homoscedastic

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homoscedastic

[‚hä·mō·skə¦das·tik]
(statistics)
Pertaining to two or more distributions whose variances are equal.
Pertaining to a variate in a bivariate distribution whose variance is the same for all values of the other variate.
References in periodicals archive ?
0 Panels Homoskedastic Homoskedastic Correlations No Autocorrelation No Autocorrelation Third Model Coefficient Prob.
For this reason, our traditional methodology for the CFNAI can be considered a special case of the dynamic factor model with a zero transition matrix and a homoskedastic idiosyncratic error structure (that is, the assumption of equal variances across unobserved idiosyncratic drivers of the underlying data series).
Homoskedastic idiosyncratic component, same variance for the common and idiosyncratic component: [e.
Regarding the diagnostic checks, as shown in Arellano and Bond (1991), the Sargan test only has an asymptotic chi-squared distribution for a homoskedastic error term.
t] is specified as a homoskedastic random walk with drift, that is, [mu](r) = r + [gamma], [sigma](r) = [sigma].
It is important to highlight that when clustered robust standard errors are used, the model passes the overidentifying restriction test (Sargan and/or Hansen J test); however when homoskedastic errors are assumed we fail the test.
Smearing" estimators such as the Duan smearing estimator have been developed to account for this bias, but these are generally only appropriate when error terms are homoskedastic (Duan 1983; Duan et al.
Next in Table 5 we report the estimation results for the homoskedastic version of Carhart (1997) model (4).
It can be observed from Appendix 2 C) that we fail to reject the null that disturbance is homoskedastic.
1) Diagnostic test results reported in table 3 show that the VAR is not serially correlated and is homoskedastic and normal.
it] is assumed to be independent and homoskedastic across countries and time; in the two-step estimator, the residuals of the first step are used to consistently estimate the variance-covariance matrix of the residuals, relaxing the assumption of homoskedasticity.
The distributions are homoskedastic, and the paired linear correlation coefficients are very close to unity (see Table 3).