New Methods for Forecasting Inflation, Applied to the US


  • The authors acknowledge funding support from the Economic and Social Research Council, UK (grant RES-000-22-2066). This research was supported in part by the Open Society Foundation and the Oxford Martin School. Comments by Chris Carroll, Kevin Lansing, Roland Meeks, John Williams and other seminar participants at the San Francisco Federal Reserve are gratefully acknowledged. We are grateful to Mark Watson for advice and to Markus Eberhardt for helping us implement Gauss software.


Models for the 12-month-ahead US rate of inflation, measured by the chain-weighted consumer expenditure deflator, are estimated for 1974–98 and subsequent pseudo out-of-sample forecasting performance is examined. Alternative forecasting approaches for different information sets are compared with benchmark univariate autoregressive models, and substantial out-performance is demonstrated including against Stock and Watson's unobserved components-stochastic volatility model. Three key ingredients to the out-performance are: including equilibrium correction component terms in relative prices; introducing nonlinearities to proxy state-dependence in the inflation process and replacing the information criterion, commonly used in VARs to select lag length, with a ‘parsimonious longer lags’ parameterization. Forecast pooling or averaging also improves forecast performance.