Nonparametric Identification of Dynamic Decision Processes with Discrete and Continuous Choices

Jason R. Blevins
Quantitative Economics 5 (2014), 531–554.

Availability:

• Published version (DOI: 10.3982/QE117)
• Preprint version (November 21, 2013)

Abstract. This paper establishes conditions for nonparametric identification of dynamic optimization models in which agents make both discrete and continuous choices. We consider identification of both the payoff function and the distribution of unobservables. Models of this kind are prevalent in applied microeconomics and many of the required conditions are standard assumptions currently used in empirical work. We focus on conditions on the model that can be implied by economic theory and assumptions about the data generating process that are likely to be satisfied in a typical application. Our analysis is intended to highlight the identifying power of each assumption individually, where possible, and our proofs are constructive in nature.

Keywords: nonparametric identification, Markov decision processes, dynamic decision processes, discrete choice, continuous choice.

JEL Classification: C14, C25, C23, C51.

BibTeX Record:

@Article{blevins-2014,
  author       = {Jason R. Blevins},
  title        = {Nonparametric Identification of Dynamic Decision
                  Processes with Discrete and Continuous Choices},
  year         = 2014,
  journal      = {Quantitative Economics},
  volume       = 5,
  pages        = {531--554}
}