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A Monte Carlo Study of Ec-Estimation in Panel Data Models with Limited Dependent Variables and Heterogeneity

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  • El-Gamal, M.A.

Abstract

The EC (Estimation-Classification) estimator, and its companion EC-algorithm, were introduced in El- Gamal and Grether (1995), and their properties further analyzed in El-Gamal and Grether (1996). The purpose of EC estimation is to uncover heterogeneity in panel data models in a manner which is more parsimonious and computationally less costly than some of the standard methods (e.g. fixed effects). The latter concern is particularly evident in limited dependent variable models where no simple method of estimating fixed effects is available (e.g. probits).

Suggested Citation

  • El-Gamal, M.A., 1997. "A Monte Carlo Study of Ec-Estimation in Panel Data Models with Limited Dependent Variables and Heterogeneity," Working papers 9703, Wisconsin Madison - Social Systems.
  • Handle: RePEc:att:wimass:9703
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    File URL: http://www.ssc.wisc.edu/econ/archive/wp9703.pdf
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    References listed on IDEAS

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    1. El-Gamal, Mahmoud A. & Grether, David M., 1995. "Are People Bayesian? Uncovering Behavioral Strategies," Working Papers 919, California Institute of Technology, Division of the Humanities and Social Sciences.
    2. Pesaran, H. & Smith, R. & Im, K.S., 1995. "Dynamic Linear Models for Heterogeneous Panels," Cambridge Working Papers in Economics 9503, Faculty of Economics, University of Cambridge.
    3. El-Gamal, M. & Grether, D.M., 1996. "Unknown Heterogeneity, the EC-EM Algorithm, and Large T Approximation," Working papers 9622, Wisconsin Madison - Social Systems.
    4. James J. Heckman & Thomas E. Macurdy, 1980. "A Life Cycle Model of Female Labour Supply," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 47-74.
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    Keywords

    EVALUATION ; ECONOMIC MODELS;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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