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Heterogeneity and Microeconometrics Modelling

Author

Listed:
  • Martin Browning

    (Department of Economics, University of Copenhagen)

  • Jesus Carro

    (Department of Economics, Carlos III, Madrid)

Abstract

Presented at the 2005 Econometric Society World Congress Plenary Session on "Modelling Heterogeneity". We survey the treatment of heterogeneity in applied microeconometrics analyses. There are three themes. First, there is usually much more heterogeneity than empirical researchers allow for. Second, the inappropriate treatment of heterogeneity can lead to serious error when estimating outcomes of interest. Finally, once we move away from the traditional linear model with a single 'fixed effect', it is very difficult to account for heterogeneity and fit the data and maintain coherence with theory structures. The latter task is one for economists: "heterogeneity is too important to be left to the statisticians". The paper concludes with a report of our own research on dynamic discrete choice models that allow for maximal heterogeneity.

Suggested Citation

  • Martin Browning & Jesus Carro, 2006. "Heterogeneity and Microeconometrics Modelling," CAM Working Papers 2006-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
  • Handle: RePEc:kud:kuieca:2006_03
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    File URL: http://www.econ.ku.dk/cam/wp0910/wp0406/2006-03.pdf/
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    References listed on IDEAS

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    1. 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.
    2. Flavio Cunha & James Heckman & Salvador Navarro, 2005. "Separating uncertainty from heterogeneity in life cycle earnings," Oxford Economic Papers, Oxford University Press, vol. 57(2), pages 191-261, April.
    3. Martin Browning & Mette Ejrnæs & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," Review of Economic Studies, Oxford University Press, vol. 77(4), pages 1353-1381.
    4. Martin Browning & Jesus M. Carro, 2010. "Heterogeneity in dynamic discrete choice models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 1-39, February.
    5. Ivan Fernandez-Val, 2005. "Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects," Boston University - Department of Economics - Working Papers Series WP2005-38, Boston University - Department of Economics.
    6. James J. Heckman & Rosa Matzkin & Lars Nesheim, 2003. "Simulation and Estimation of Nonaddative Hedonic Models," NBER Working Papers 9895, National Bureau of Economic Research, Inc.
    7. 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.
    8. Costas Meghir & Luigi Pistaferri, 2004. "Income Variance Dynamics and Heterogeneity," Econometrica, Econometric Society, vol. 72(1), pages 1-32, January.
    9. Carro, Jesus M., 2007. "Estimating dynamic panel data discrete choice models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 503-528, October.
    10. Tiemen Woutersen, 2002. "Robustness against Incidental Parameters," UWO Department of Economics Working Papers 20028, University of Western Ontario, Department of Economics.
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    Citations

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    Cited by:

    1. Browning, Martin & Carro, Jesus M., 2014. "Dynamic binary outcome models with maximal heterogeneity," Journal of Econometrics, Elsevier, vol. 178(2), pages 805-823.
    2. Fischer, Gregory & Berry, James & Guiteras, Raymond, 2012. "Eliciting and utilizing willingness to pay: evidence from field trials in Northern Ghana," LSE Research Online Documents on Economics 47913, London School of Economics and Political Science, LSE Library.
    3. Ralph Stinebrickner & Todd R. Stinebrickner, 2014. "A Major in Science? Initial Beliefs and Final Outcomes for College Major and Dropout," Review of Economic Studies, Oxford University Press, vol. 81(1), pages 426-472.
    4. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," Review of Economic Studies, Oxford University Press, vol. 82(3), pages 991-1030.
    5. Peter C. B. Phillips & Donggyu Sul, 2007. "Transition Modeling and Econometric Convergence Tests," Econometrica, Econometric Society, vol. 75(6), pages 1771-1855, November.
    6. Hiroyuki Kasahara & Katsumi Shimotsu, 2006. "Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices," Working Papers 1092, Queen's University, Department of Economics.
    7. repec:ucp:jpolec:doi:10.1086/692808 is not listed on IDEAS
    8. Arthur Lewbel & Krishna Pendakur, 2017. "Unobserved Preference Heterogeneity in Demand Using Generalized Random Coefficients," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1100-1148.
    9. M. Dolores Collado & Martin Browning, 2007. "Habits and heterogeneity in demands: a panel data analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 625-640.
    10. Santiago Pereda Fernández, 2016. "Copula-based random effects models for clustered data," Temi di discussione (Economic working papers) 1092, Bank of Italy, Economic Research and International Relations Area.
    11. Chernozhukov, Victor & Fernández-Val, Iván & Hoderlein, Stefan & Holzmann, Hajo & Newey, Whitney, 2015. "Nonparametric identification in panels using quantiles," Journal of Econometrics, Elsevier, vol. 188(2), pages 378-392.
    12. Hochguertel, Stefan & Ohlsson, Henry, 2011. "Wealth mobility and dynamics over entire individual working life cycles," Working Paper Series 1301, European Central Bank.
    13. 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.
    14. Bryan S. Graham & James Powell, 2008. "Identification and Estimation of 'Irregular' Correlated Random Coefficient Models," NBER Working Papers 14469, National Bureau of Economic Research, Inc.
    15. Lu, Xun & White, Halbert, 2014. "Testing for separability in structural equations," Journal of Econometrics, Elsevier, vol. 182(1), pages 14-26.
    16. Stéphane Bonhomme & Elena Manresa, 2012. "Grouped Patterns of Heterogeneity in Panel Data," Working Papers wp2012_1208, CEMFI.
    17. Ryo Okui & Wendun Wang, 2018. "Heterogeneous structural breaks in panel data models," Papers 1801.04672, arXiv.org.
    18. Mendis, Sachintha & Hovhannisyan, Vardges, 2017. "Assessing Provincial-Level Demand For Food Quantity And Quality In China: An Easi Demand System Approach," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252797, Southern Agricultural Economics Association.
    19. Lin Chang-Ching & Ng Serena, 2012. "Estimation of Panel Data Models with Parameter Heterogeneity when Group Membership is Unknown," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 1-14, August.
    20. Helmers, Christian & Patnam, Manasa, 2011. "The formation and evolution of childhood skill acquisition: Evidence from India," Journal of Development Economics, Elsevier, vol. 95(2), pages 252-266, July.

    More about this item

    Keywords

    heterogeneity; applied microeconometrics; fixed effects; dyanamic discrete choice;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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