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Macroeconomic switching

  • Christopher Sims
  • Tao Zha

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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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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  1. 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, June.
  2. Eric M. Leeper & Tao Zha, 2002. "Modest Policy Interventions," NBER Working Papers 9192, National Bureau of Economic Research, Inc.
  3. Richard Clarida & Jordi Gali & Mark Gertler, 1998. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," NBER Working Papers 6442, National Bureau of Economic Research, Inc.
  4. 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.
  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. Hanson, Michael S., 2006. "Varying monetary policy regimes: A vector autoregressive investigation," Journal of Economics and Business, Elsevier, vol. 58(5-6), pages 407-427.
  7. 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.
  8. 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.
  9. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
  10. 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.
  11. Eric M. Leeper & Tao Zha, 2002. "Empirical Analysis of Policy Interventions," NBER Working Papers 9063, National Bureau of Economic Research, Inc.
  12. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper 96-13, Federal Reserve Bank of Atlanta.
  13. 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.).
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