Solving dynamic discrete choice models using smoothing and sieve methods

Dennis Kristensen, Patrick K. Mogensen, Jong Myun Moon and Bertel Schjerning

Journal of Econometrics, 2021, vol. 223, issue 2, 328-360

Abstract: We propose to combine smoothing, simulations and sieve approximations to solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models. We use importance sampling to approximate the Bellman operators defining the two functions. The random Bellman operators, and therefore also the corresponding solutions, are generally non-smooth which is undesirable. To circumvent this issue, we introduce smoothed versions of the random Bellman operators and solve for the corresponding smoothed value functions using sieve methods. We also show that one can avoid using sieves by generalizing and adapting the “self-approximating” method of Rust (1997b) to our setting. We provide an asymptotic theory for both approximate solution methods and show that they converge with N-rate, where N is number of Monte Carlo draws, towards Gaussian processes. We examine their performance in practice through a set of numerical experiments and find that both methods perform well with the sieve method being particularly attractive in terms of computational speed and accuracy.

Keywords: Dynamic discrete choice; Numerical solution; Monte Carlo; Sieves (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods (2020)
Working Paper: Solving dynamic discrete choice models using smoothing and sieve methods (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:223:y:2021:i:2:p:328-360

DOI: 10.1016/j.jeconom.2020.02.007

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