Heteroskedastic


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Related to Heteroskedastic: Homoscedastic

Heteroskedastic

A sequence of variables in which each variable has a different variance. Heteroskedastics may be used to measure the margin of the error between predicted and actual data. See also: ARCH.
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The first conclusion indicated from these results is that both the empirical Bayes models and the Box-Cox heteroskedastic models outperformed traditional linear or loglinear models.
Informational content of special regressors in heteroskedastic binary response models Journal of econometrics 193 pp.
T-values reported in parentheses using HC3 heteroskedastic robust standard errors.
Lewbel's empirical approach utilizes a heteroskedastic covariance restriction to construct internal instruments and it is most suitable when conventional IVs are weak or difficult to obtain.
Unnikrishnan, "Analysis of large truck crash severity using heteroskedastic ordered probit models," Accident Analysis & Prevention, vol.
Statistical techniques such as heteroskedastic regression are used to measure the degree of response instability, which is used as a measure of the degree of conflicted attitudes among respondents (for an explanation of this technique, see Alvarez 1997).
(**) Significant at 10% Table 6: Model Diagnostics Result Diagnostics test GFCF MODEL TAXR MODEL [R.sup.2] 0.676046 0.52629 Durban Watson 2.002 1.787 Godfrey LM test 0.497844 1.0532 autocorrelation (0.7796) (0.5906) Jarque--Bera test normality 92.5267 7.5527 (0.072) (0.061) Heteroskedastic 6.17885 7.3011 Breusch Pagan (.4035) (0.2993)
The heteroskedastic ordered probit model was also applied to study the impact of vehicle, occupant, driver, and environmental characteristics on injury outcomes for those involved in crashes with heavy-duty trucks [12].
Nahmens, "Volatility forecast of construction cost index using general autoregressive conditional heteroskedastic method," Journal of Construction Engineering and Management, vol.
Initially, tests for heteroskedastic and serially correlated errors were performed.
The Autoregressive Conditionally Heteroskedastic (ARCH) process of Engle (1982) successfully captures the volatility clusters and generalizes the implausible assumption of constant variance forecast.
And for a given X there is no series correlation between observations, for more the error terms are not heteroskedastic. In another words individual observations over time are different individual observations and such approach may be justified in cases where the sample size from indirect data is very small.