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

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 whe...

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Bibliographic Details
Published inJournal of economic dynamics & control Vol. 34; no. 9; pp. 1531 - 1549
Main Authors Tucci, Marco P., Kendrick, David A., Amman, Hans M.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.09.2010
Elsevier
Elsevier Sequoia S.A
SeriesJournal of Economic Dynamics and Control
Subjects
Active learning
Active learning Dual control Optimal experimentation Stochastic optimization Time-varying parameters Numerical experiments
Comparative analysis
Dual control
Econometric models
Economic models
Estimation
Experiment design
Learning
Monte Carlo simulation
Numerical experiments
Optimal experimentation
Parameter estimation
Quantitative Finance
Stochastic control theory
Stochastic models
Stochastic optimization
Studies
Time-varying parameters
C63
E61
Time-varying parameters
Active learning
Numerical experiments
Dual control
Optimal experimentation
Stochastic optimization
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Summary: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 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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0165-1889
1879-1743
DOI:10.1016/j.jedc.2010.06.014