Advances in Econometrics: Kernel estimators of regression functions

By H. Bierens. 1987

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Citations in the corpus

Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models (2006, background): “It should be mentioned that Ahmad and Cerrito (1994) and Bierens (1983, 1987) also consider the case of estimating a regression function with mixed categorical and continuous variables, but they did not study the theoretical properties of the resulting estimator when using datadriven methods such…”
Identification and Estimation of Regression Models with Misclassification (2006, unknown): “The first proof is a direct application of Theorem 3.2.1 (and the Cramer-Wold device) in Bierens (1987) and the conditions 10-14 in the text are precisely his conditions 3.2.1 on p.118.”
A Kernel Test for Neglected Nonlinearity (2012, methodology): “Relying upon the work of Bierens (1982, 1987, 1990) and described in detail in Bradley and McClelland (1994), this test is consistent against all deviations from the null hypothesis and has the greatest asymptotic power of all tests in the set of conditional moment tests, in the sense that the…”
Local nonlinear least squares: Using parametric information in nonparametric regression (2000, background): “Bierens ( 1987 ) and HH ardle ( 1990 ) ? see Lemma CLT in Appendix A .”
Nonparametric Kernel Estimation for Semiparametric Models (1995, methodology): “The condition on ^ ( r ) is slightly stronger than the condition used by Bierens (1983, 1987) for the case where n = 0, because we allow for data-dependent bandwidth parameters.”
Semiparametric Estimation of First-Price Auctions with Risk Averse Bidders* (2011, unknown): “See also Bierens (1987).”
An equality test across nonparametric regressions (2001, background): “Indeed, a discrete variable does not a ect the rate of convergence of nonparametric estimators and does not create any bias in estimation, see e.g. Bierens (1987) and Delgado and Mora (1995).”
Two-step estimation of semiparametric censored regression models (2001, methodology): “Thus the method for constructing \higher order" kernels described in Bierens (1987) would be valid in this context as well.”