Bootstrap Prediction Intervals for Factor Models |
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Authors: | Sílvia Gonçalves Benoit Perron Antoine Djogbenou |
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Institution: | 1. Department of Economics, University of Western Ontario, London, Ontario, Canada, N6A 5C2 (sgoncal9@uwo.ca);2. Département de Sciences économiques, CIREQ and CIRANO, Université de Montréal, Montreal, H3T 1J4, QC, Canada (Benoit.perron@umontreal.ca);3. Département de Sciences économiques, Université de Montréal and CIREQ, Montreal, H3C 3J7, QC, Canada (antoine.djogbenou@umontreal.ca) |
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Abstract: | 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. |
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Keywords: | Bootstrap Conditional mean Factor model Forecast |
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