Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-23T06:36:00.360Z Has data issue: false hasContentIssue false
  • English
  • Français

G-7 INFLATION FORECASTS: RANDOM WALK, PHILLIPS CURVE OR WHAT ELSE?

Published online by Cambridge University Press:  18 January 2007

FABIO CANOVA
FABIO CANOVA
Affiliation:
ICREA, Universitat Pompeu Fabra and CEPR
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

G-7 CountriesInflationPanel VAR ModelsMarkov Chain Monte Carlo Methods
Type
ARTICLES
Information
Macroeconomic Dynamics , Volume 11 , Issue 1 , February 2007 , pp. 1 - 30
Copyright
© 2007 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Atkeson A. and L. Ohanian 2001 Are Phillips Curves useful for forecasting inflation? Federal Reserve Bank of Minneapolis Quarterly Review 25 (1), 2– 11.Google Scholar
Benerjee A., M. Marcellino and I. Masten 2003 Leading Indicators for Euro Area Inflation and GDP Growth. CEPR Working Paper 3893.Google Scholar
Bai J. and S. Ng 2002 Approximate factor models. Econometrica 70, 191– 221.Google Scholar
Blinder A. 1997 Is there a core of practical macroeconomics we should all believe? American Economic Review 87, 240– 243.Google Scholar
Canova F. 1993a Forecasting a multitude of time series with common seasonal pattern. Journal of Econometrics 55, 173– 202.Google Scholar
Canova F. 1993b Modelling and forecasting exchange rates using a Bayesian time varying coef- ficient model. Journal of Economic Dynamics and Control 17, 233– 262.Google Scholar
Canova F. 2002 G-7 Inflation Forecast. ECB Working Paper 149.Google Scholar
Canova F. and M. Ciccarelli 2004 Forecasting and turning point prediction in a Bayesian panel VAR model. Journal of Econometrics 120, 327– 359.Google Scholar
Canova F. and M. Ciccarelli 2002 Panel Index VAR Models: Specification, Estimation, Testing and Leading Indicators. CEPR Working Paper 4033.Google Scholar
Cecchetti S., R. Chu and C. Steindel 2001 The unreliability of inflation indicators. Current Issues in Economics and Finance, Federal Reserve Bank of New York 6 (4), 1– 6.Google Scholar
Cristadoro R., M. Forni, L. Reichlin and G. Veronesi (Forthcoming) A core inflation model for the Euro area. Forthcoming, Journal of Money Banking and Credit.
Evans C. and D. Marshall 1998 The economic determinants of the term structure of nominal interest rates. Carnegie Rochester Conference Series in Public Policy 49, 53– 101.Google Scholar
Fama E. 1970 Efficient capital markets: A review of theory and empirical work. Journal of Finance 25, 383– 417.Google Scholar
Fuhrer J. and G. Moore 1995 Inflation persistence. Quarterly Journal of Economics 110, 127– 159.Google Scholar
Gali J. and M. Gertler 1999 Inflation dynamics: A structural econometric analysis. Journal of Monetary Economics 44, 195– 222.Google Scholar
Goodhart C. and B. Hoffman 2000 Asset prices and the conduct of monetary policy. LSE manuscript.Google Scholar
Granger C. and Y Yeon 2004 Thick modelling. Economic Modelling 21, 323– 343.Google Scholar
Holtz–Eakin D., W. Newey and H. Rosen 1988 Estimating vector autoregressions with panel data. Econometrica 56 (6), 1371– 1395.Google Scholar
Inoue A. and L. Kilian 2003 Bagging Time Series. Manuscript, University of North Carolina.
Ivanov V., and L. Kilian 2000 A Practitioner's Guide to Lag Order Selection for Vector Autoregressions. Manuscript, University of Michigan.
Lahiri K. and J. Moore 1991 Leading Economic Indicators: New Approaches and Forecasting Records. Chicago. University of Chicago Press.
Levin A. and J. Piger 2002 Is Inflation Persistence Intrinsic in Industrialized Countries? Federal Reserve Bank of Saint Louis Working Paper 2002–023.Google Scholar
Mankiw G. 2001 The inesorable trade-off between inflation and unemployment. Economic Journal 111, C45– 60.Google Scholar
Marcellino M. 2002 Instability and Non-linearity in the EMU. CEPR Working Paper 3312.Google Scholar
Marcellino M., J. Stock and M. Watson 2003 Macroeconomic forecasting in the Euro Area: Country-specific versus area-wide information. European Economic Review 49, 1– 17.Google Scholar
Plosser C. and G. Rouwenshort 1994 International term structure and real economic growth. Journal of Monetary Economics 33, 133– 156.Google Scholar
Stock J. and M. Watson 1996 Evidence on structural instability in macroeconomic time series relations. Journal of Business and Economic Statistics 14, 11– 30.Google Scholar
Stock J. and M. Watson 1999 Forecasting inflation. Journal of Monetary Economics 44, 293– 335.Google Scholar
Stock J. and M. Watson 2000 Forecasting output and inflation: The role of asset prices. Manuscript.Google Scholar
Stock J. and M. Watson 2002 Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics 20, 147– 162.Google Scholar
Wagonner D. and T. Zha 1999 Conditional forecasts in dynamic multivariate models. Review of Economics and Statistics 81, 1– 14.Google Scholar
Wright J. 2003 Forecasting US Inflation by Bayesian Model Averaging. Federal Reserve Board, International Finance Discussion Paper 780.Google Scholar