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Full-Text Articles in Social and Behavioral Sciences
Nonlinear Cointegrating Power Function Regression With Endogeneity, Zhishui Hu, Peter C.B. Phillips, Qiying Wang
Nonlinear Cointegrating Power Function Regression With Endogeneity, Zhishui Hu, Peter C.B. Phillips, Qiying Wang
Cowles Foundation Discussion Papers
This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. Accompanying the asymptotic theory of nonlinear regression, the paper establishes some new results on weak convergence to stochastic integrals that go beyond the usual semi-martingale structure and considerably extend existing limit theory, complementing other recent findings on stochastic integral asymptotics. The paper also provides a general framework for extremum estimation limit theory that …
On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, Patrick J. Rosopa, Alice M. Brawley, Theresa P. Atkinson, Stephen A. Robertson
On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, Patrick J. Rosopa, Alice M. Brawley, Theresa P. Atkinson, Stephen A. Robertson
Journal of Modern Applied Statistical Methods
Preliminary tests for homoscedasticity may be unnecessary in general linear models. Based on Monte Carlo simulations, results suggest that when testing for differences between independent slopes, the unconditional use of weighted least squares regression and HC4 regression performed the best across a wide range of conditions.
Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox
Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox
Journal of Modern Applied Statistical Methods
There is a substantial collection of robust analysis of covariance (ANCOVA) methods that effectively deals with non-normality, unequal population slope parameters, outliers, and heteroscedasticity. Some are based on the usual linear model and others are based on smoothers (nonparametric regression estimators). However, extant results are limited to one or two covariates. A minor goal here is to extend a recently-proposed method, based on the usual linear model, to situations where there are up to six covariates. The usual linear model might provide a poor approximation of the true regression surface. The main goal is to suggest a method, based on …