Martin, 2015. "Constrained Hamiltonian Monte Carlo in BEKK GARCH with Targeting," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 95-113, January.

  3. Martin Burda & Matthew C. Harding & Jerry Hausman, 2008. "A Bayesian mixed logit-probit model for multinomial choice," CeMMAP working papers CWP23/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Vij, Akshay & Krueger, Rico, 2017. "Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 76-101.
    2. Schröder, Nadine & Hruschka, Harald, 2016. "Investigating the effects of mailing variables and endogeneity on mailing decisions," European Journal of Operational Research, Elsevier, vol. 250(2), pages 579-589.
    3. Wan, Alan T.K. & Zhang, Xinyu & Wang, Shouyang, 2014. "Frequentist model averaging for multinomial and ordered logit models," International Journal of Forecasting, Elsevier, vol. 30(1), pages 118-128.
    4. Worawan Chandoevwit & Nada Wasi, 2019. "Estimating Demand for Long-term Care Insurance in Thailand: Evidence from a Discrete Choice Experiment," PIER Discussion Papers 106, Puey Ungphakorn Institute for Economic Research.
    5. Dubois, Pierre & Griffith, Rachel & O'Connell, Martin, 2022. "The Use of Scanner Data for Economics Research," TSE Working Papers 22-1286, Toulouse School of Economics (TSE).
    6. Florian Heiss & Stephan Hetzenecker & Maximilian Osterhaus, 2019. "Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach," Papers 1909.08434, arXiv.org, revised Sep 2019.
    7. Dubois, Pierre & Griffith, Rachel & O'Connell, Martin, 2017. "How well targeted are soda taxes?," CEPR Discussion Papers 12484, C.E.P.R. Discussion Papers.
    8. Steven Berry & Philip Haile, 2023. "Nonparametric Identification of Differentiated Products Demand Using Micro Data," Cowles Foundation Discussion Papers 2357, Cowles Foundation for Research in Economics, Yale University.
    9. Mark J. Jensen & John M. Maheu, 2014. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," Working Paper series 31_14, Rimini Centre for Economic Analysis.
    10. E. Weyl & Michal Fabinger, 2015. "A Tractable Approach to Pass-Through Patterns," 2015 Meeting Papers 747, Society for Economic Dynamics.
    11. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    12. Akshay Vij & Rico Krueger, 2018. "Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions," Papers 1802.02299, arXiv.org.
    13. Bansal, Prateek & Daziano, Ricardo A. & Achtnicht, Martin, 2018. "Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models," Journal of choice modelling, Elsevier, vol. 27(C), pages 97-113.
    14. Bel, K. & Paap, R., 2014. "A Multivariate Model for Multinomial Choices," Econometric Institute Research Papers EI 2014-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. Apps, Patricia & Kabátek, Jan & Rees, Ray & van Soest, Arthur, 2012. "Labor Supply Heterogeneity and Demand for Child Care of Mothers with Young Children," IZA Discussion Papers 7007, Institute of Labor Economics (IZA).
    16. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," NBER Working Papers 29305, National Bureau of Economic Research, Inc.
    17. Michael P. Keane, 2013. "Panel data discrete choice models of consumer demand," Economics Papers 2013-W08, Economics Group, Nuffield College, University of Oxford.
    18. Steven T. Berry & Philip A. Haile, 2009. "Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers," Cowles Foundation Discussion Papers 1718, Cowles Foundation for Research in Economics, Yale University, revised Mar 2010.
    19. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2017. "Nonseparable multinomial choice models in cross-section and panel data," CeMMAP working papers 33/17, Institute for Fiscal Studies.
    20. Yang Li & Asim Ansari, 2014. "A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models," Management Science, INFORMS, vol. 60(5), pages 1161-1179, May.
    21. Kabátek, Jan, 2015. "Essays on public policy and household decision making," Other publications TiSEM 8cdb178e-ad98-42e5-a7e1-b, Tilburg University, School of Economics and Management.
    22. Didier Nibbering, 2019. "A High-dimensional Multinomial Choice Model," Monash Econometrics and Business Statistics Working Papers 19/19, Monash University, Department of Econometrics and Business Statistics.
    23. Claudia Bazzani & Marco A. Palma & Rodolfo M. Nayga, 2018. "On the use of flexible mixing distributions in WTP space: an induced value choice experiment," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(2), pages 185-198, April.
    24. Daziano, Ricardo A., 2013. "Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range," Resource and Energy Economics, Elsevier, vol. 35(3), pages 429-450.
    25. Jerry Hausman & Haoyang Liu & Ye Luo & Christopher Palmer, 2021. "Errors in the Dependent Variable of Quantile Regression Models," Econometrica, Econometric Society, vol. 89(2), pages 849-873, March.
    26. Cohen, Michael, 2010. "A Structured Covariance Probit Demand Model," Research Reports 149970, University of Connecticut, Food Marketing Policy Center.
    27. Igor Prünster & Matteo Ruggiero, 2011. "A Bayesian nonparametric approach to modeling market share dynamics," Carlo Alberto Notebooks 217, Collegio Carlo Alberto.
    28. Keane, Michael P. & Wasi, Nada, 2016. "How to model consumer heterogeneity? Lessons from three case studies on SP and RP data," Research in Economics, Elsevier, vol. 70(2), pages 197-231.
    29. Balcombe, Kelvin & Fraser, Iain & Williams, Louis & McSorley, Eugene, 2017. "Examining the relationship between visual attention and stated preferences: A discrete choice experiment using eye-tracking," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 238-257.
    30. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    31. Victor Chernozhukov & Jerry A. Hausman & Whitney K. Newey, 2019. "Demand Analysis with Many Prices," NBER Working Papers 26424, National Bureau of Economic Research, Inc.
    32. Rico Krueger & Taha H. Rashidi & Akshay Vij, 2019. "Semi-Parametric Hierarchical Bayes Estimates of New Yorkers' Willingness to Pay for Features of Shared Automated Vehicle Services," Papers 1907.09639, arXiv.org.
    33. Kenneth L. Judd & Ben Skrainka, 2011. "High performance quadrature rules: how numerical integration affects a popular model of product differentiation," CeMMAP working papers CWP03/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. David Throsby & Anita Zednik & Jorge E. Araña, 2021. "Public preferences for heritage conservation strategies: a choice modelling approach," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(3), pages 333-358, September.
    35. Matthew Harding & Ephraim Leibtag & Michael F. Lovenheim, 2012. "The Heterogeneous Geographic and Socioeconomic Incidence of Cigarette Taxes: Evidence from Nielsen Homescan Data," American Economic Journal: Economic Policy, American Economic Association, vol. 4(4), pages 169-198, November.
    36. Rico Krueger & Akshay Vij & Taha H. Rashidi, 2018. "A Dirichlet Process Mixture Model of Discrete Choice," Papers 1801.06296, arXiv.org.
    37. Train, Kenneth, 2016. "Mixed logit with a flexible mixing distribution," Journal of choice modelling, Elsevier, vol. 19(C), pages 40-53.
    38. Martin Burda & Remi Daviet, 2023. "Hamiltonian sequential Monte Carlo with application to consumer choice behavior," Econometric Reviews, Taylor & Francis Journals, vol. 42(1), pages 54-77, January.
    39. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    40. Jinyong Hahn & Jerry Hausman & Josh Lustig, 2017. "Specification test on mixed logit models," CeMMAP working papers 58/17, Institute for Fiscal Studies.
    41. Martin Burda & Artem Prokhorov, 2012. "Copula Based Factorization in Bayesian Multivariate Infinite Mixture Models," Working Papers 12012, Concordia University, Department of Economics.
    42. Karabatsos, George & Walker, Stephen G., 2012. "Bayesian nonparametric mixed random utility models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1714-1722.
    43. Peter Lenk, 2014. "Bayesian estimation of random utility models," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 20, pages 457-497, Edward Elgar Publishing.
    44. Griffith, Rachel & O'Connell, Martin & Miller, Helen, 2011. "Corporate taxes and the location of intellectual property," CEPR Discussion Papers 8424, C.E.P.R. Discussion Papers.
    45. Norets, Andriy & Pelenis, Justinas, 2022. "Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity," Journal of Econometrics, Elsevier, vol. 230(1), pages 62-82.
    46. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    47. Nada Wasi & Michael P. Keane, 2012. "Estimation of Discrete Choice Models with Many Alternatives Using Random Subsets of the Full Choice Set: With an Application to Demand for Frozen Pizza," Economics Papers 2012-W13, Economics Group, Nuffield College, University of Oxford.
    48. Michael P. Keane & Nada Wasi, 2013. "The Structure of Consumer Taste Heterogeneity in Revealed vs. Stated Preference Data," Economics Papers 2013-W10, Economics Group, Nuffield College, University of Oxford.
    49. I. G. Ukpong & K. G. Balcombe & I. M. Fraser & F. J. Areal, 2019. "Preferences for Mitigation of the Negative Impacts of the Oil and Gas Industry in the Niger Delta Region of Nigeria," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(2), pages 811-843, October.
    50. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
    51. Patrick Bajari & Jeremy T. Fox & Kyoo il Kim & Stephen P. Ryan, 2009. "A Simple Nonparametric Estimator for the Distribution of Random Coefficients," NBER Working Papers 15210, National Bureau of Economic Research, Inc.
    52. Matzkin, Rosa L., 2019. "Constructive identification in some nonseparable discrete choice models," Journal of Econometrics, Elsevier, vol. 211(1), pages 83-103.
    53. Heiss, Florian & Hetzenecker, Stephan & Osterhaus, Maximilian, 2019. "Nonparametric estimation of the random coefficients model: An elastic net approach," Ruhr Economic Papers 824, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    54. Christopher Dobronyi & Christian Gouri'eroux, 2020. "Consumer Theory with Non-Parametric Taste Uncertainty and Individual Heterogeneity," Papers 2010.13937, arXiv.org, revised Jan 2021.
    55. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.

  4. Martin Burda & Roman Liesenfeld & Jean-Francois Richard, 2008. "Bayesian Analysis of a Probit Panel Data Model with Unobserved Individual Heterogeneity and Autocorrelated Errors," Working Papers tecipa-321, University of Toronto, Department of Economics.

    Cited by:

    1. Bekierman Jeremias & Gribisch Bastian, 2016. "Estimating stochastic volatility models using realized measures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 279-300, June.

Articles

  1. Burda Martin, 2015. "Constrained Hamiltonian Monte Carlo in BEKK GARCH with Targeting," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 95-113, January.

    Cited by:

    1. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    2. Agudze, Komla M. & Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco, 2022. "Markov switching panel with endogenous synchronization effects," Journal of Econometrics, Elsevier, vol. 230(2), pages 281-298.
    3. Xiaoning Kang & Xinwei Deng & Kam‐Wah Tsui & Mohsen Pourahmadi, 2020. "On variable ordination of modified Cholesky decomposition for estimating time‐varying covariance matrices," International Statistical Review, International Statistical Institute, vol. 88(3), pages 616-641, December.
    4. William Bednar & Nick Pretnar, 2019. "Home Production with Time to Consume," 2019 Meeting Papers 328, Society for Economic Dynamics.

  2. Martin Burda & Matthew Harding & Jerry Hausman, 2015. "A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 353-376, April.

    Cited by:

    1. Burda, Martin & Harding, Matthew, 2014. "Environmental Justice: Evidence from Superfund cleanup durations," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 380-401.
    2. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2018. "Bayesian Nonparametric Calibration and Combination of Predictive Distributions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 675-685, April.
    3. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    4. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2017. "Modeling heaped duration data: An application to neonatal mortality," Journal of Econometrics, Elsevier, vol. 200(2), pages 363-377.
    5. Lo, Simon M.S. & Mammen, Enno & Wilke, Ralf A., 2020. "A nested copula duration model for competing risks with multiple spells," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

  3. Burda, Martin & Harding, Matthew, 2014. "Environmental Justice: Evidence from Superfund cleanup durations," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 380-401.

    Cited by:

    1. Anna Aizer & Janet Currie & Peter Simon & Patrick Vivier, 2016. "Do Low Levels of Blood Lead Reduce Children's Future Test Scores?," NBER Working Papers 22558, National Bureau of Economic Research, Inc.
    2. Jacob LaRiviere & Matthew McMahon & Justin Roush, 2019. "Second-Best Prioritization of Environmental Cleanups," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(4), pages 1225-1249, April.
    3. Marcus, Michelle & Mueller, Rosie, 2024. "Unregulated contaminants in drinking water: Evidence from PFAS and housing prices," Journal of Environmental Economics and Management, Elsevier, vol. 125(C).
    4. Roberto Pasetto & Benedetta Mattioli & Daniela Marsili, 2019. "Environmental Justice in Industrially Contaminated Sites. A Review of Scientific Evidence in the WHO European Region," IJERPH, MDPI, vol. 16(6), pages 1-20, March.
    5. Phattraporn Soytong & Ranjith Perera, 2017. "Spatial analysis of the environmental conflict between state, society and industry at the Map Ta Phut-Rayong conurbation in Thailand," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(3), pages 839-862, June.
    6. Bez, Charlotte S. & Ash, Michael & Boyce, James K., 2024. "Environmental inequality in industrial brownfields: Evidence from French municipalities," Ecological Economics, Elsevier, vol. 217(C).
    7. Shannon M. Cruz, 2019. "Themes Across New Directions in Community Engagement," IJERPH, MDPI, vol. 16(19), pages 1-13, October.
    8. Ivan Rudik & Derek Lemoine & Antonia Marcheva, 2024. "Equity and Efficiency in the Bipartisan Infrastructure Law’s Adaptation Investments," NBER Chapters, in: Environmental and Energy Policy and the Economy, volume 6, National Bureau of Economic Research, Inc.

  4. Burda, Martin & Prokhorov, Artem, 2014. "Copula based factorization in Bayesian multivariate infinite mixture models," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 200-213.
    See citations under working paper version above.
  5. Burda Martin & Maheu John M., 2013. "Bayesian adaptively updated Hamiltonian Monte Carlo with an application to high-dimensional BEKK GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 345-372, September.
    See citations under working paper version above.
  6. Martin Burda & Matthew Harding, 2013. "Panel Probit With Flexible Correlated Effects: Quantifying Technology Spillovers In The Presence Of Latent Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 956-981, September.

    Cited by:

    1. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2019. "Forecasting with a Panel Tobit Model," CAEPR Working Papers 2019-005, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. Laura Liu, 2020. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," CAEPR Working Papers 2020-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    3. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Papers 1909.05560, arXiv.org.
    4. Bresson, Georges & Lacroix, Guy & Arshad Rahman, Mohammad, 2020. "Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada," IZA Discussion Papers 12928, Institute of Labor Economics (IZA).
    5. Cheng Hsiao & Yimeng Xie & Qiankun Zhou, 2021. "Factor dimension determination for panel interactive effects models: an orthogonal projection approach," Computational Statistics, Springer, vol. 36(2), pages 1481-1497, June.
    6. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.

  7. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.

    Cited by:

    1. Matthew Harding & Michael Lovenheim, 2014. "The Effect of Prices on Nutrition: Comparing the Impact of Product- and Nutrient-Specific Taxes," NBER Working Papers 19781, National Bureau of Economic Research, Inc.
    2. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
    3. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Incorporating Search and Sales Information in Demand Estimation," Cowles Foundation Discussion Papers 2313R1, Cowles Foundation for Research in Economics, Yale University, revised Mar 2023.
    4. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2017. "Nonseparable multinomial choice models in cross-section and panel data," CeMMAP working papers 33/17, Institute for Fiscal Studies.
    5. Victor Chernozhukov & Jerry A. Hausman & Whitney K. Newey, 2019. "Demand Analysis with Many Prices," NBER Working Papers 26424, National Bureau of Economic Research, Inc.
    6. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Incorporating Search and Sales Information in Demand Estimation," Cowles Foundation Discussion Papers 2313, Cowles Foundation for Research in Economics, Yale University.
    7. Murat K. Munkin, 2022. "Count Roy model with finite mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1160-1181, September.
    8. Marques, Filipe J. & Loingeville, Florence, 2016. "Improved near-exact distributions for the product of independent Generalized Gamma random variables," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 55-66.
    9. Chen, Roger B., 2018. "Models of count with endogenous choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 862-875.
    10. Matzkin, Rosa L., 2019. "Constructive identification in some nonseparable discrete choice models," Journal of Econometrics, Elsevier, vol. 211(1), pages 83-103.
    11. Sfeir, Georges & Abou-Zeid, Maya & Kaysi, Isam, 2020. "Multivariate count data models for adoption of new transport modes in an organization-based context," Transport Policy, Elsevier, vol. 91(C), pages 59-75.
    12. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636, arXiv.org, revised Sep 2019.

  8. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (5) 2008-06-27 2009-04-05 2011-07-13 2012-07-08 2012-12-22. Author is listed
  2. NEP-ETS: Econometric Time Series (2) 2011-07-13 2012-07-08
  3. NEP-ORE: Operations Research (2) 2011-07-13 2012-07-08
  4. NEP-DCM: Discrete Choice Models (1) 2009-04-05

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Martin Burda should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.