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G-7 Inflation Forecasts: Random Walk, Phillips Curve Or What Else?

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  • CANOVA, FABIO

Abstract

This paper compares the forecasting performance of some leading models of inflation for G-7 countries. We show that bivariate and trivariate models suggested by economic theory or statistical analysis are not much better than univariate ones. Phillips curve specifications fit well into this class. Improvements in both the MSE of the forecasts and turning point prediction are obtained with time-varying coefficients models, which exploit international interdependencies. The performance of the latter class of models is stable throughout the 1990s.

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Bibliographic Info

Article provided by Cambridge University Press in its journal Macroeconomic Dynamics.

Volume (Year): 11 (2007)
Issue (Month): 01 (February)
Pages: 1-30

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Handle: RePEc:cup:macdyn:v:11:y:2007:i:01:p:1-30_05

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Cited by:
  1. Samad Sarferaz & Francesco Furlanetto & Francesco Furlanetto, 2014. "Identification of Financial Factors in Economic Fluctuations," KOF Working papers 14-364, KOF Swiss Economic Institute, ETH Zurich.
  2. Dandan Liu & Dennis Jansen, 2011. "Does a factor Phillips curve help? An evaluation of the predictive power for U.S. inflation," Empirical Economics, Springer, vol. 40(3), pages 807-826, May.
  3. Rumler, Fabio & Valderrama, Maria Teresa, 2010. "Comparing the New Keynesian Phillips Curve with time series models to forecast inflation," The North American Journal of Economics and Finance, Elsevier, vol. 21(2), pages 126-144, August.
  4. Kitov, Ivan & KItov, Oleg, 2013. "Does Banque de France control inflation and unemployment?," MPRA Paper 50239, University Library of Munich, Germany.
  5. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
  6. Steffen Ahrens & Matthias Hartmann, 2014. "State-dependence vs. Time-dependence: An Empirical Multi-Country Investigation of Price Sluggishness," Kiel Working Papers 1907, Kiel Institute for the World Economy.
  7. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, 01.
  8. Mubariz Hasanov & Aysen Arac & Funda Telatar, 2012. "Nonlinearity and Structural Stability in the Phillips Curve: Evidence from Turkey," Hacettepe University Department of Economics Working Papers 20123, Hacettepe University, Department of Economics.
  9. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
  10. Hartmann, Matthias & Herwartz, Helmut, 2012. "Causal relations between inflation and inflation uncertainty—Cross sectional evidence in favour of the Friedman–Ball hypothesis," Economics Letters, Elsevier, vol. 115(2), pages 144-147.
  11. Nyberg , Henri & Saikkonen, Pentti, 2012. "Forecasting with a noncausal VAR model," Research Discussion Papers 33/2012, Bank of Finland.

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