Several tests for heteroskedasticity in linear regression models are examined. Asymptoticrobustness to heterokurticity, nonnormality and skewness is discussed. The finite sample eliability of asymptotically valid tests is investigated using Monte Carlo experiments. It is found that asymptotic...

As shown by the results of Dufour, Khalaf, Bernard and Genest (2004, Journal of Econometrics 122, 317--347), exact tests for heteroskedasticity in linear regression models can be obtained, by using Monte Carlo (MC) techniques, if either (i) it is assumed that the true form of the error...

It is argued that, when researchers wish to carry out a Chow test of the significance of prediction errors, it is necessary to assume homoskedasticity because standard results on heteroskedasticity-robust tests are not available. The effects of heteroskedasticity on the Chow prediction error...

Quasi-likelihood ratio tests for autoregressive moving-average (ARMA) models are examined. The ARMA models are stationary and invertible with white-noise terms that are not restricted to be normally distributed. The white-noise terms are instead subject to the weaker assumption that they are...