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 model represented by equation (6) was therefore estimated with the xtgls specification for heteroskedastic panels, and the results obtained are displayed in Table 4.
As previously indicated, a heteroskedastic component is expected to exist in the error term; hence, a robust correction in the variance and covariance matrix of the errors is applied.
The variant based on Doz, Giannone, and Reichlin (2012) instead assumes a heteroskedastic H with diagonal elements equal to o[[sigma].
An examination of Figure 6 provides an indication of the complexity in the heteroskedastic model error structure, which makes it difficult to provide a priori estimates for the model error for use in the source reconstruction.
In our regression analysis, we calculate heteroskedastic and autocorrelation consistent standard errors using the Newey-West approach where the number of correlated lags is set equal to the number of months in the respective relative strength strategy.
In addition to the FE and RE panel data models, the Prais and Winsten (1954) heteroskedastic panel corrected standard error model which can control for AR(1) specific to each panel is applied (StataCorp 2007).
Because the ordinary least squares (OLS) estimates display heteroskedasticity, the equations are estimated using both weighted least squares (WLS) and multiplicative heteroskedastic models.
Topics include: a minimum mean squared error semiparametric combining estimator, using panel data to examine racial and gender differences in debt burdens, and the Hausman test and some alternatives with heteroskedastic data.
Since the seminal work of Engle [7], several heteroskedastic parametric models have been proposed for representing the structure of volatility in return series, including: the GARCH model of Bollerslev [8], the A-GARCH, NA-GARCH and V-GARCH models of Engle and Ng [9], the Quadratic ARCH model of Sentana [10], the A-PARCH of Ding, Granger and Engle [11], and the Augmented GARCH model of Duan [12], among others.
We tested the poolability of our data over time by Roy-Zellner test, which, unlike the commonly used Chow test, allows heteroskedastic errors (Baltagi, 2005).
Although researchers have made considerable amount of progress in identifying various conditionally heteroskedastic asset pricing models that have better explanatory powers (e.
1987, A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return, Review of Economics and Statistics, 69:542-547.