Bootstrapping out-of-sample predictability tests with real-time data

Abstract

In this paper we develop a block bootstrap approach to out-of-sample inference when real-time data are used to produce forecasts. In particular, we establish its first-order asymptotic validity for West-type (1996) tests of predictive ability in the presence of regular data revisions. This allows the user to conduct asymptotically valid inference without having to estimate the asymptotic variances derived in Clark and McCracken’s (2009) extension of West (1996) when data are subject to revision. Monte Carlo experiments indicate that the bootstrap can provide satisfactory finite sample size and power even in modest sample sizes. We conclude with an application to inflation forecasting that revisits the results in Ang et al. (2007) in the presence of real-time data.