Independent Stepwise Residuals for Testing Homoscedasticity

Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals

‘Classical regression analysis’ assumes the normality (N), homoscedasticity (H) and serial independence (I) of regression residuals. Violation of the normality assumption may lead the investigator to inaccurate inferential statements. Recently, tests for normality have been derived for the case of homoscedastic serially independent (HZ) residuals [e.g., White and Macdonald (1980)]. Similarly, t...

Transformations to Symmetry and Homoscedasticity

This paper considers transformations in regression to eliminate skewness and heteroscedasticity of the response. We work with the "transform-both-sides" model where the relationship between the 'lledian response and the independent varibles has been identified, at least tentatively. To preserve this relationship, the response and the regression model are transformed in the same way. Extending t...

Stepwise Multiple Testing as Formalized Data Snooping

It is common in econometric applications that several hypothesis tests are carried out at the same time. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. This paper suggests a stepwise multiple testing procedure which asymptotically controls the familywise error rate at a desired level. Compared to related singlestep methods, the procedur...

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson'...

On Flexible Tests of Independence and homoscedasticity