Euler Equations for the Estimation of Dynamic Discrete Choice Structural Models

Victor Aguirregabiria (victor.aguirregabiria@utoronto.ca) and Arvind Magesan

Working Papers from University of Toronto, Department of Economics

Abstract: We derive marginal conditions of optimality (i.e., Euler equations) for a general class of Dynamic Discrete Choice (DDC) structural models. These conditions can be used to estimate structural parameters in these models without having to solve for or approximate value functions. This result extends to discrete choice models the GMM-Euler equation approach proposed by Hansen and Singleton (1982) for the estimation of dynamic continuous decision models. We first show that DDC models can be represented as models of continuous choice where the decision variable is a vector of choice probabilities. We then prove that the marginal conditions of optimality and the envelope conditions required to construct Euler equations are also satisfied in DDC models. The GMM estimation of these Euler equations avoids the curse of dimensionality associated to the computation of value functions and the explicit integration over the space of state variables. We present an empirical application and compare estimates using the GMM-Euler equations method with those from maximum likelihood and two-step methods.

Keywords: Dynamic discrete choice structural models; Euler equations; Choice probabilities. (search for similar items in EconPapers)
JEL-codes: C13 C25 C51 C61 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2013-06-02
New Economics Papers: this item is included in nep-dcm
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Citations: View citations in EconPapers (13)

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Chapter: Euler Equations for the Estimation of Dynamic Discrete Choice Structural Models (2013)
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