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Automated Forecasts of Asia-Pacific Economic Activity

This paper reports quarterly ex ante forecasts of macroeconomic activity for the U.S.A., Japan and Australia for the period 1995-1997. The forecasts are based on automated time series models of vector autoregressions (VAR's), reduced rank regressions (RRR's), error correction models (ECM's) and Bayesian vector autoregressions (BVAR's). The models are automated by using an asymptotic predictive form of the model selection criterion PIC to determine autoregressive lag order, cointegrating rank and trend degree in the VAR's, RRR's, and ECM's. The same criterion is used to find optimal values of the hyperparameters in the BVAR's. The forecasts are graphed and tabulated. In the case of the U.S.A., the results are compared with forecasts from the Fair model, a structural econometric model of the U.S. economy.

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File URL: http://cowles.econ.yale.edu/P/cd/d11a/d1103.pdf
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Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1103.

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Length: 15 pages
Date of creation: Jun 1995
Date of revision:
Publication status: Published in Asia Pacific Economic Review (1996), 1: 92-102
Handle: RePEc:cwl:cwldpp:1103
Contact details of provider: Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA
Phone: (203) 432-3702
Fax: (203) 432-6167
Web page: http://cowles.econ.yale.edu/

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Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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  1. 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.
  2. Peter C.B. Phillips, 1992. "Bayes Methods for Trending Multiple Time Series with an Empirical Application to the US Economy," Cowles Foundation Discussion Papers 1025, Cowles Foundation for Research in Economics, Yale University.
  3. Phillips, Peter C B & Ploberger, Werner, 1996. "An Asymptotic Theory of Bayesian Inference for Time Series," Econometrica, Econometric Society, vol. 64(2), pages 381-412, March.
  4. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
  5. Phillips, Peter C.B. & Ploberger, Werner, 1994. "Posterior Odds Testing for a Unit Root with Data-Based Model Selection," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 774-808, August.
  6. McNees, Stephen K, 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 23, January.
  7. Peter C.B. Phillips, 1994. "Model Determination and Macroeconomic Activity," Cowles Foundation Discussion Papers 1083, Cowles Foundation for Research in Economics, Yale University.
  8. Nicholas G. Polson & George C. Tiao (ed.), 1995. "Bayesian Inference," Books, Edward Elgar, volume 0, number 602, 6.
  9. Trevor, R G & Thorp, S J, 1988. "VAR Forecasting Models of the Australian Economy: A Preliminary Analysis," Australian Economic Papers, Wiley Blackwell, vol. 27(0), pages 108-20, Supplemen.
  10. Peter C.B. Phillips, 1992. "Bayes Models and Forecasts of Australian Macroeconomic Time Series," Cowles Foundation Discussion Papers 1024, Cowles Foundation for Research in Economics, Yale University.
  11. Peter C.B. Phillips, 1992. "Bayesian Model Selection and Prediction with Empirical Applications," Cowles Foundation Discussion Papers 1023, Cowles Foundation for Research in Economics, Yale University.
  12. McNees, Stephen K, 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 5-15, January.
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