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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Improving weighted least squares inference
Organization Unit
Authors
  • Cyrus J DiCiccio
  • Joseph P Romano
  • Michael Wolf
Language
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 232
ISSN 1664-7041
Number of Pages 43
Date 2017
Abstract Text These days, it is common practice to base inference about the coefficients in a hetoskedastic linear model on the ordinary least squares estimator in conjunction with using heteroskedasticity consistent standard errors. Even when the true form of heteroskedasticity is unknown, heteroskedasticity consistent standard errors can also used to base valid inference on a weighted least squares estimator and using such an estimator can provide large gains in efficiency over the ordinary least squares estimator. However, intervals based on asymptotic approximations with plug-in standard errors often have coverage that is below the nominal level, especially for small sample sizes. Similarly, tests can have null rejection probabilities that are above the nominal level. In this paper, it is shown that under unknown hereroskedasticy, a bootstrap approximation to the sampling distribution of the weighted least squares estimator is valid, which allows for inference with improved finite-sample properties. For testing linear constraints, permutations tests are proposed which are exact when the error distribution is symmetric and is asymptotically valid otherwise. Another concern that has discouraged the use of weighting is that the weighted least squares estimator may be less efficient than the ordinary least squares estimator when the model used to estimate the unknown form of the heteroskedasticity is misspecified. To address this problem, a new estimator is proposed that is asymptotically at least as efficient as both the ordinary and the weighted least squares estimator. Simulation studies demonstrate the attractive finite-sample properties of this new estimator as well as the improvements in performance realized by bootstrap confidence intervals.
Official URL http://www.econ.uzh.ch/static/wp/econwp232.pdf
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Keywords Bootstrap, conditional heteroskedasticity, HC standard errors, Bootstrap-Statistik, Heteroskedastizität, Gewichtung
Additional Information Revised version