There are several approaches to dealing with heteroscedasticity. If the error variance at different times is known, weighted regression is a good method. If, as is ...

Testing for heteroscedasticity is a common diagnostic practice in regression analysis. Depending upon the outcome of the test, the model is either estimated by OLS or WLS. The results of a Monte Carlo ...

One of the key assumptions of the ordinary regression model is that the errors have the same variance throughout the sample. This is also called the homoscedasticity ...

In this paper, we introduce a new identification and estimation strategy for partially linear regression models with a general form of unknown heteroscedasticity, that is, Y = X'β₀ + m(Z) + U and U = ...