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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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    2. Jackson Bunting, 2022. "Continuous permanent unobserved heterogeneity in dynamic discrete choice models," Papers 2202.03960, arXiv.org, revised Feb 2024.
    3. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza‐Rodrigues, 2021. "Identification of counterfactuals in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 12(2), pages 351-403, May.
    4. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    5. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    6. Natalia Khorunzhina & Robert A. Miller, 2022. "2021 Klein Lecture: American Dream Delayed: Shifting Determinants Of Homeownership," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(1), pages 3-35, February.
    7. Jaap H. Abbring & Øystein Daljord, 2020. "Identifying the discount factor in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 11(2), pages 471-501, May.
    8. Knapp, David & Hosek, James & Mattock, Michael G. & Asch, Beth J., 2023. "Predicting teacher retention behavior: Ex ante prediction and ex post realization of a voluntary retirement incentive offer," Economics of Education Review, Elsevier, vol. 93(C).
    9. Joseph Mullins, 2022. "Designing Cash Transfers in the Presence of Children's Human Capital Formation," Working Papers 2022-019, Human Capital and Economic Opportunity Working Group.
    10. Schneider, Ulrich, 2019. "Identification of Time Preferences in Dynamic Discrete Choice Models: Exploiting Choice Restrictions," MPRA Paper 102137, University Library of Munich, Germany, revised 29 Jul 2020.
    11. Knapp, David & Lopez Garcia, Italo & Kumar, Krishna & Lee, Jinkook & Won, Jongwook, 2021. "A dynamic behavioral model of Korean saving, work, and benefit claiming decisions," The Journal of the Economics of Ageing, Elsevier, vol. 20(C).

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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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