The parameter set in an adaptive control Monte Carlo experiment: Some considerations

Marco P. Tucci (), David Kendrick and Hans Amman
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Marco P. Tucci: Dipartimento di Economia Politica - UNISI - Università degli Studi di Siena = University of Siena, Department of Economics - UTHealth - The University of Texas Health Science Center at Houston, Universiteit Utrecht / Utrecht University [Utrecht]

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Abstract: Comparisons of various methods for solving stochastic control economic models can be done with Monte Carlo methods. These methods have been applied to simple one-state, one-control quadratic-linear tracking models; however, large outliers may occur in a substantial number of the Monte Carlo runs when certain parameter sets are used in these models. Building on the work of Mizrach (1991) and Amman and Kendrick (1994, 1995), this paper tracks the source of these outliers to two sources: (1) the use of a zero or the penalty weights on the control variables and (2) the generation of near-zero initial estimate of the control parameter in the systems equations by the Monte Carlo routine. This result leads to an understanding of why both the unsophisticated Optimal Feedback (Certainty Equivalence) and the sophisticated Dual methods do poorly in some Monte Carlo comparisons relative to the moderately sophisticated Expected Optimal Feedback method.

Keywords: Active learning; Dual control; Optimal experimentation; Stochastic optimization; Time-varying parameters; Numerical experiments (search for similar items in EconPapers)
Date: 2010-09-16
Note: View the original document on HAL open archive server: https://hal.science/hal-00732676
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Citations: View citations in EconPapers (9)

Published in Journal of Economic Dynamics and Control, 2010, 34 (9), pp.1531. ⟨10.1016/j.jedc.2010.06.014⟩

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Journal Article: The parameter set in an adaptive control Monte Carlo experiment: Some considerations (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00732676

DOI: 10.1016/j.jedc.2010.06.014

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