Heteroskedasticity-Robust Tests for Structural Change
نویسنده
چکیده
It is remarkably easy to test for structural change, of the type that the classic F or “Chow” test is designed to detect, in a manner that is robust to heteroskedasticity of possibly unknown form. This paper first discusses how to test for structural change in nonlinear regression models by using a variant of the Gauss-Newton regression. It then shows how to make these tests robust to heteroskedasticity of unknown form and discusses several related procedures for doing so. Finally, it presents the results of a number of Monte Carlo experiments designed to see how well the new tests perform in finite samples. This research was supported, in part, by the Social Sciences and Humanities Research Council of Canada. I am grateful to Allan Gregory and Simon Power for helpful comments on an earlier draft. This paper was published in the Empirical Economics, 14, 1989, 77–92.
منابع مشابه
Fractional Dickey-Fuller tests under heteroskedasticity
In a recent paper, Dolado, Gonzalo and Mayoral (2002) introduce a fractional Dickey-Fuller (FD-F) t-statistic for testing a unit root against the alternative of a mean reverting fractional unit root process. This t-statistic is based on the assumption that the errors are unconditionally homoskedastic. However, Busetti and Taylor (2003), McConnell and Perez-Quiros (2000), and van Dijk et al. (20...
متن کاملTesting for Weak-form Efficiency of Crude Palm Oil Spot and Futures Markets: New Evidence from a GARCH Unit Root Test with Multiple Structural Breaks
There is a sizeable literature that tests for weak-form efficiency in commodity and energy spot and futures prices. While many studies now allow for multiple structural breaks to address the criticism that conventional unit root tests have low power to reject the unit root null in the presence of structural change, the extant literature overlooks the fact that conventional unit root tests are b...
متن کاملHeteroskedasticity-robust inference in finite samples
Since the advent of heteroskedasticity-robust standard errors, several papers have proposed adjustments to the original White formulation. We replicate earlier ndings that each of these adjusted estimators performs quite poorly in nite samples. We propose a class of alternative heteroskedasticity-robust tests of linear hypotheses based on an Edgeworth expansions of the test statistic distributi...
متن کاملRobust Tests for Heteroskedasticity and Autocorrelation Using Score Function
The standard Lagrange multiplier test for heteroskedasticity was originally developed assuming normality of the disturbance term see Godfrey (1978b), and Breush and Pagan (1979)]. Therefore, the resulting test depends heavily on the normality assumption. Koenker (1981) suggests a studentized form which is robust to nonnormality. This approach seems to be limited because of the unavailability of...
متن کاملExact optimal inference in regression models under heteroskedasticity and non-normality of unknown form
Simple point-optimal sign-based tests are developed for inference on linear and nonlinear regression models with non-Gaussian heteroskedastic errors. The tests are exact, distribution-free, robust to heteroskedasticity of unknown form, and may be inverted to build confidence regions for the parameters of the regression function. Since point-optimal sign tests depend on the alternative hypothesi...
متن کامل