On a new test for autocorrelation in regression models under nonnormality

A new test for autocorrelation in a general regression model under departures from the assumption of normality is derived by applying a beta distribution and bootstrap approximation, Critical values of the test, can be computed for each given design matrix, irrespective of the form of the underlying error distribution, Monte Carlo simulations are conducted in order to illustrate the performance of the test. Among others, it. is found that the suggested test is more robust and far more powerful than existing nonparametric tests.