期刊
STATISTICAL SCIENCE
卷 34, 期 4, 页码 523-544出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/18-STS693
关键词
Ancillarity of regressors; misspecification; econometrics; sandwich estimator; bootstrap
资金
- NSF [DMS-14-06563]
In the early 1980s, Halbert White inaugurated a model-robust form of statistical inference based on the sandwich estimator of standard error. This estimator is known to be heteroskedasticity-consistent, but it is less well known to be nonlinearity-consistent as well. Nonlinearity, however, raises fundamental issues because in its presence regressors are not ancillary, hence cannot be treated as fixed. The consequences are deep: (1) population slopes need to be reinterpreted as statistical functionals obtained from OLS fits to largely arbitrary joint x-y distributions; (2) the meaning of slope parameters needs to be rethought; (3) the regressor distribution affects the slope parameters; (4) randomness of the regressors becomes a source of sampling variability in slope estimates of order 1/v N; (5) inference needs to be based on model-robust standard errors, including sandwich estimators or the x-y bootstrap. In theory, model-robust and model-trusting standard errors can deviate by arbitrary magnitudes either way. In practice, significant deviations between them can be detected with a diagnostic test.
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