Testing for Parameter Constancy in Linear Regressions: An Empirical Distribution Function Approach.

This paper proposes some tests for parameter constancy in linear regressions. The tests use weighted empirical distribution functions of estimated residuals and are asymptotically distribution free. The proposed tests have nontrivial local power against a wide range of alternatives. In particular, the tests are capable of detecting error heterogeneity that is not necessarily manifested in the form of changing variances. The model allows for both dynamic and trending regressors. As an intermediate result, some weak convergence for (stochastically) weighted sequential empirical processes is established. Copyright 1996 by The Econometric Society.