Approximate Policy Optimization and Adaptive Control in Regression Models

Authors:
Jiarui Han;Tze Leung Lai;Viktor Spivakovsky
Affiliations:
Barclays Global Investors, San Francisco, USA 94105;Barclays Global Investors, San Francisco, USA 94105;Citadel Investment Group, Chicago, USA 60603
Venue:
Computational Economics
Year:
2006

Citing 8
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Abstract

In this paper we use recent advances in approximate dynamic programming to develop an approximate policy optimization procedure that uses Monte Carlo simulations for numerical solution of dynamic optimization problems in economics. The procedure is applied to the classical problem of "learning by doing" in regression models, for which the value and extent of active experimentation are demonstrated in a variety of numerical studies.