The authors show that the CUSUM test of the stability over time of the coefficients of a linear regression model, which is usually based on recursive residuals, can also be applied to ordinary least squares residuals. The authors derive the limiting null distribution of the resulting test and compare its local power to that of the standard procedure. It turns out that neither version is uniformly superior to the other.

The well-known CUSUM test for structural change is investigated whe n there are lagged dependent variables among the regressors in a linear model. The authors show that both a modified CUSUM test, suggested b y J. M. Dufour (1982), and the straightforward CUSUM test retain their asymptotic significance levels in dynamic models, and find that the power depends crucially on the angle between the mean regressor and the structural shift.

The authors consider the relative bias of the OLS-based estimate s(squared) of the disturbance variance in the linear regression model when disturbances are stationary AR(1). They improve upon previous bounds for the bias and show that E(s[squared]/[sigma squared]) tends to zero as autocorrelation increases whenever there is an intercept in the regression.

We generalize an empirical likelihood approach to deal with missing data to a model of consumer credit scoring. An application to recent consumer credit data shows that our procedure yields parameter estimates which are significantly different (both statistically and economically) from the case where customers who were refused credit are ignored. This has obvious implications for commercial banks as it shows that refused customers should not be ignored when developing scorecards for the retail business. We also show that forecasts of defaults derived from the method proposed in this paper improve upon the standard ones when refused customers do not enter the estimation data set.