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Macroeconomic forecasting and structural change
Author(s) -
D'Agostino Antonello,
Gambetti Luca,
Gian Domenico
Publication year - 2011
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1257
Subject(s) - econometrics , vector autoregression , economics , inflation (cosmology) , stochastic volatility , volatility (finance) , random walk , interest rate , unemployment , sample (material) , autoregressive model , inflation rate , macroeconomics , statistics , mathematics , physics , chemistry , chromatography , theoretical physics
SUMMARY The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.