Premium
New Methods for Forecasting Inflation, Applied to the US *
Author(s) -
Aron Janine,
Muellbauer John
Publication year - 2013
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.2012.00728.x
Subject(s) - econometrics , univariate , pooling , autoregressive model , economics , inflation (cosmology) , volatility (finance) , stock (firearms) , statistics , computer science , mathematics , multivariate statistics , mechanical engineering , physics , artificial intelligence , theoretical physics , engineering
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.