A Classification System for Economic Stochastic Control Models

Kendrick, David A.; Amman, Hans M.

doi:10.1007/s10614-005-9000-8

Cited by 22 publications

(17 citation statements)

References 85 publications

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“…31 Dual control minimizes the approximate cost-to-go defined in equation (27). The EOF control is determined using the feedback rule 31 See Kendrick and Amman (2006) for a description of the three different methods.…”

Section: The Importance Of Penalty Weights On the Of And Eof Solutionsmentioning

confidence: 99%

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

Tucci¹,

Kendrick²,

Amman³

2010

Journal of Economic Dynamics and Control

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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 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.

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Section: The Importance Of Penalty Weights On the Of And Eof Solutionsmentioning

confidence: 99%

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

Tucci¹,

Kendrick²,

Amman³

2010

Journal of Economic Dynamics and Control

Self Cite

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“…Kendrick and Amman (2010) consider the importance of both 1-period and 2-period lags in the control variable structure when evaluating quarterly policy, but do not consider the 2-period lags in the state variables, as we have done here. Kendrick and Amman (2006) discuss modeling stochastic systems with forward-looking variables, but our analysis does not employ forwardlooking loops.…”

Section: Contribution To Research On Robustness and Fiscal Policymentioning

confidence: 99%

“…(6) and (7) when writing the state-space system in a standard form. Kendrick and Amman (2006) use a vector of ones for the system, and then multiply this by a coefficient matrix that consists of the constants in each of the state equations. This leads to an extra additive matrix term in the system state equation.…”

Section: Macroeconomic Model Derivation For Deterministic and Lqg Optmentioning

confidence: 99%

Entering H $$^{\infty }$$ ∞ -Optimal Control Robustness into a Macroeconomic LQ-Tracking Model

Hudgins

2014

Comput Econ

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This analysis explores robust designs for an applied macroeconomic discrete-time LQ tracking model with perfect state measurements. We develop a procedure that reframes the tracking problem as a regulator problem that is then used to simulate the deterministic, stochastic LQG, H-infinity, multiple-parameter minimax, and mixed stochastic/H-infinity control, for quarterly fiscal policy. We compare the results of the five different design structures within a closed-economy accelerator model using data for the United States for the period 1947-2012. When the consumption and investment tracking errors are more heavily emphasized, the H-infinity design renders the most aggressive fiscal policy, followed by the multiple-parameter minimax, mixed, LQG, and deterministic versions. When the control tracking errors are heavily weighted, the resulting fiscal policy is initially more aggressive under the multi-parameter specification than under the H-infinity and mixed designs. The results from both weighting schemes show that fiscal policy remains more aggressive under the robust designs than the deterministic model. The simulations show that the multiparameter minimax and mixed designs provide a balancing compromise between the stochastic and robust methods when the worst-case concerns can be primarily limited to a subset of the state-space.

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“…A useful distinction was adapted from the engineering literature by Kendrick (1981); see also Kendrick and Amman (2006): Open-loop policies neglect the possibility of receiving information (measurements) during the operation of the system; open-loop feedback (or passive learning) policies use actual information to determine the control but do not anticipate future measurements; and closed-loop (or active learning) policies make some use also of information about future measurements. Given the intricacies of the interplay between control and information, even for very simple stochastic control problems (for example, linear scalar system, time horizon of only two periods)…”

Section: Optcon1mentioning

confidence: 99%

Optimal control of nonlinear dynamic econometric models: An algorithm and an application

Blueschke-Nikolaeva

Blueschke

Neck

2012

Computational Statistics & Data Analysis

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A Classification System for Economic Stochastic Control Models

Abstract: stochastic control, economic stochastic control models, classification system, naming system, adaptive control, min–max control, robust control,

Cited by 22 publications

References 85 publications

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

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

Entering H $$^{\infty }$$ ∞ -Optimal Control Robustness into a Macroeconomic LQ-Tracking Model

Optimal control of nonlinear dynamic econometric models: An algorithm and an application

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