Examples of 'heteroskedasticity' in a sentence
Meaning of "heteroskedasticity"
Heteroskedasticity is a statistical term used to describe the situation where the variability of a variable is unequal across the range of values of a second variable
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- Alternative spelling of heteroscedasticity
How to use "heteroskedasticity" in a sentence
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heteroskedasticity
Errors are robust to heteroskedasticity and are clustered by country.
Robust standard errors are robust to possible problems of heteroskedasticity.
This plot is indicating heteroskedasticity in the data.
Heteroskedasticity and autocorrelation problems.
The spellings homoskedasticity and heteroskedasticity are also frequently used.
Regression models for cross sectional data typically display heteroskedasticity.
The problem of heteroskedasticity can be checked for in any of several ways.
Several tests exist to check for the presence of heteroskedasticity.
The models of heteroskedasticity are evaluated using graphical residual analysis.
Determination of the number of common stochastic trends under conditional heteroskedasticity.
Heteroskedasticity occurs when the amount of error is correlated with an independent variable.
All the above tests are based on standard errors corrected for heteroskedasticity.
A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature.
This incorporates both the zero flows and solves the heteroskedasticity problem.
Jump risks and heteroskedasticity in Korean financial markets.
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Robust White standard errors are calculated for statistical inference to account for heteroskedasticity.
We tested for and found the presence of heteroskedasticity using a White test.
Heteroskedasticity test, the White test results.
Robustifying Glejser test of heteroskedasticity.
In that case, heteroskedasticity is present.
Our results are robust to problems with omitted variable, heteroskedasticity and endogeneity.
The Generalized Autoregressive Conditional Heteroskedasticity ( GARCH ) model is another popular model for estimating stochastic volatility.
For instance, several tests exist to test for the presence of heteroskedasticity.
Standard errors for all equations are heteroskedasticity and serial-correlation robust.
F-statistics from White 's test also suggest that the regressions do not suffer from heteroskedasticity.
Therefore, there is no problem of heteroskedasticity in the model.
Notes, ( a ) Heteroskedasticity consistent standard errors are in parentheses.
Moreover, there is evidence of heteroskedasticity in the model.
General undertaking generalized autoregressive conditional heteroskedasticity ( GARCH ).
See also autoregressive conditional heteroskedasticity ( ARCH ) models and autoregressive integrated moving average ( ARIMA ) models.
Conditional variances are important parts of autoregressive conditional heteroskedasticity ( ARCH ) models.
H1, There is heteroskedasticity problem.
The Breusch-Pagan ( BP ) test is one of the most common tests for heteroskedasticity.
Engle pioneered the method of autoregressive conditional heteroskedasticity ( ARCH ) and Granger the method of cointegration.
The statistical analysis included checks for heteroskedasticity and multi-collinearity.
In statistics, the Breusch-Pagan test is used to test for heteroskedasticity in a linear regression model.
More general models include autoregressive conditional heteroskedasticity ( ARCH ) models and generalized ARCH ( GARCH ) models.
The GARCH-in-mean ( GARCH-M ) model adds a heteroskedasticity term into the mean equation.
