[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/inu/caeprp/2008011.html
   My bibliography  Save this paper

Regime Switching, Learning, and the Great Moderation

Author

Listed:
  • James Murray

    (Indiana University Bloomington)

Abstract
This paper examines the "bad luck" explanation for changing volatility in U.S. inflation and output when agents do not have rational expectations, but instead form expectations through least squares learning with an endogenously changing learning gain. It has been suggested that this type of endogenously changing learning mechanism can create periods of excess volatility without the need for changes in the variance of the underlying shocks. Bad luck is modeled into a standard New Keynesian model by augmenting it with two states that evolve according to a Markov chain, where one state is characterized by large variances for structural shocks, and the other state has relatively smaller variances. To assess whether learning can explain the Great Moderation, the New Keynesian model with volatility regime switching and dynamic gain learning is estimated by maximum likelihood. The results show that learning does lead to lower variances for the shocks in the volatile regime, but changes in regime is still significant in differences in volatility from the 1970s and after the 1980s.

Suggested Citation

  • James Murray, 2008. "Regime Switching, Learning, and the Great Moderation," CAEPR Working Papers 2008-011, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    • Handle: RePEc:inu:caeprp:2008011
    as

    Download full text from publisher

    File URL: https://caepr.indiana.edu/RePEc/inu/caeprp/caepr2008-011.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giorgio E. Primiceri, 2006. "Why Inflation Rose and Fell: Policy-Makers' Beliefs and U. S. Postwar Stabilization Policy," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(3), pages 867-901.
    2. Thomas A. Lubik & Frank Schorfheide, 2004. "Testing for Indeterminacy: An Application to U.S. Monetary Policy," American Economic Review, American Economic Association, vol. 94(1), pages 190-217, March.
    3. Alejandro Justiniano & Giorgio E. Primiceri, 2008. "The Time-Varying Volatility of Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 98(3), pages 604-641, June.
    4. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. Raf Wouters & Sergey Slobodyan, 2007. "Learning dynamics in an estimated medium-sized DSGE model," 2007 Meeting Papers 689, Society for Economic Dynamics.
    7. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    8. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    9. Jeffrey C. Fuhrer, 2000. "Habit Formation in Consumption and Its Implications for Monetary-Policy Models," American Economic Review, American Economic Association, vol. 90(3), pages 367-390, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Agustín Arias & Markus Kirchner, 2019. "Shifting Inflation Expectations and Monetary Policy," Working Papers Central Bank of Chile 829, Central Bank of Chile.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    2. James Murray, 2008. "Regime Switching, Learning, and the Great Moderation," Caepr Working Papers 2008-011, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    3. Alejandro Justiniano & Giorgio E. Primiceri & Andrea Tambalotti, 2013. "The Effects of the Saving and Banking Glut on the U.S. Economy," NBER Chapters, in: NBER International Seminar on Macroeconomics 2013, pages 52-67, National Bureau of Economic Research, Inc.
    4. Olivier Coibion & Yuriy Gorodnichenko, 2011. "Monetary Policy, Trend Inflation, and the Great Moderation: An Alternative Interpretation," American Economic Review, American Economic Association, vol. 101(1), pages 341-370, February.
    5. James Murray, 2008. "Initial Expectations in New Keynesian Models with Learning," CAEPR Working Papers 2008-017, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Murray, James, 2011. "Learning and judgment shocks in U.S. business cycles," MPRA Paper 29257, University Library of Munich, Germany.
    7. Gbaguidi DAVID, 2011. "Expectations Impact On The Effectiveness Of The Inflation-Real Activity Trade-Off," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 2(2), pages 141-181.
    8. James Murray, 2008. "Initial Expectations in New Keynesian Models with Learning," Caepr Working Papers 2008-017, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    9. Gbaguidi, David Sedo, 2011. "Regime Switching in a New Keynesian Phillips Curve with Non-zero Steady-state Inflation Rate," MPRA Paper 35481, University Library of Munich, Germany.
    10. Cole, Stephen J., 2020. "The influence of learning and price-level targeting on central bank forward guidance," Journal of Macroeconomics, Elsevier, vol. 65(C).
    11. Kostas Mavromatis, 2018. "U.S. Monetary Regimes and Optimal Monetary Policy in the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(7), pages 1441-1478, October.
    12. Castelnuovo, Efrem & Nisticò, Salvatore, 2010. "Stock market conditions and monetary policy in a DSGE model for the U.S," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1700-1731, September.
    13. Gáti, Laura, 2023. "Monetary policy & anchored expectations—An endogenous gain learning model," Journal of Monetary Economics, Elsevier, vol. 140(S), pages 37-47.
    14. Cole, Stephen J., 2018. "The effectiveness of central bank forward guidance under inflation and price-level targeting," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 146-161.
    15. Fabio Milani, 2009. "Adaptive Learning and Macroeconomic Inertia in the Euro Area," Journal of Common Market Studies, Wiley Blackwell, vol. 47(3), pages 579-599, June.
    16. George W. Evans & Seppo Honkapohja, 2009. "Expectations, Learning and Monetary Policy: An Overview of Recent Research," Central Banking, Analysis, and Economic Policies Book Series, in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.),Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 2, pages 027-076, Central Bank of Chile.
    17. Benoit Mojon, 2007. "Monetary policy, output composition and the Great Moderation," Working Paper Series WP-07-07, Federal Reserve Bank of Chicago.
    18. Lhuissier, Stéphane, 2018. "The Regime-Switching Volatility Of Euro Area Business Cycles," Macroeconomic Dynamics, Cambridge University Press, vol. 22(2), pages 426-469, March.
    19. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
    20. Nlemfu Mukoko, Jean Blaise, 2016. "On the Welfare Costs of Monetary Policy," MPRA Paper 72479, University Library of Munich, Germany, revised Jul 2016.

    More about this item

    Keywords

    Learning; regime switching; great moderation; New Keynesian model; maximum likelihood;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inu:caeprp:2008011. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Center for Applied Economics and Policy Research (email available below). General contact details of provider: https://edirc.repec.org/data/caeprus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.