DP11300 Solution and Estimation of Dynamic Discrete Choice Structural Models Using Euler Equations
|Author(s):||Victor Aguirregabiria, Arvind Magesan|
|Publication Date:||May 2016|
|Keyword(s):||Approximation bias, Dynamic programming discrete choice models, Estimation, Euler equations, Policy iteration|
|JEL(s):||C13, C35, C51, C61|
|Programme Areas:||Industrial Organization|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=11300|
This paper extends the Euler Equation (EE) representation of dynamic decision problems to a general class of discrete choice models and shows that the advantages of this approach apply not only to the estimation of structural parameters but also to the computation of a solution and to the evaluation of counterfactual experiments. We use a choice probabilities representation of the discrete decision problem to derive marginal conditions of optimality with the same features as the standard EEs in continuous decision problems. These EEs imply a fixed point mapping in the space of conditional choice values, that we denote the Euler equation-value (EE-value) operator. We show that, in contrast to Euler equation operators in continuous decision models, this operator is a contraction. We present numerical examples that illustrate how solving the model by iterating in the EE-value mapping implies substantial computational savings relative to iterating in the Bellman equation (that requires a much larger number of iterations) or in the policy function (that involves a costly valuation step). We define a sample version of the EE-value operator and use it to construct a sequence of consistent estimators of the structural parameters, and to evaluate counterfactual experiments. The computational cost of evaluating this sample-based EE-value operator increases linearly with sample size, and provides an unbiased (in finite samples) and consistent estimator the counterfactual. As such there is no curse of dimensionality in the consistent estimation of the model and in the evaluation of counterfactual experiments. We illustrate the computational gains of our methods using several Monte Carlo experiments.