Locally Robust Tests for Serial Correlation in Least Squares Regression
The Annals of Statistics · 1980 · Volume 8 · Issue 5
Abstract
Kariya and Eaton and Kariya studied a robustness property of the usual tests for serial correlation against departure from normality. When the results were applied to a regression model $y = X \beta + u(X: nxk),$ it was assumed that the column space of $X$ is spanned by some $k$ latent vectors of the covariance matrix of error term $u.$ In this paper we delete this assumption and in a much broader class of distributions derive a locally best invariant test for a one-sided problem and a locally best unbiased and invariant test for a two-sided problem. The null distributions of these tests are the same as those under normality.
Keywords
62F05, 62G10, 62G35, 62J05, Durbin-Watson test, Invariance, LBI test, LBUI test, least squares regression, robustness, serial correlation
Enable JavaScript for citation tools and additional paper interactions.