Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-13T01:02:01.423Z Has data issue: false hasContentIssue false
  • English
  • Français

SIGNAL EXTRACTION AND NON-CERTAINTY-EQUIVALENCE IN OPTIMAL MONETARY POLICY RULES

Published online by Cambridge University Press:  30 January 2004

ERIC T. SWANSON
ERIC T. SWANSON
Affiliation:
Board of Governors of the Federal Reserve System
Get access Rights & Permissions [Opens in a new window]

Abstract

A standard result in the literature on monetary policy rules is that of certainty-equivalence: Given the expected values of the state variables of the economy, policy should be independent of all higher moments of those variables. Some exceptions to this rule have been pointed out in the literature, including restricting the policy response to a limited subset of state variables, or to estimates of the state variables that are biased. In contrast, this paper studies fully optimal policy rules with optimal estimation of state variables. The rules in this framework exhibit certainty-equivalence with respect to estimates of an unobserved state variable (“excess demand”) X, but are not certainty-equivalent when (i) X must be estimated by signal extraction and (ii) the optimal rule is expressed as a reduced form that combines policymakers' estimation and policy-setting stages. I find that it is optimal for policymakers to attenuate their reaction to a variable about which uncertainty has increased, while responding more aggressively to variables about which uncertainty has not changed.

Keywords

Certainty EquivalenceSignal ExtractionOptimal Monetary Policy
Type
Research Article
Information
Macroeconomic Dynamics , Volume 8 , Issue 1 , February 2004 , pp. 27 - 50
Copyright
© 2004 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aoki K. 1999 On the Optimal Monetary Policy Response to Noisy Indicators. Manuscript, Princeton University.
Aoki M. 1967 Optimization of Stochastic Systems: Topics in Discrete-Time Systems. New York: Academic Press.
Bernanke B. & Boivin J. 2002 Monetary policy in a data-rich environment. Journal of Monetary Economics in press.
Bertsekas D. 1987 Dynamic Programming. Englewood Cliffs, NJ: Prentice-Hall.
Brainard W. 1967 Uncertainty and the effectiveness of policy. American Economic Review 57, 411– 425.Google Scholar
Drew A. & Hunt B. 2000 Efficient simple policy rules and the implications of potential output uncertainty. Journal of Business and Economics 52, 143– 159.Google Scholar
Estrella A. & Mishkin F. 1999 Rethinking the role of NAIRU in monetary policy: Implications of model formulation and uncertainty. In Taylor J. B. (ed.), Monetary Policy Rules, pp. 405–430. Chicago: University of Chicago Press.
Mankiw N. G. & Shapiro M. 1986 News or noise: An analysis of GNP revisions. Survey of Current Business 66, 20– 25.Google Scholar
McNees S. 1995 An assessment of the “official” economic forecasts. New England Economic Review, July/Aug., 13– 23.Google Scholar
Meyer L. Swanson E. & Wieland V. 2001 NAIRU uncertainty and nonlinear policy rules. American Economic Review 91, 226– 231.Google Scholar
Onatski A. & Stock J. 2000 Robust Monetary Policy under Model Uncertainty in a Small Model of the U.S. Economy. NBER working paper 7490.
Orphanides A. 1998 Monetary Policy Evaluation with Noisy Information. Federal Reserve Board Finance and Economics Discussion Series 1998–50.
Orphanides A. 2001 Monetary policy rules based on real-time data. American Economic Review 91, 964– 985.Google Scholar
Orphanides A. Porter R. Reifschneider D. Tetlow R. & Finan F. 2000 Errors in the measurement of the output gap and the design of monetary policy. Journal of Business and Economics 52, 117– 142.Google Scholar
Romer C. & Romer D. 2000 Federal Reserve private information and the behavior of interest rates. American Economic Review 90, 429– 457.Google Scholar
Rudebusch G. 2001 Is the Fed too timid? Monetary policy in an uncertain world. Review of Economics and Statistics 83, 203– 217.Google Scholar
Rudebusch G. 2002 Assessing nominal income rules for monetary policy with model and data uncertainty. Economic Journal 112, 402– 432.Google Scholar
Sack B. 2000 Does the Fed act gradually? A VAR analysis. Journal of Monetary Economics 46, 229– 256.Google Scholar
Sargent T. 1987 Dynamic Macroeconomic Theory. Cambridge, MA: Harvard University Press.
Smets F. 1998 Output Gap Uncertainty: Does It Matter for the Taylor Rule? Manuscript, European Central Bank.
Stock J. & Watson M. 1998 Diffusion Indexes. NBER working paper 6702.
Stock J. & Watson M. 2000 Forecasting inflation. Journal of Monetary Economics 46, 293– 335.Google Scholar
Svensson L. 1997 Inflation forecast targeting: Implementing and monitoring inflation targets. European Economic Review 41, 1111– 1146.Google Scholar
Svensson L. & Woodford M. 2000 Indicator Variables for Optimal Policy. NBER working paper 7953.
Swanson E. 2001 Optimal Nonlinear Policy: Signal Extraction with a Non-Normal Prior. Manuscript, Federal Reserve Board.
Taylor J. 1993 Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy 39, 195– 214.Google Scholar
Wieland V. 1998 Monetary Policy and Uncertainty About the Natural Unemployment Rate. Federal Reserve Board Finance and Economics Discussion Series 1998–22.