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Constancy of the variance of a measure over the levels of the factor under study.
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Constant 1.01 0.0 1.05 0.0 CD TPD -0.07 0.0 ID -.2.3 0.0 SIZE -0.17 0.0 -0.18 0.0 AGE 0.02 0.004 0.02 0.01 leverage -0.01 0.7 -0.01 0.07 liquidity -0.001 0.015 -0.001 0.007 EX -0.002 0.05 CRISIS -0.5 0.0 -0.07 0.0 Panels Homoskedastic Homoskedastic Correlations No Autocorrelation No Autocorrelation Third Model Coefficient Prob.
Homoskedastic idiosyncratic component, common component has a larger variance than the idiosyncratic component: [e.sub.it]~N(0,1) and r= 2[theta].
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.
Additionally we also notice that there are methods [31-33] based on Variance-Stabilizing Transformations or VST (the Anscombe [34] or Freeman-Tukey [35] VST for this particular case): given a set or sequence of nonhomoskedastic random variables (random variables with different finite variances), with the same underlying distribution family, a VST is a specific distribution transformation that renders that set or sequence of random variables into an homoskedastic one (random variables with the same finite variance); moreover such transformation is chosen so the transformed random variables are Gaussian with unit variance.
"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. It implies no heteroskedasticity problems in errors.3
In the one-step estimator, the error term [[epsilon].sub.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).
Conditional variance and jump-diffusion models outperform simple diffusion and homoskedastic models.
(35.) A Cook-Weisberg test for heteroskedasticity indicates that the models are homoskedastic. Logging the data reduced the heteroskedasticity significantly.
To obtain normally distributed outcomes at each gestational age with homoskedastic residual error variance, we first estimated the power transformation of each of the fetal outcomes by modeling their transformed mean as a cubic polynomial in gestational age (in days), using the "boxcox" function from the main package of Venables and Ripley's MASS library (Royston 1995).