heteroscedasticity

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Related to Heteroskedastic: Homoscedastic

het·er·o·sced·as·tic·i·ty

(het'ĕr-ō-skĕd'as-tis'ĭ-tē),
Nonconstancy of the variance of a measure over the levels of the factor under study.
[hetero + G. skedastikos, pertaining to scattering, fr. skedannumi, to scatter]
References in periodicals archive ?
Although the direction of the bias in [Beta] is known, necessary information regarding [[Sigma].sup.2] is not available due to heteroskedastic nature of [w.sub.t].
Hansen, Bruce E., 1992, Heteroskedastic Cointegration, Journal of Econometrics, 54: 139-158.
Box-Cox Heteroskedastic Model (Model-5): In the context of the risk classification scheme discussed in this study, the Box and Cox model makes the functional form of pure premium model described as equation (4).
Because the theory of the previous section suggests that g and [Delta]y are heteroskedastic, I employ the method of Phillips and Perron [1988] in order to test whether they are mean stationary.
Our method of empirical analysis combines three approaches: first, a generalized autoregressive conditional heteroskedastic (GARCH) estimation of inflation variance variable, ||sigma~.sup.2~; second, an error-correction model of money demand; and third, estimation of time-varying coefficients of the error-correction model with Kalman filtering.
Corrected maximum likelihood estimators in linear heteroskedastic regression models Brazilian Review of Econometrics 28 pp.
Regressions [3] and [4] account for heteroskedastic panels and consider a common AR(1) coefficient for all panels.
We fit the following general linear model using PROC REG in SAS Version 9.4 incorporating HC3 heteroskedastic robust standard errors (Long and Ervin 2000).
Modeling the growth curve of Maine-Anjou beef cattle using heteroskedastic random coefficients models.
This method requires having regressors that are uncorrected with the product of the heteroskedastic errors, which has been shown to be a feature of many models, including measurement error and omitted factor models (Baum etal.
[13] conducted the study about the influence of vehicle, occupant, driver, and environmental characteristics on accident injuries involved with heavy-duty trucks, and the conclusion was obtained by using the heteroskedastic ordered probit models, which showed that the likelihood of severe accident is estimated to rise with the more vehicles involved in accident.
Thus, the OLS estimator would be biased and inconsistent if the error is heteroskedastic and its variance depends on one or more of the explanatory variables.