Title(题名):  Bootstrap Prediction Intervals for Factor Models
Authors(作者):  Silvia Gon?alves, Benoit Perron, and Antoine Djogbenou
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
Pages(页码):  p53-69
ISSN:  0735-0015
Abstract(摘要):  We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the innovations. The bootstrap allows us to relax this assumption and to construct valid prediction intervals under more general conditions. Moreover, even under Gaussianity, the bootstrap leads to more accurate intervals in cases where the cross-sectional dimension is relatively small as it reduces the bias of the ordinary least-squares (OLS) estimator.
Key words(关键词):  Bootstrap; Conditional mean; Factor model; Forecast.
Where(馆藏地):  304外文报刊阅览室
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