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G-7 Inflation Forecasts

  • Canova, Fabio

This Paper compares the forecasting performance of some leading models of inflation for the cross section of G-7 countries. We show that bivariate and trivariate models suggested by economic theory or statistical analysis are hardly better than univariate models. Phillips curve specifications fit well into this class. Significant improvements in both the MSE of the forecasts and turning point prediction are obtained with time-varying coefficients models that exploit international interdependencies. The performance of the latter class of models is independent of the sample, while it is not the case for standard specifications.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 3283.

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Date of creation: Mar 2002
Date of revision:
Handle: RePEc:cpr:ceprdp:3283
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  17. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
  18. Andrew Atkeson & Lee E. Ohanian., 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  19. Canova, Fabio, 1992. "An Alternative Approach to Modeling and Forecasting Seasonal Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 97-108, January.
  20. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-95, November.
  21. Canova, Fabio, 1993. "Modelling and forecasting exchange rates with a Bayesian time-varying coefficient model," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 233-261.
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