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Identifying dynamic discrete choice models off short panels

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  • Arcidiacono, Peter
  • Miller, Robert A.

Abstract

This paper analyzes the identification of flow payoffs and counterfactual choice probabilities (CCPs) in single-agent dynamic discrete choice models. We develop new results on non-stationary models where the time horizon for the agent extends beyond the length of the data (short panels). We show that counterfactual CCPs in short panels are identified when induced by temporary policy changes affecting payoffs, even though the utility flows are not. Counterfactual CCPs induced by innovations to state transitions are generally not identified unless the model exhibits single action finite dependence, and the payoffs of those actions establishing single action finite dependence are known.

Suggested Citation

  • Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
  • Handle: RePEc:eee:econom:v:215:y:2020:i:2:p:473-485
    DOI: 10.1016/j.jeconom.2018.12.025
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    More about this item

    Keywords

    Dynamic discrete choice; Identification; Conditional choice probabilities; Nonstationary models;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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