On the Power of the Blus Procedure

A New Test for Autocorrelated Errors in the Linear Regression Model

“…In every case given the size of the test was 0·05. Details of the method used for calculating the significance points and powers are given in Koerts and Abrahamse (1968).…”

“…Unfortunately the distribution of d depends upon the design matrix used; however, they were further able to show that the significance points of the distribution have upper and lower bounds, and later published tables of these bounds (Durbin and Watson, 1951). Koerts and Abrahamse (1968), using results due to Imhof (1961), gave a method for calculating the exact significancepoints of statistics of the type proposed by Anderson and adopted by Durbin and Watson (i.e. ratios of quadratic forms in normal variables) for any particular design matrix.…”

On Theil's errors

“…When testing against heteroskedasticity, choose the middle k observations; when testing against first-order autocorrelation, choose the first k observations or the last k or a mixture of the two. Improvements and extensions of Theil's work on BLUS residuals can be found in Koerts (1967), Putter (1967), Koerts and Abrahamse (1968), Abrahamse and Koerts (1971) and others.…”

Knowledge Discovery in Spatial Data

“…Furthermore, the standard assumption of constant variance of the disturbance terms may fail to hold in the presence of spatial autocorrelation (Cliff andOrd 1973, 1981;Krämer and Donninger 1987;Anselin 1988;Griffith 1988;Anselin and Griffith 1988;Cordy and Griffith 1993). Recently, based on the theoretical results by Imhof (1961) and the algebraic results by Koerts and Abrahamse (1968), Boots (1995, with corrections 1996), as well as Hepple (1998) have independently derived the exact distributions of Moran's I 0 and Geary's C for the OLR residuals under the null hypothesis of no spatial autocorrelation among the normally distributed disturbances. Heteroscedasticity in the disturbances caused by spatial autocorrelation thus makes such testing methods invalid.…”