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Conditional forecasts in dynamic multivariate models

  • Daniel F. Waggoner
  • Tao Zha

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are provided to show the use of these methods.

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Paper provided by Federal Reserve Bank of Atlanta in its series FRB Atlanta Working Paper No. with number 98-22.

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Date of creation: 1998
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Handle: RePEc:fip:fedawp:98-22
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  1. Francis X. Diebold & Peter F. Christoffersen, 1997. "Cointegration and Long-Horizon Forecasting," IMF Working Papers 97/61, International Monetary Fund.
  2. Francis X. Diebold, 1997. "The Past, Present, and Future of Macroeconomic Forecasting," NBER Working Papers 6290, National Bureau of Economic Research, Inc.
  3. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
  4. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  5. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148 Elsevier.
  6. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
  7. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
  8. Miller, Preston J & Roberds, William T, 1991. "The Quantitative Significance of the Lucas Critique," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 361-87, October.
  9. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, . "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
  10. Christopher A. Sims, 1982. "Policy Analysis with Econometric Models," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 13(1), pages 107-164.
  11. Christopher A. Sims & Tao Zha, 1995. "Error bands for impulse responses," Working Paper 95-6, Federal Reserve Bank of Atlanta.
  12. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, March.
  13. Daniel F. Waggoner & Tao Zha, 1997. "Normalization, probability distribution, and impulse responses," Working Paper 97-11, Federal Reserve Bank of Atlanta.
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