Robustness and U.S. Monetary Policy Experimentation

We study how a concern for robustness modifies a policy maker’s incentive to experiment. A policy maker has a prior over two submodels of inflationunemployment dynamics. One submodel implies an exploitable trade-off, the other does not. Bayes’ law gives the policy maker an incentive to experiment. The policy maker fears that both submodels and his prior probability distribution over them are misspecified. We compute decision rules that are robust to misspecifications of each submodel and of the prior distribution over submodels. We compare robust rules to ones that Cogley, Colacito, and Sargent (2007) computed assuming that the models and the prior distribution are correctly specified. We explain how the policy maker’s desires to protect against misspecifications of the submodels, on the one hand, and misspecifications of the prior over them, on the other, have different effects on the decision rule.

[1]  Evan L. Porteus,et al.  Temporal Resolution of Uncertainty and Dynamic Choice Theory , 1978 .

[2]  Finn E. Kydland,et al.  Rules Rather than Discretion: The Inconsistency of Optimal Plans , 1977, Journal of Political Economy.

[3]  M. Arak.  Some International Evidence on Output-Inflation Tradeoffs: Comment , 1977 .

[4]  Nancy L. Stokey.  Reputation and Time Consistency , 1989 .

[5]  P. Whittle.  Risk-Sensitive Optimal Control , 1990 .

[6]  Gunter W. Beck,et al.  Learning and control in a changing economic environment , 2002 .

[7]  Paul A. Samuelson,et al.  Analytical Aspects of Anti-Inflation Policy , 2008 .

[8]  Thomas J. Sargent,et al.  Rational Expectations, the Real Rate of Interest, and the Natural Rate of Unemployment , 1973 .

[9]  Alan Greenspan,et al.  Risk and Uncertainty in Monetary Policy , 2004 .

[10]  A. Blinder.  Central Banking in Theory and Practice , 1998 .

[11]  Martin Schneider,et al.  Recursive multiple-priors , 2003, J. Econ. Theory.

[12]  M. Ozier.  Learning from Learning. , 1985 .

[13]  K. Narendra,et al.  Discounted Linear Exponential Quadratic Gaussian Control , 1995 .

[14]  Timothy Cogley,et al.  The Conquest of U.S. Inflation: Learning and Robustness to Model Uncertainty , 2005, SSRN Electronic Journal.

[15]  Lars Peter Hansen,et al.  Recursive Robust Estimation and Control Without Commitment , 2007, J. Econ. Theory.

[16]  R. Sundaram,et al.  Bayesian Economist... Bayesian Agents I: An Alternative Approach to Optimal Learning , 1993 .

[17]  Volker Wieland,et al.  Monetary Policy, Parameter Uncertainty and Optimal Learning , 1999 .

[18]  Volker Wieland,et al.  Learning by doing and the value of optimal experimentation , 2000 .

[19]  David M. Kreps,et al.  Advances In Economics and Econometrics: Theory And Applications: Seventh World Congress , 1997 .

[20]  Timothy Cogley,et al.  Benefits from U.S. Monetary Policy Experimentation in the Days of Samuelson and Solow and Lucas , 2007 .

[21]  M. Marinacci,et al.  A Smooth Model of Decision Making Under Ambiguity , 2003 .

[22]  R. Lucas.  Expectations and the neutrality of money , 1972 .

[23]  R. Marimon.  Learning from learning in economics , 1996 .

[24]  Kenneth Kasa.  Will the Fed Ever Learn , 1999 .

[25]  Janet L. Yellen,et al.  Innovations and Issues in Monetary Policy: Panel Discussion , 2004 .

[26]  Lars Peter Hansen,et al.  Robust estimation and control under commitment , 2005, J. Econ. Theory.

[27]  T. Sargent,et al.  Fragile beliefs and the price of model uncertainty , 2006 .

[28]  Sharon Gifford,et al.  Risk and Uncertainty , 2010 .

[29]  Anthony O'Hagan,et al.  Eliciting expert beliefs in substantial practical applications , 1998 .

[30]  R. Lucas.  Methods and Problems in Business Cycle Theory , 1980 .