Monetary policy under uncertainty

“Optimal Monetary Policy under Uncertainty: A Markov Jump-Linear-Quadratic Approach” (with Noah Williams, University of Wisconsin), Federal Reserve Bank of St. Louis Review 90(4), , 2008, 275-293, PDFAbstract.

The line: We use a Markov jump-linear-quadratic (MJLQ) approach to analyze how policy is affected by uncertainty, learning, and experimentation,  finding that learning may have sizeable effects on losses and need not always be beneficial, whereas the experimentation component typically has little effect and can in some cases lead to attenuation of policy.

“Optimal Monetary Policy under Uncertainty in DSGE Models: A Markov-Jump-Linear-Quadratic Approach” (with Noah Williams, University of Wisconsin), presented at the conference on Monetary Policy Under Uncertainty and Learning, Santiago, Chile, November 2007, PDFAbstract.

The line: We use a Markov jump-linear-quadratic (MJLQ) approach to a benchmark New Keynesian model, analyzing how policy is affected by uncertainty, and how learning and active experimentation affect policy and losses.

“Bayesian and Adaptive Optimal Policy under Model Uncertainty” (with Noah Williams, University of Wisconsin), September 2007, PDFAbstract.

The line: Bayesian optimal policy (which includes both learning and experimentation) in a both general and tractable case of model uncertainty is compared to adaptive optimal policy (which includes learning but excludes experimentation), and the results indicate that optimal experimentation brings only modest gains above the learning under adaptive optimal policy.

“Monetary Policy with Model Uncertainty: Distribution Forecast Targeting” (with Noah Williams, University of Wisconsin), May 2007, PDFAbstract and Matlab programs.

The line: A very flexible, powerful, and yet tractable framework for the analysis and determination of optimal monetary policy under  model uncertainty and certainty non-equivalence is introduced and shown to incorporate a large variety of different configurations of uncertainty and central-bank judgment.

“Optimal Policy Projections” (with Robert J. Tetlow, Federal Reserve Board), August 2005, International Journal of Central Banking 1(3) (2005) 177-207, PDFAbstract.

The line: Optimal Policy Projections – a method to give advice to policymakers on optimal monetary policy, taking central-bank judgment into account – is demonstrated with the Fed’s FRB/US model and two Greenbook forecasts.

“Comment on Brock and Durlauf, ‘Local Robustness Analysis: Theory and Applications’,” AEA Meeting, Philadelphia, January 2005, overhead slides.

The line: Brock and Durlauf (2004) provides an elegant and compact analysis in the frequency domain of local robust control, but there are many limitations of their method, which makes it less practical.

“Monetary Policy with Judgment: Forecast Targeting,” International Journal of Central Banking 1(1) (2005) 1-54, PDFAbstractAppendix.

The line: Monetary policy that uses central-bank judgment – information, knowledge, and views outside the scope of a particular model – may perform much better than monetary policy that disregards judgment and follows a simple instrument rule.

“Optimal Policy with Low-Probability Extreme Events,” in Macroeconomics, Monetary Policy, and Financial Stability – A Festschrift for Charles Freedman, Proceedings of a conference held by the Bank of Canada, Ottawa, June 2003, 79-104. PDFAbstract.

“Monetary Policy and Learning,” Federal Reserve Bank of Atlanta Economic Review, Third Quarter 2003, 11-16, PDF (109 KB).

Discussion of Francesco Lippi and Stefano Neri, “Information Variables for Monetary Policy in a Small Structural Model of the Euro Area,” Workshop on  Small Structural Models for Monetary Policy Analysis: Progress, Puzzles, and Opportunities, Sveriges Riksbank, Stockholm, June 6-7, 2003, overhead slides (50 KB).

“Liquidity Traps, Policy Rules for Inflation Targeting, and Eurosystem Monetary-Policy Strategy,” research summary, NBER Reporter, Winter 2002/2003PDF (119 KB). Previous research summary for NBER Reporter, Winter 97/98.

“Optimal Policy with Partial Information in a Forward-Looking Model: Certainty-Equivalence Redux,” (with Michael Woodford, Columbia University), June 2002, PDF (204 KB). Abstract.

“Indicator Variables for Optimal Policy under Asymmetric Information,” (with Michael Woodford, Columbia University), June 2002, Journal of Economic Dynamics and Control 28 (2004) 661-690, PDF (356 KB). Abstract.

Discusson of Athanasios Orphanides and John C. Williams, “Imperfect Knowledge, Inflation Expectations, and Monetary Policy,” Econometric Society North American Winter Meeting, Atlanta, Jan 4-6, 2002, overhead slides (PDF, 56 KB).
“Robust Control Made Simple: Lecture Notes,” February 2007, PDF.

“Indicator Variables for Optimal Policy,” (with Michael Woodford, Columbia University), Journal of Monetary Economics 50 (2003) 691-720. PDF (334 KB). Abstract.

  • Longer working paper version with more details, Jan 2002, PDF (388 KB).

“Comment on Isard, Laxton and Eliasson, ‘Simple Monetary Policy Rules Under Model Uncertainty’,” in Peter Isard, Assaf Razin and Andrew Rose, eds. (1999), International Finance and Financial Crises: Essays in Honor of Robert P. FloodInternational Monetary Fund, Washington, and Kluwer Academic Publishers, Boston, 251-257, PDF (116 KB).

“Inflation Targeting: Some Extensions,” Scandinavian Journal of Economics 101(3) (1999) 337-361.

  • Revised and corrected Working Paper version, February 1998, PDF (0.3 MB). Abstract.
  • Revised and shortened Journal version, December 1998, PDF (0.2 MB). Abstract.