Approximating highdimensional dynamic models: Sieve value function iteration
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2013-01-01
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Abstract
Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of highdimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik's parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated. Copyright © 2013 by Emerald Group Publishing Limited.
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Arcidiacono, P, P Bayer, FA Bugni and J James (2013). Approximating highdimensional dynamic models: Sieve value function iteration. Advances in Econometrics, 31. pp. 45–95. 10.1108/S0731-9053(2013)0000032002 Retrieved from https://hdl.handle.net/10161/13086.
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Peter S. Arcidiacono
Professor Arcidiacono specializes in research involving applied microeconomics, applied economics, and labor economics. His research primarily focuses on education and discrimination. His work focuses specifically on the exploration of a variety of subjects, such as structural estimation, affirmative action, minimum wages, teen sex, discrimination, higher education, and dynamic discrete choice models, among others. He recently received funding from a National Science Foundation Grant for his project, “CCP Estimation of Dynamic Discrete Choice Models with Unobserved Heterogeneity.” He has also been awarded grants from NICHD for his work entitled, “A Dynamic Model of Teen Sex, Abortion, and Childbearing;” and from the Smith Richardson Foundation for his study, “Does the River Spill Over? Race and Peer Effects in the College & Beyond” with Jacob Vigdor. Other recent studies of his include, “The Distributional Effects of Minimum Wage Increases when Both Labor Supply and Labor Demand are Endogenous” with Tom Ahm and Walter Wessles; “Explaining Cross-racial Differences in Teenage Labor Force Participation: Results from a General Equilibrium Search Model” with Alvin Murphy and Omari Swinton; and “The Effects of Gender Interactions in the Lab and in the Field” in collaboration with Kate Antonovics and Randy Walsh.
Patrick Bayer
Bayer's research focuses on wide range of subjects including racial inequality and segregation, social interactions, housing markets, education, and criminal justice. His most recent work has been published in the Quarterly Journal of Economics, American Economic Review, Econometrica, and the Review of Financial Studies. He is currently working on projects that examine jury representation and its consequences, the intergenerational consequences of residential and school segregation, neighborhood tipping, gentrification, the effect of police and criminal justice interactions on families, and the impact of bail reform.
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