As we will see later, the variance function in the Normal setting, is constant, however, we must find a way to quantify heteroscedasticity ( non-constant variance ) in the absence of joint Normality.
32.
If an autoregressive moving average model ( ARMA model ) is assumed for the error variance, the model is a "'generalized autoregressive conditional heteroscedasticity "'( "'GARCH ) "'model.
33.
The existence of heteroscedasticity is a major concern in the application of regression analysis, including the analysis of variance, as it can invalidate modelling errors are uncorrelated and uniform hence that their variances do not vary with the effects being modeled.
34.
For any non-linear model ( for instance Logit and Probit models ), however, heteroscedasticity has more severe consequences : the Greene, simply computing a robust covariance matrix for an otherwise inconsistent estimator does not give it redemption.
35.
When this is not the case, the errors are said to be heteroscedastic, or to have heteroscedasticity, and this behaviour will be reflected in the residuals \ scriptstyle \ widehat { u _ i } estimated from a fitted model.
36.
For any non-linear model ( for instance Logit and Probit models ), however, heteroscedasticity has more severe consequences : the Greene, simply computing a robust covariance matrix for an otherwise inconsistent estimator does not give it redemption .
37.
|4 = "'Iteratively reweighted least squares "'( IRLS ) is used when heteroscedasticity, or correlations, or both are present among the error terms of the model, but where little is known about the covariance structure of the errors independently of the data.
38.
A special case of generalized least squares called "'weighted least squares "'occurs when all the off-diagonal entries of " ? " ( the correlation matrix of the residuals ) are null; the variances of the observations ( along the covariance matrix diagonal ) may still be unequal ( heteroscedasticity ).
39.
In addition, another word of caution was in the form, " heteroscedasticity has never been a reason to throw out an otherwise good model . " With the advent of heteroscedasticity-consistent standard errors allowing for inference without specifying the conditional second moment of error term, testing conditional homoscedasticity is not as important as in the past.
40.
In addition, another word of caution was in the form, " heteroscedasticity has never been a reason to throw out an otherwise good model . " With the advent of heteroscedasticity-consistent standard errors allowing for inference without specifying the conditional second moment of error term, testing conditional homoscedasticity is not as important as in the past.
