Temporal-Difference Estimation of Dynamic Discrete Choice Models.
We study the use of Temporal-Difference learning for estimating the structural parameters in dynamic discrete choice models. Our algorithms are based on the conditional choice probability approach but use functional approximations to estimate various terms in the pseudo-log-likelihood function. We s...
| Publicado en: | Review of Economic Studies Vol. 93; no. 4; pp. 2181 - 2215 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=195161448&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 195161448 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346527 REM jtl: Review of Economic Studies issn: 00346527 maglogo: N pubinfo: dt: Jul2026 vid: 93 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 195161448 10.1093/restud/rdaf081 ppf: 2181 ppct: 34 formats: tig: atl: Temporal-Difference Estimation of Dynamic Discrete Choice Models. aug: au: Adusumilli, Karun Eckardt, Dita affil: Department of Economics, University of Pennsylvania, USA Department of Economics, University of Warwick su: Discrete choice models Parameter estimation Conditional probability Statistical models Dynamic programming Nonparametric estimation sug: subj: Discrete choice models Parameter estimation Conditional probability Statistical models Dynamic programming Nonparametric estimation keyword: copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 Dynamic discrete choice models Dynamic discrete games inLanguage:en publisher:Oxford University Press Reinforcement Learning sameAs:https://dx.doi.org/10.1093/restud/rdaf081 Temporal-Difference learning copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 Dynamic discrete choice models Dynamic discrete games inLanguage:en publisher:Oxford University Press Reinforcement Learning sameAs:https://dx.doi.org/10.1093/restud/rdaf081 Temporal-Difference learning ab: We study the use of Temporal-Difference learning for estimating the structural parameters in dynamic discrete choice models. Our algorithms are based on the conditional choice probability approach but use functional approximations to estimate various terms in the pseudo-log-likelihood function. We suggest two approaches: The first—linear semi-gradient—provides approximations to the recursive terms using basis functions. The second—Approximate Value Iteration—builds a sequence of approximations to the recursive terms by solving non-parametric estimation problems. Our approaches are fast and naturally allow for continuous and/or high-dimensional state spaces. Furthermore, they do not require specification of transition densities. In dynamic games, they avoid integrating over other players' actions, further heightening the computational advantage. Our proposals can be paired with popular existing methods such as pseudo-maximum-likelihood, and we propose locally robust corrections for the latter to achieve parametric rates of convergence. Monte Carlo simulations confirm the properties of our algorithms in practice. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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