Macroeconomic forecasting and structural change

Antonello D'Agostino, Luca Gambetti, Domenico Giannone

Research output: Contribution to journalArticleResearchpeer-review

118 Citations (Scopus)

Abstract

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. © 2011 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)82-101
JournalJournal of Applied Econometrics
Volume28
Issue number1
DOIs
Publication statusPublished - 1 Jan 2013

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