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

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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.

Suggested Citation

  • Silvia Goncalves & Michael W. McCracken & Yongxu Yao, 2023. "Bootstrapping out-of-sample predictability tests with real-time data," Working Papers 2023-029, Federal Reserve Bank of St. Louis, revised 03 Sep 2024.
    • Handle: RePEc:fip:fedlwp:97409
    DOI: 10.20955/wp.2023.029
    Note: Publisher DOI: https://doi.org/10.1016/j.jeconom.2024.105916
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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