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Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation

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  • Bruneel-Zupanc, Christophe Alain

Abstract

This paper develops a general framework for models, static or dynamic, in which agents simultaneously make both discrete and continuous choices. I show that such models are nonparametrically identified. Based on the constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) but extended to discrete and continuous choice models. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation. To illustrate my new method, I estimate a dynamic model of female labor supply and consumption.

Suggested Citation

  • Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
  • Handle: RePEc:tse:wpaper:125232
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    References listed on IDEAS

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