Title(题名):  Forecasting Macroeconomic Variables Under Model Instability
Authors(作者):  Davide Pettenuzzo and Allan Timmermann
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
 April 2017,Vol35,Issue2
Pages(页码):  p183-201
ISSN:  0735-0015
Abstract(摘要):  We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. gross domestic product (GDP) growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model, which is a specification that allows for time-varying parameters and stochastic volatility. Supplementary materials for this article are available online.
Key words(关键词):   Change-point models; GDP growth forecasts; Inflation forecasts; Regime switching; Stochastic volatility’, Time-varying parameters.
Where(馆藏地):  304外文报刊阅览室
Available online
 2017/12/28 14:19:04
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