Homoskedastic


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Related to Homoskedastic: Homoscedastic

Homoskedastic

Describing a sequence of variables where each variable has the same or a very similar variance. Homoskedasticity is often assumed in statistics but is not always true. See also: Heteroskedastic.
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Homoskedastic idiosyncratic component, common component has a smaller variance than the idiosyncratic component: [e.sub.it]~N(0,1) and r= [theta]/2.
Note that estimates from the log-2SLS model were re-transformed into dollars with a homoskedastic nonparametric retransformation (Duan 1983).
However, in our case, errors are homoskedastic and sample size is small, so IV technique with fixed effect is the most suitable technique.
where [[??].sub.t] [equivalent to] [A.sub.t][[lambda].sub.t] and [[alpha].sub.t] = [A.sub.t]/[A.sub.t-1] = exp([alpha] + [[epsilon].sub.t]), [alpha] > 0, and [[epsilon].sub.t] is the mean zero, homoskedastic TFP shock innovation.
Under the additional assumption that asset returns and consumption are jointly log normal and homoskedastic, the Epstein-Zin Euler equation implies that the risk premium RP on any asset i over the short-term safe asset is
The null hypothesis that the errors are homoskedastic was rejected for the regressions with both a dummy variable for positive and for negative returns.
where [[bar [sdebt.sub.i]], is the average (across years) secured debt-to-total debt ratio for firm i, [bar [X'.sub.i]], is a vector of control variables averaged across years, (89) [bar [tort.sub.i]] is an indicator variable for whether or not the firm is a high-tort-risk firm (several different measures of this risk are used), (90) and [[epsilon].sub.i] is a normally distributed, homoskedastic error term that is uncorrelated with the regressors.
The 4 lag VAR has no significant residual autocorrelation; the Jarque-Bera statistic indicates normality of residuals and the White test for heteroskedasticity indicates homoskedastic residuals.
Spatial error dependence, which may be caused by spatial correlation of omitted variables or spatial mismatch in data measurement, violates the standard assumptions of the linear regression model (e.g., the assumption of independent, homoskedastic residuals).
By contrast, for the Tobit model, a standard MLE technique to estimate equations with censored dependent variables, the consistency hinges on the assumption of normally distributed and homoskedastic errors.