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

Marco P. Tucci, David Kendrick and Hans Amman

Journal of Economic Dynamics and Control, 2010, vol. 34, issue 9, 1531-1549

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) and (Amman and Kendrick, 1995), this paper tracks the source of these outliers to two sources: (1) the use of a zero for 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
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Citations: View citations in EconPapers (10)

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Working Paper: The parameter set in an adaptive control Monte Carlo experiment: Some considerations (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:34:y:2010:i:9:p:1531-1549

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