homoscedasticity

(redirected from Homoskedasticity)
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ho·mo·sced·as·tic·i·ty

(hō'mō-skĕd-as-tis'ĭ-tē),
Constancy of the variance of a measure over the levels of the factor under study.

homoscedasticity

characterized by variances which do not differ greatly between distributions.
References in periodicals archive ?
The White test was used to detect heteroskedasticity and was unable to reject homoskedasticity.
The Tobit estimators are theoretically more consistent and efficient than the TPM unless the assumptions of normality and homoskedasticity are violated to an important degree (Kennedy 1998; Greene 2003), for example, as defined by the Kullback-Leibler distance (Kullback and Leibler 1951).
j-1] is a linear function of the shocks, homoskedasticity ensures the current variance of the shock can be used for all time periods.
Compared with the more commonly used diagnostic tests for general linear models, such as testing for distribution and homoskedasticity of residuals, formal tests of the assumed functional form of any independent variables against the dependent variable are scarcely reported in the epidemiologic literature.
All three calculated values allowed us to reject the hypothesis of homoskedasticity for all periods at the 5 percent level of significance, suggesting that the OLS estimates may be inefficient.
In all regressions in Table 7, the White (1980) test rejects the null hypothesis of homoskedasticity, so all t-statistics are reported based on White (1980) standard errors.
Table 5, which contains the results, shows that the assumption of normality and homoskedasticity is never rejected, and the probit models easily pass the specification tests.
White, Park, and Glejser specification tests all rejected the null hypothesis of homoskedasticity by year.
The regressions for tables 2, 3 and 4 were checked for heteroskedasticity using the White test; all but 2 do not reject the hypothesis of homoskedasticity.
Rather than adjusting the data in the first regime to attain homoskedasticity, we choose to restrict the analysis of regime 1 data beginning with 1950.
A likelihood ratio test failed to reject the hypothesis of homoskedasticity for the error term of the probit models (see Greene, 1997, pp.
As an additional check on the homoskedasticity of the residuals, we applied a weighted least squares regression procedure in which the data were transformed according to the residual variance for each country (see Kmenta 1997, chap.