Approximating highdimensional dynamic models: Sieve value function iteration
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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.
Published Version (Please cite this version)10.1108/S0731-9053(2013)0000032002
Publication InfoArcidiacono, P; Bayer, P; Bugni, FA; & James, J (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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Professor of Economics
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 pro
Gilhuly Family Distinguished Professor in Economics
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
Professor of Economics
Professor Bugni’s research interests are in theoretical econometrics, partial identification, inference, moment (in)equalities, missing data, and stochastic processes. He has received grants from the National Science Foundation. His most recent work has been published in journals like Econometrica. He is currently working on projects the explore the determinants of college graduation, missing functional data, robustness in partial identification, and regression with missing covariates.
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