heteroscedasticity

(redirected from Heteroskedasticity)
Also found in: Financial.

het·er·o·sced·as·tic·i·ty

(het'ĕr-ō-skĕd'as-tis'ĭ-tē),
Nonconstancy of the variance of a measure over the levels of the factor under study.
[hetero + G. skedastikos, pertaining to scattering, fr. skedannumi, to scatter]
References in periodicals archive ?
These results provided an estimate of this first-order autocorrelation (p = .90).(2) Furthermore, these IV-OLS estimates revealed highly significant cross-sectional heteroskedasticity, LM = 1,776.86 (on this Breusch-Pagan LaGrange Multiplier statistic, see Greene 1990, 465-69).
(23) Second, heteroskedasticity difficulties are common in studies employing the PCT design because variability often differs considerably from unit to unit, often due to differences in size, but perhaps for other reasons.
Since financial variables tend to exhibit volatility clustering, generalised autoregressive conditional heteroskedasticity (GARCH) family models are used.
According to Christie (1987), earnings and stock returns, measured per share, are standardized by beginning-of-fiscal-year stock price to control heteroskedasticity. Besides, White-Huber standard errors are used to calculate heteroskedasticity-robust t statistic (White, 1980).
White's test does not show heteroskedasticity of the residuals at a level of significance of 0.05 for the years 2000, 2001, 2002, 2003, 2005, 2006, and 2012.
The Autoregressive conditional heteroskedasticity test is used to check the presence of heteroskedasticty.
Thirdly, in case the problems of serial correlation or heteroskedasticity are detected from the regression diagnostics then it implies that Fixed Effect or Random Effects Models provide spurious regression results.
Table 4 encompasses the results of heteroskedasticity by using Breusch Pagan Godfrey test.
Heteroskedasticity or within-panel serial correlation problems were overcome using the robust Hubert estimator for the variance.
The article discusses the problem of heteroskedasticity, which can arise in the process of calculating econometric models of large dimension and ways to overcome it.
First, our spatial model addresses the issue of heteroskedasticity. As Messner and Anselin (2004) mentioned, estimation based on a small area can be seriously affected by variation in the size of the population in the area.
Subsequently, the heteroskedasticity diagnostic test and serial correlations was performed on the selected FE model and the results are reported in Table 1.