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Macroeconomic switching

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  • Christopher Sims
  • Tao Zha

Abstract

We discuss the results of fitting a 6-variable structural VAR in which we allow for certain types of parameter variation over time. Allowing structural equation variances to change over time is extremely important in improving fit. Allowing the coefficients that define the model’s dynamics to change is less important to improving fit, though models with changing parameters are consistent with the data. We pay special attention to a version of the model that allows the monetary policy rule, but not other parts of the model, to show changing coefficients. Results from this model fit some aspects of conventional wisdom about changes in monetary policy over time, but imply that the changes in policy have been more subtle than dramatic. We construct counterfactual histories for the early 1980’s, suppressing the “Volcker regime” in monetary policy. We find a steadier decline in inflation and a smaller recession earlier in the period, but slower growth later, than actually occurred.

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Bibliographic Info

Article provided by Federal Reserve Bank of San Francisco in its journal Proceedings.

Volume (Year): (2002)
Issue (Month): Mar ()
Pages:

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Handle: RePEc:fip:fedfpr:y:2002:i:mar:x:6

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References

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  1. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Working Paper 0107, Federal Reserve Bank of Cleveland.
  2. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  3. Eric M. Leeper & Tao Zha, 2002. "Modest Policy Interventions," NBER Working Papers 9192, National Bureau of Economic Research, Inc.
  4. Clarida, Richard & Galí, Jordi & Gertler, Mark, 1998. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," CEPR Discussion Papers 1908, C.E.P.R. Discussion Papers.
  5. Timothy Cogley & Thomas Sargent, . "Evolving Post-World War II U.S. Inflation Dynamics," Working Papers 2132872, Department of Economics, W. P. Carey School of Business, Arizona State University.
  6. 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 357-84, March.
  7. Eric Leeper & Tao Zha, 2002. "Empirical analysis of policy interventions," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  8. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-68, November.
  9. Michael S. Hanson, 2006. "Varying Monetary Policy Regimes: A Vector Autoregressive Investigation," Wesleyan Economics Working Papers 2006-003, Wesleyan University, Department of Economics.
  10. Rochelle M. Edge, 2000. "Time-to-build, time-to-plan, habit-persistence, and the liquidity effect," International Finance Discussion Papers 673, Board of Governors of the Federal Reserve System (U.S.).
  11. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 349-366, November.
  12. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
  13. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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