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Another Look at the Identification of Dynamic Discrete Decision Processes

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  • Victor Aguirregabiria

    (Boston University)

Abstract

This paper presents an econometric approach to estimate the behavioral effects of counterfactual policy experiments in the context of dynamic decision models where the current utility function and the distribution of unobservables are nonparametrically specified. Previous studies have shown that the identification of the current utility function in dynamic decision models requires of stronger assumptions than in static decision models. We show in this paper that knowledge of the current utility function (or of a 'normalized' utility function) is not necessary to identify counterfactual choice probabilities in dynamic models. To identify these counterfactuals we need the probability distribution of the unobservables and the difference between the present value of choosing always the same alternative and the present value of deviating one period from this strategy. We show that both functions are identified from the factual choice probabilities under similar conditions as in static decision models. Based on this result we propose a nonparametric procedure to estimate the behavioral effects of counterfactual experiments in dynamic decision models. We apply this method to evaluate the effects of an investment subsidy program in the context of a model of machine replacement.

Suggested Citation

  • Victor Aguirregabiria, 2005. "Another Look at the Identification of Dynamic Discrete Decision Processes," Econometrics 0504006, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpem:0504006
    Note: Type of Document - pdf; pages: 41
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0504/0504006.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Le-Yu Chen, 2009. "Identification of structural dynamic discrete choice models," CeMMAP working papers CWP08/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. George-Levi Gayle & Limor Golan, "undated". "Estimating a Dynamic Adverse Selection Model: Labor Force Experience and the Changing Gender Earnings Gap 1968-93," GSIA Working Papers 2006-E40, Carnegie Mellon University, Tepper School of Business.
    4. 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.
    5. Xun Tang, 2009. "Rationalizable Counterfactual Choice Probabilities in Dynamic Binary Choice Processes," PIER Working Paper Archive 09-022, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    6. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    7. Patrick Bajari & Han Hong, 2006. "Semiparametric Estimation of a Dynamic Game of Incomplete Information," NBER Technical Working Papers 0320, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Dynamic discrete decision processes; Nonparametric Identification; Counterfactual experiments.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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