Jesus Gonzalo, Jean-Yves Pitarakis
arXiv 6 Feb 2023 · Econometrics · publishedInternational Journal of Forecasting (2023) · 2 citations (OpenAlex)
arXiv:2302.02866 · PDF · DOI · OpenAlex · Extracted main text
This paper is concerned with detecting the presence of out of sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out of sample MSE comparisons that is implemented in a pairwise manner using one predictor at a time and resulting in an aggregate test statistic that is standard normally distributed under the global null hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary or a combination of both. Upon rejection of the null hypothesis we subsequently introduce a predictor screening procedure designed to identify the most active predictors. An empirical application to key predictors of US economic activity illustrates the usefulness of our methods and highlights the important forward looking role played by the series of manufacturing new orders.
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods | 0.405 | 1 | 1 |
| 2 | Direct Multi-Step Forecast based Comparison of Nested Models via an Encompassing Test | 0.405 | 1 | 1 |