Heteroskedastic

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Heteroskedastic

A sequence of variables in which each variable has a different variance. Heteroskedastics may be used to measure the margin of the error between predicted and actual data. See also: ARCH.
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A simple positive-definite heteroscedasticity and autocorrelation consistent covariance matrix.
The model was also evaluated for indications of possible heteroscedasticity using the Glejser and Modified Glejser tests (2,306 307), and for possible autocorrelation using the Durbin Watson test.
In addition, market size directly affects the inaccuracy introduced by heteroscedasticity (see discussion above).
Rejection of the independence hypothesis indicates the presence of heteroscedasticity in residual returns.
This paper focuses solely on the Exponential Generalized Autoregressive Conditional Heteroscedasticity Model (EGARCH) model of Nelson (1991).
This correlation between the residuals and one of our main explanatory variables may lead to bias in the WTP estimates if the estimations are not corrected for heteroscedasticity (Yatchew and Griliches 1985; Hanemann and Kanninen 1996).
Inspection of these plots indicated that the magnitude of the residuals on average was the same, and therefore that no heteroscedasticity was present.
Tests of COIs based on heteroscedasticity, when considering SHMM(u/het) and SREG(u/het) vs SHMM(u/hom) and SREG(u/hom) resulted in larger numbers of COIs considered significant in all of the clusters, except U2, U6, and U7 for SHMM(u/het) vs SHMM(u/hom) and U6U7 for SREG(u/het) vs SREG(u/hom) (Table 4).
In previous studies of alcohol consumption, the error terms were found to be independent (Blaylock and Blisard 1993), while evidence of nonnormality and heteroscedasticity of errors has been reported (Yen 1994).
Heteroscedasticity - an LM test of the dependence of the squared residuals on squared fitted values (distributed as [[Chi].
In particular, models for TSCS data often allow for temporally and spatially correlated errors, as well as for heteroscedasticity.
The difficulty of testing models using traditional statistical schemes results from threshold parameters, parameter correlation, heteroscedasticity in the residuals, and insensitive parameters (Beven and Binley 1992).