Model-Adaptive Approach to Dynamic Discrete Choice Models with Large State Spaces

Ertian Chen

Papers from arXiv.org

Abstract: Estimating dynamic discrete choice models with large state spaces poses computational difficulties. This paper develops a novel model-adaptive approach to solve the linear system of fixed point equations of the policy valuation operator. We propose a model-adaptive sieve space, constructed by iteratively augmenting the space with the residual from the previous iteration. We show both theoretically and numerically that model-adaptive sieves dramatically improve performance. In particular, the approximation error decays at a superlinear rate in the sieve dimension, unlike a linear rate achieved using conventional methods. Our method works for both conditional choice probability estimators and full-solution estimators with policy iteration. We apply the method to analyze consumer demand for laundry detergent using Kantar's Worldpanel Take Home data. On average, our method is 51.5% faster than the conventional methods in solving the dynamic programming problem, making the Bayesian MCMC estimator computationally feasible. The results confirm the computational efficiency of our method in practice.

Date: 2025-01
New Economics Papers: this item is included in nep-dcm and nep-ecm
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