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Optimal Nonlinear Policy: Signal Extraction with a Non-Normal Prior

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  • Eric Swanson

Abstract

The literature on optimal monetary policy typically makes three major assumptions: 1) policymakers’ preferences are quadratic, 2) the economy is linear, and 3) stochastic shocks and policymakers’ prior beliefs about unobserved variables are normally distributed. This paper relaxes the third assumption and explores its implications for optimal policy. The separation principle continues to hold in this framework, allowing for tractability and application to forward-looking models, but policymakers’ beliefs are no longer updated in a linear fashion, allowing for plausible nonlinearities in optimal policy. We consider in particular a class of models in which policymakers’ priors about the natural rate of unemployment are diffuse in a region around the mean. When this is the case, it is optimal for policy to respond cautiously to small surprises in the observed unemployment rate, but become increasingly aggressive at the margin. These features of optimal policy match statements by Federal Reserve officials and the behavior of the Fed in the 1990s

Suggested Citation

  • Eric Swanson, 2005. "Optimal Nonlinear Policy: Signal Extraction with a Non-Normal Prior," Computing in Economics and Finance 2005 147, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:147
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    References listed on IDEAS

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    1. Orphanides, Athanasios & Wieland, Volker, 2000. "Inflation zone targeting," European Economic Review, Elsevier, vol. 44(7), pages 1351-1387, June.
    2. Swanson, Eric T., 2004. "Signal Extraction And Non-Certainty-Equivalence In Optimal Monetary Policy Rules," Macroeconomic Dynamics, Cambridge University Press, vol. 8(1), pages 27-50, February.
    3. Volker Wieland, "undated". "Monetary Policy and Uncertainty about the Natural Unemployment Rate," Computing in Economics and Finance 1997 11, Society for Computational Economics.
    4. Sack, Brian & Wieland, Volker, 2000. "Interest-rate smoothing and optimal monetary policy: a review of recent empirical evidence," Journal of Economics and Business, Elsevier, vol. 52(1-2), pages 205-228.
    5. Svensson, Lars E. O. & Woodford, Michael, 2003. "Indicator variables for optimal policy," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 691-720, April.
    6. Andrew J. Filardo, 1998. "New evidence on the output cost of fighting inflation," Economic Review, Federal Reserve Bank of Kansas City, vol. 83(Q III).
    7. Pearlman, Joseph & Currie, David & Levine, Paul, 1986. "Rational expectations models with partial information," Economic Modelling, Elsevier, vol. 3(2), pages 90-105, April.
    8. Laurence H. Meyer & Eric T. Swanson & Volker W. Wieland, 2001. "NAIRU Uncertainty and Nonlinear Policy Rules," American Economic Review, American Economic Association, vol. 91(2), pages 226-231, May.
    9. repec:cup:macdyn:v:8:y:2004:i:1:p:27-50 is not listed on IDEAS
    10. Pearlman, Joseph G., 1992. "Reputational and nonreputational policies under partial information," Journal of Economic Dynamics and Control, Elsevier, vol. 16(2), pages 339-357, April.
    11. Douglas O. Staiger & James H. Stock & Mark W. Watson, 1997. "How Precise Are Estimates of the Natural Rate of Unemployment?," NBER Chapters, in: Reducing Inflation: Motivation and Strategy, pages 195-246, National Bureau of Economic Research, Inc.
    12. Swanson, Eric T., 2006. "Optimal nonlinear policy: signal extraction with a non-normal prior," Journal of Economic Dynamics and Control, Elsevier, vol. 30(2), pages 185-203, February.
    13. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
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    1. Swanson, Eric T., 2006. "Optimal nonlinear policy: signal extraction with a non-normal prior," Journal of Economic Dynamics and Control, Elsevier, vol. 30(2), pages 185-203, February.
    2. Hauk, Esther & Lanteri, Andrea & Marcet, Albert, 2021. "Optimal policy with general signal extraction," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 54-86.
    3. Mandler, Martin, 2010. "Macroeconomic dynamics and inflation regimes in the U.S. Results from threshold vector autoregressions," MPRA Paper 21887, University Library of Munich, Germany.
    4. Neuenkirch, Matthias & Tillmann, Peter, 2014. "Inflation targeting, credibility, and non-linear Taylor rules," Journal of International Money and Finance, Elsevier, vol. 41(C), pages 30-45.
    5. Lim, G.C. & McNelis, Paul D., 2007. "Inflation targeting, learning and Q volatility in small open economies," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3699-3722, November.
    6. Laurence H. Meyer & Eric T. Swanson & Volker W. Wieland, 2001. "NAIRU Uncertainty and Nonlinear Policy Rules," American Economic Review, American Economic Association, vol. 91(2), pages 226-231, May.
    7. Christopher J. Erceg & James Hebden & Michael T. Kiley & J. David López-Salido & Robert J. Tetlow, 2018. "Some Implications of Uncertainty and Misperception for Monetary Policy," Finance and Economics Discussion Series 2018-059, Board of Governors of the Federal Reserve System (U.S.).
    8. Lim, G.C. & McNelis, Paul D., 2008. "Computational Macroeconomics for the Open Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262123061, April.
    9. Andrea Lanteri & Albert Marcet & Esther Hauk, 2014. "Optimal Policy with Endogenous Signal Extraction," 2014 Meeting Papers 677, Society for Economic Dynamics.

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    More about this item

    Keywords

    nonlinear policy; optimal filtering; signal extraction; learning; non-normal priors;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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