Macroeconomic Forecasting Using Diffusion Indexes
2002; Taylor & Francis; Volume: 20; Issue: 2 Linguagem: Inglês
10.1198/073500102317351921
ISSN1537-2707
AutoresJames H. Stock, Mark W. Watson,
Tópico(s)Market Dynamics and Volatility
ResumoThis article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, and 24-monthahead forecasts for eight monthly U.S. macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998. During this sample period these new forecasts outperformed univariate autoregressions, small vector autoregressions, and leading indicator models.
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