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Nonstationary dynamic models with finite dependence

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  • Peter Arcidiacono
  • Robert A. Miller

Abstract

The estimation of nonstationary dynamic discrete choice models typically requires making assumptions far beyond the length of the data. We extend the class of dynamic discrete choice models that require only a few‐period‐ahead conditional choice probabilities, and develop algorithms to calculate the finite dependence paths. We do this both in single agent and games settings, resulting in expressions for the value functions that allow for much weaker assumptions regarding the time horizon and the transitions of the state variables beyond the sample period.

Suggested Citation

  • Peter Arcidiacono & Robert A. Miller, 2019. "Nonstationary dynamic models with finite dependence," Quantitative Economics, Econometric Society, vol. 10(3), pages 853-890, July.
    • Handle: RePEc:wly:quante:v:10:y:2019:i:3:p:853-890
    DOI: 10.3982/QE626
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2020. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," Working Papers tecipa-674, University of Toronto, Department of Economics.
    5. Khorunzhina, Natalia & Miller, Robert, 2019. "American dream delayed: shifting determinants of homeownership," MPRA Paper 94832, University Library of Munich, Germany.
    6. Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
    7. 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.

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