Vector autoregressions and reality
AbstractThe statistical significance of variance decompositions and impulse response functions for unrestricted vector autoregressions is questionable. Most previous studies are suspect because they have not provided confidence intervals for variance decompositions and impulse response functions. Here two methods of computing such intervals are developed, one using a normal approximation, the other using bootstrapped resampling. An example from Sims’ work illustrates the importance of computing these confidence intervals. In the example, the 95 percent confidence intervals for variance decompositions span up to 66 percentage points at that usual forecasting horizon.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Minneapolis in its series Staff Report with number 107.
Date of creation: 1987
Date of revision:
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- 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.
- Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
- Fair, Ray C, 1979.
"An Analysis of the Accuracy of Four Macroeconometric Models,"
Journal of Political Economy,
University of Chicago Press, vol. 87(4), pages 701-18, August.
- Ray C. Fair, 1978. "An Analysis of the Accuracy of Four Macroeconometric Models," Cowles Foundation Discussion Papers 492, Cowles Foundation for Research in Economics, Yale University.
- Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
- Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
- Stanley Fischer, 1981. "Relative Shocks, Relative Price Variability, and Inflation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 12(2), pages 381-442.
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