Nonconvexities in Stochastic Control Models

Amman, Hans M.; Kendrick, David A.

doi:10.2307/2527206

Cited by 31 publications

(24 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1982), Mizrach (1991), Amman and Kendrick (1995) and others). An advantage of the algorithm used here is that it approximates policy and value functions for this nonlinear dynamic programming problem over a wide range of the state space and is flexible to allow for nondifferentiabilities in the value function and discontinuities in the optimal policy.…”

mentioning

confidence: 98%

Monetary Policy, Parameter Uncertainty and Optimal Learning

Wieland

1999

SSRN Journal

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractSince central banks have limited information concerning the transmission channel of monetary policy, they are faced with the difficult task of simultaneously controlling the policy target and estimating the impact of policy actions. A tradeoff between estimation and control arises because policy actions influence estimation and provide information which may improve future performance. I analyze this tradeoff in a simple model with parameter uncertainty and conduct dynamic simulations of the policymaker's decision problem in the presence of the type of uncertainties that arose in the wake of German reunification. A policy that separates learning from control may induce a persistent upward bias in money growth and inflation, just as observed after unification. In contrast, the optimal learning strategy which exploits the tradeoff between control and estimation significantly improves stabilization performance and reduces the likelihood of inflationary bias.

show abstract

mentioning

confidence: 98%

Monetary Policy, Parameter Uncertainty and Optimal Learning

Wieland

1999

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…See for example Tse and Bar-Shalom (1973), Kendrick (1981), Kendrick (1982), Mizrach (1991), Amman andKendrick (1995), Tucci (1996)). …”

mentioning

confidence: 99%

“…As mentioned above, numerical approaches to solving this class of optimal learning problems have been studied extensively and for some time in engineering. Termed \dual control", these methods have been further developed and applied to economic problems by Kendrick (1981) and, Norman (1976), Mizrach (1991), Amman and Kendrick (1995) and others. There are several important dierences between dual control and the dynamic programming algorithm used here: (i) while the dual control algorithm typically involves a rst-or second-order linear approximation, the DP algorithm used here directly takes into account the nonlinearity of the updating equations which are at the center of the learning problem; (ii) while the dual control algorithm approximates ex-post payos for a given initial belief about the unknown parameters for alternative sequences of shocks as in Monte Carlo simulations, the DP algorithm used here approximates ex-ante payos and policies for a range of initial beliefs and any possible sequence of shocks.…”

mentioning

confidence: 99%

Monetary Policy and Uncertainty about the Natural Unemployment Rate

Wieland¹

2003

SSRN Journal

View full text Add to dashboard Cite

Recent empirical research concerning the relationship between ination and unemployment, a relationship that is central to the design of monetary policy, has been characterized by an active debate about the precision of relevant parameter estimates such as the estimated natural unemployment rate. This paper studies the optimal monetary policy in the presence of uncertainty about the natural rate and the short-run ination-unemployment tradeo in a simple macroeconomic model. Two conicting motives drive the optimal policy. In the static version of the model, uncertainty provides a motive for the policymaker to move more cautiously than she would if she knew the true parameters. In the dynamic version, uncertainty also motivates an element of experimentation in policy. I nd that the optimal policy that balances the cautionary and activist motives typically exhibits gradualism, i.e. it is less aggressive than a policy that disregards parameter uncertainty. Exceptions occur when uncertainty i s v ery high and ination close to target.

show abstract

“…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). Also, the presence of the nonconvexities has been confirmed by Wieland (2000a) using a different solution method, namely value function iteration.…”

Section: Non-convexitiesmentioning

confidence: 83%

Learning About Learning in Dynamic Economic Models

Kendrick

Amman

Tucci

2014

Handbook of Computational Economics Vol. 3

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Nonconvexities in Stochastic Control Models

Cited by 31 publications

References 7 publications

Monetary Policy, Parameter Uncertainty and Optimal Learning

Monetary Policy, Parameter Uncertainty and Optimal Learning

Monetary Policy and Uncertainty about the Natural Unemployment Rate

Learning About Learning in Dynamic Economic Models

Contact Info

Product

Resources

About