Bootstrap prediction intervals for factor models
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, Bai and Ng (2006) proposed the construction of asymptotic intervals
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 OLS estimator as shown in a recent paper by
Gonçalves and Perron (2014).
[ - ]
