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

Silvia Goncalves, Michael McCracken and Yongxu Yao ()
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Yongxu Yao: https://www.mcgill.ca/economics/node/42/target%3D

No 2023-029, Working Papers from Federal Reserve Bank of St. Louis

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.

Keywords: real-time data; bootstrap; prediction; inference (search for similar items in EconPapers)
JEL-codes: C12 C52 C53 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2023-11-30, Revised 2024-09-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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DOI: 10.20955/wp.2023.029

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