Bootstrapping factor models with cross sectional dependence
| Autor: | Benoit Perron, Sílvia Gonçalves |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics::Theory
Economics and Econometrics Covariance matrix Multivariate random variable Applied Mathematics 05 social sciences Matrix norm Estimator Context (language use) 01 natural sciences 010104 statistics & probability Bootstrapping (electronics) Resampling 0502 economics and business Consistent estimator Statistics Statistics::Methodology 0101 mathematics 050205 econometrics Mathematics |
| Zdroj: | Journal of Econometrics. 218:476-495 |
| ISSN: | 0304-4076 |
| Popis: | We consider bootstrap methods for factor-augmented regressions with cross sectional dependence among idiosyncratic errors. This is important to capture the bias of the OLS estimator derived recently by Goncalves and Perron (2014). We first show that a common approach of resampling cross sectional vectors over time is invalid in this context because it induces a zero bias. We then propose the cross-sectional dependent (CSD) bootstrap where bootstrap samples are obtained by taking a random vector and multiplying it by the square root of a consistent estimator of the covariance matrix of the idiosyncratic errors. We show that if the covariance matrix estimator is consistent in the spectral norm, then the CSD bootstrap is consistent, and we verify this condition for the thresholding estimator of Bickel and Levina (2008). Finally, we apply our new bootstrap procedure to forecasting inflation using convenience yields as recently explored by Gospodinov and Ng (2013). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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