Stochastic control for economic models: past, present and the paths ahead

Kendrick, David A.

doi:10.1016/j.jedc.2003.02.002

Cited by 56 publications

(48 citation statements)

References 104 publications

(83 reference statements)

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“…In future work with more detailed versions of NEDyM, we expect to use the ideas of parameter estimation from the engineering and control literature (Gelb, 1974;Kendrick, 2005) in order to determine the history of the system, as well as the dynamic parameter values, from the observable economic variables, when and where available. Such ideas have been successfully applied for very large systems with partial and irregular observations, in meteorology (Kalnay, 2003) and oceanography (Malanotte-Rizzoli, 1996; Ghil, 1997).…”

Section: Model Equilibriummentioning

confidence: 99%

Business cycles, bifurcations and chaos in a neo-classical model with investment dynamics

Hallegatte

Ghil

Dumas

et al. 2008

Journal of Economic Behavior & Organization

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This paper is motivated by the rising interest in assessing the effect of disruptions in resources and environmental conditions on economic growth. Such an assessment requires, ultimately, the use of truly integrated models of the climate and economic systems. For these purposes, we have developed a Non-Equilibrium Dynamic Model (NEDyM) by introducing investment dynamics and nonequilibrium effects into a Solow growth model. NEDyM can reproduce various economic regimes, such as manager-or shareholder-driven economies, and permits one to examine the effects of disruptions on the economy, given either an assumption of steady-state growth or an assumption of business cycles with transient disequilibrium. We have applied NEDyM to an idealized economy that resembles in certain respects the 15-state European Union in 2001.The key parameter in NEDyM is investment flexibility. For certain values of this parameter, the model reproduces classical business cycles with realistic characteristics; in particular, NEDyM captures the cycles' asymmetry, with a longer growth phase and more rapid contraction. The cyclical behavior is due to the investmentprofit instability and is constrained by the increase in labor costs and the inertia of production capacity. For somewhat greater investment flexibility, the model exhibits chaotic behavior, because a new constraint intervenes, namely limited investment capacity. The preliminary results presented here show that complex behavior in the economic system may be due entirely, or at least largely, to deterministic, intrinsic factors, even if the economic long-term equilibrium is neo-classical in nature. In the chaotic regime, moreover, slight shocks -such as those due to natural or man-made catastrophes -may lead to significant changes in the economic system.

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Section: Model Equilibriummentioning

confidence: 99%

Business cycles, bifurcations and chaos in a neo-classical model with investment dynamics

Hallegatte

Ghil

Dumas

et al. 2008

Journal of Economic Behavior & Organization

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“…As we have related elsewhere, Kendrick (2005), in the early work on dual control we did not expect that the cost-to-go function would exhibit nonconvexities and we did not make any allowance for this. However, we accidentally discovered that local optima existed in the cost-to-go functions, Kendrick (1978), Norman, Norman and Palash (1979), and this was confirmed by theoretical research by Mizrach (1991) and by numerical research by Amman and Kendrick (1995).…”

Section: Non-convexitiesmentioning

confidence: 99%

Learning About Learning in Dynamic Economic Models

Kendrick

Amman

Tucci

2014

Handbook of Computational Economics Vol. 3

Self Cite

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This chapter of the Handbook of Computational Economics is mostly about research on active learning and is confined to discussion of learning in dynamic models in which the systems equations are linear, the criterion function is quadratic and the additive noise terms are Gaussian. Though there is much work on learning in more general systems, it is useful here to focus on models with these specifications since more general systems can be approximated in this way and since much of the early work on learning has been done with these quadratic-linear-gaussian systems.We begin with what has been learned about learning in dynamic economic models in the last few decades. Then we progress to a discussion of what we hope to learn in the future from a new project that is just getting underway. However before doing either of these it is useful to provide a short description of the mathematical framework that will be used in the chapter.Key words: Active learning, dual control, optimal experimentation, stochastic optimization, time-varying parameters, forward looking variables, numerical experiments.JEL Classification: C63, E61.Email addresses: kendrick@austin.utexas.edu (David A. Kendrick), h.m.amman@uu.nl (Hans M. Amman), tucci@unisi.it (Marco P. Tucci). 1 Corresponding author David A. Kendrick. We are indebted to a referee for many helpful comments and suggestions.

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“…Robust policy rules are found assuming that the moments of parameter uncertainty are not available and by using min-max methods where the maximization is taken over the range of parameter values and then we minimize with respect to control variables (cf. Kendrick 2005). In Onatski and Stock (2002), Giannoni (2002) and Giannoni (2007) the authors using the min-max technique show that the robust optimal policy rule is likely to involve an aggressive response of the interest rate to inflation and the output gap shocks than is the case in the absence of uncertainty.…”

Section: Related Literature On Uncertainty In Monetary Policymentioning

confidence: 99%

Robust Monetary Policy in a Model of the Polish Economy: Is the Uncertainty Responsible for the Interest Rate Smoothing Effect?

Górajski

2017

Comput Econ

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Optimal monetary policy models in the linear-quadratic framework produce high variability of interest rates, and are hence inconsistent with the data where typically interest rate smoothing is observed. In this paper we determine optimal monetary polices in a VAR model of the Polish economy with parameter uncertainty. We prove that there exists a structure of the multiplicative uncertainty in the optimal linear-quadratic model that explains the central bank's behaviour. Thus proving that parameter uncertainty can be the rationale for "timid" movements in the short-interest rate dynamics. Finally, we show that there is trade off between parameter uncertainty and the interest rate smoothing incentive.

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Stochastic control for economic models: past, present and the paths ahead

Cited by 56 publications

References 104 publications

Business cycles, bifurcations and chaos in a neo-classical model with investment dynamics

Business cycles, bifurcations and chaos in a neo-classical model with investment dynamics

Learning About Learning in Dynamic Economic Models

Robust Monetary Policy in a Model of the Polish Economy: Is the Uncertainty Responsible for the Interest Rate Smoothing Effect?

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