Uncovering Regimes in Out of Sample Forecast Errors from Predictive Regressions
Anibal Emiliano Da Silva Neto,
Jesus Gonzalo and
Oxford Bulletin of Economics and Statistics, 2021, vol. 83, issue 3, 713-741
We introduce a set of test statistics for assessing the presence of regimes in out of sample forecast errors produced by recursively estimated linear predictive regressions that can accommodate multiple highly persistent predictors. Our test statistics are designed to be robust to the chosen starting window size and are shown to be both consistent and locally powerful. Their limiting null distributions are also free of nuisance parameters and hence robust to the degree of persistence of the predictors. Our methods are subsequently applied to the predictability of the value premium whose dynamics are shown to be characterized by state dependence.
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Working Paper: Uncovering regimes in out of sample forecast errors from predictive regressions (2020)
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