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Dynamic binary outcome models with maximal heterogeneity


  • Browning, Martin
  • Carro, Jesús M.


Most econometric schemes to allow for heterogeneity in micro behaviour have two drawbacks: they do not fit the data and they rule out interesting economic models. In this paper we consider the time homogeneous first order Markov (HFOM) model that allows for maximal heterogeneity. That is, the modelling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an individual observable variable). `Maximal' means that the joint distribution of initial values and the transition probabilities is unrestricted. We establish necessary and sufficient conditions for the point identification of our heterogeneity structure and show how it depends on the length of the panel. A feasible ML estimation procedure is developed. Tests for a variety of subsidiary hypotheses such as the assumption that marginal dynamic effects are homogeneous are developed. We apply our techniques to a long panel of Danish workers who are very homogeneous in terms of observables. We show that individual unemployment dynamics are very heterogeneous, even for such a homogeneous group. We also show that the impact of cyclical variables on individual unemployment probabilities differs widely across workers. Some workers have unemployment dynamics that are independent of the cycle whereas others are highly sensitive to macro shocks.

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  • Browning, Martin & Carro, Jesús M., 2009. "Dynamic binary outcome models with maximal heterogeneity," UC3M Working papers. Economics we091710, Universidad Carlos III de Madrid. Departamento de Economía.
  • Handle: RePEc:cte:werepe:we091710

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    1. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
    2. Pedro Mira & Jesús M. Carro, 2006. "A dynamic model of contraceptive choice of Spanish couples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 955-980.
    3. Peter Arcidiacono & John Bailey Jones, 2003. "Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm," Econometrica, Econometric Society, vol. 71(3), pages 933-946, May.
    4. Joseph G. Altonji & Rosa L. Matzkin, 2001. "Panel Data Estimators for Nonseparable Models with Endogenous Regressors," NBER Technical Working Papers 0267, National Bureau of Economic Research, Inc.
    5. Martin Browning & Jesus M. Carro, 2013. "The Identification of a Mixture of First-Order Binary Markov Chains," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 455-459, June.
    6. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 487-535.
    7. Bo E. Honoré & Elie Tamer, 2006. "Bounds on Parameters in Panel Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 74(3), pages 611-629, May.
    8. Martin Browning & Jesus M. Carro, 2010. "Heterogeneity in dynamic discrete choice models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 1-39, February.
    9. Rob Alessie & Stefan Hochguertel & Arthur van Soest, 2004. "Ownership of Stocks and Mutual Funds: A Panel Data Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 783-796, August.
    10. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    11. Dean R. Hyslop, 1999. "State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women," Econometrica, Econometric Society, vol. 67(6), pages 1255-1294, November.
    12. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    13. Victor Chernozhukov & Ivan Fernandez-Val & Jinyong Hahn & Whitney K. Newey, 2008. "Identification and estimation of marginal effects in nonlinear panel models," CeMMAP working papers CWP25/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Ham, John C. & Shore-Sheppard, Lara, 2005. "The effect of Medicaid expansions for low-income children on Medicaid participation and private insurance coverage: evidence from the SIPP," Journal of Public Economics, Elsevier, vol. 89(1), pages 57-83, January.
    15. Becker, Gary S & Grossman, Michael & Murphy, Kevin M, 1994. "An Empirical Analysis of Cigarette Addiction," American Economic Review, American Economic Association, vol. 84(3), pages 396-418, June.
    16. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
    17. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
    18. Andrew B. Bernard & J. Bradford Jensen, 2004. "Why Some Firms Export," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 561-569, May.
    19. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    20. Patrick Bajari & Jeremy T. Fox & Kyoo il Kim & Stephen Ryan, 2007. "A Simple Nonparametric Estimator for the Distribution of Random Coefficients in Discrete Choice Models," Working Papers 36, Portuguese Competition Authority.
    21. Gottschalk, Peter & Moffitt, Robert A, 1994. "Welfare Dependence: Concepts, Measures, and Trends," American Economic Review, American Economic Association, vol. 84(2), pages 38-42, May.
    22. Eric Gautier & Yuichi Kitamura, 2013. "Nonparametric Estimation in Random Coefficients Binary Choice Models," Econometrica, Econometric Society, vol. 81(2), pages 581-607, March.
    23. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296 Elsevier.
    24. Hiroyuki Kasahara & Katsumi Shimotsu, 2009. "Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices," Econometrica, Econometric Society, vol. 77(1), pages 135-175, January.
    25. Martin Browning & Jesus Carro, 2006. "Heterogeneity and Microeconometrics Modelling," CAM Working Papers 2006-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    26. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters,in: Studies in Labor Markets, pages 91-140 National Bureau of Economic Research, Inc.
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    Cited by:

    1. Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 471-496, September.
    2. Plum, Alexander & Ayllón, Sara, 2015. "Heterogeneity in unemployment state dependence," Economics Letters, Elsevier, vol. 136(C), pages 85-87.
    3. Martin Browning & Jesus M. Carro, 2013. "The Identification of a Mixture of First-Order Binary Markov Chains," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 455-459, June.
    4. Bester, C. Alan & Hansen, Christian B., 2016. "Grouped effects estimators in fixed effects models," Journal of Econometrics, Elsevier, vol. 190(1), pages 197-208.
    5. Stéphane Bonhomme & Elena Manresa, 2012. "Grouped Patterns of Heterogeneity in Panel Data," Working Papers wp2012_1208, CEMFI.

    More about this item


    Discrete choice;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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