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Working Paper
Bootstrapping out-of-sample predictability tests with real-time data
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 ...
Working Paper
Bootstrapping out-of-sample predictability tests with real-time data
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 ...
Working Paper
Out-of-Sample Inference with Annual Benchmark Revisions
This paper examines the properties of out-of-sample predictability tests evaluated with real-time data subject to annual benchmark revisions. The presence of both regular and annual revisions can create time heterogeneity in the moments of the real-time forecast evaluation function, which is not compatible with the standard covariance stationarity assumption used to derive the asymptotic theory of these tests. To cover both regular and annual revisions, we replace this standard assumption with a periodic covariance stationarity assumption that allows for periodic patterns of time ...