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Closed-Form Identification Of Dynamic Discrete Choice Models With Proxies For Unobserved State Variables

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  • Hu, Yingyao
  • Sasaki, Yuya

Abstract

Proxies for unobserved skills and technologies are increasingly available in empirical data. For dynamic discrete choice models of forward-looking agents where a continuous state variable is unobserved but its proxy is available, we derive closed-form identification of the structure by explicitly solving integral equations. In the first step, we derive closed-form identification of Markov components, including the conditional choice probabilities and the law of state transition. In the second step, we plug in these first-step identifying formulas to obtain primitive structural parameters of dynamically optimizing agents.

Suggested Citation

  • Hu, Yingyao & Sasaki, Yuya, 2018. "Closed-Form Identification Of Dynamic Discrete Choice Models With Proxies For Unobserved State Variables," Econometric Theory, Cambridge University Press, vol. 34(1), pages 166-185, February.
    • Handle: RePEc:cup:etheor:v:34:y:2018:i:01:p:166-185_00
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    Cited by:

    1. 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.
    2. Sasaki, Yuya & Takahashi, Yuya & Xin, Yi & Hu, Yingyao, 2023. "Dynamic discrete choice models with incomplete data: Sharp identification," Journal of Econometrics, Elsevier, vol. 236(1).

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