Breakdown point theory for implied probability bootstrap
AbstractThis paper studies robustness of bootstrap inference methods under moment conditions. In particular, we compare the uniform weight and implied probability bootstraps by analyzing behaviors of the bootstrap quantiles when outliers take arbitrarily large values, and derive the breakdown points for those bootstrap quantiles. The breakdown point properties characterize the situation where the implied probability bootstrap is more robust than the uniform weight bootstrap against outliers. Simulation studies illustrate our theoretical findings.
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Bibliographic InfoArticle provided by Royal Economic Society in its journal Econometrics Journal.
Volume (Year): 15 (2012)
Issue (Month): 1 (02)
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Other versions of this item:
- Lorenzo Camponovo & Taisuke Otsu, 2011. "Breakdown Point Theory for Implied Probability Bootstrap," Cowles Foundation Discussion Papers 1793, Cowles Foundation for Research in Economics, Yale University.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
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- Lorenzo Camponovo & Taisuke Otsu, 2011.
"Robustness of Bootstrap in Instrumental Variable Regression,"
Cowles Foundation Discussion Papers
1796, Cowles Foundation for Research in Economics, Yale University.
- Lorenzo Camponovo & Taisuke Otsu, 2014. "Robustness of bootstrap in instrumental variable regression," STICERD - Econometrics Paper Series /2014/572, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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