Nested Pseudo likelihood estimation of continuous-time dynamic discrete games

Jason Blevins and Minhae Kim

Journal of Econometrics, 2024, vol. 238, issue 2

Abstract: We introduce a sequential estimator for continuous time dynamic discrete choice models (single-agent models and games) by adapting the nested pseudo likelihood (NPL) estimator of Aguirregabiria and Mira (2002, 2007), developed for discrete time models with discrete time data, to the continuous time case with data sampled either discretely (i.e., uniformly-spaced snapshot data) or continuously. We establish conditions for consistency and asymptotic normality of the estimator, a local convergence condition, and, for single agent models, a zero Jacobian property assuring local convergence. We carry out a series of Monte Carlo experiments using an entry-exit game with five heterogeneous firms to confirm the large-sample properties and demonstrate finite-sample bias reduction via iteration. In our simulations we show that the convergence issues documented for the NPL estimator in discrete time models are less likely to affect comparable continuous-time models. We also show that there can be large bias in economically-relevant parameters, such as the competitive effect and entry cost, from estimating a misspecified discrete time model when in fact the data generating process is a continuous time model.

Keywords: continuous time; dynamic discrete games; dynamic discrete choice; nested pseudo likelihood; entry games (search for similar items in EconPapers)
JEL-codes: C13 C35 C73 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Nested Pseudo Likelihood Estimation of Continuous-Time Dynamic Discrete Games (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:2:s0304407623002920

DOI: 10.1016/j.jeconom.2023.105576

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