Hello, I have researched plenty of websites about the definition of Heteroskedasticity, but I can't seem to understand the concept.
Maybe you guys can shed some light on the definition of Heteroskedasticity?
one of the assumption of regression analysis is that the variance of the error terms should be constant. The error term is the difference between the predicted value by regression and the actual value for each observation.These error terms should show constancy of variance that is these error terms should not show any pattern in terms of variation but should be uniformly and constantly distributed around the regression line.
conditional Heteroskedasticity occurs when the error terms depends on the level of the independent variables so that if error terms show some relation with the independent variables that is they show regular upward trend or downward trend then there is correlation between the error terms and the level of independent variable. BP test is used to measure this correlation that is it measures the level of correlation of error terms with the independent variable if there is correlation present as identified by bp test then there is conditional Heteroskedasticity present. In case the error terms does not show any relation with the independent variables than there is no Heteroskedasticity and but there is Homoskedasticity which validates one of the assumption of the regression that the error terms shows constant variance.
i hope the above definition gives you some idea of what is Heteroskedasticity .
thanks
thank you, so heteroskedasticity, if I understood correctly, is about errors between predicted values and actual values in a regression,
but how can you relate this to volatility?
one of the assumption of regression analysis is that the variance of the error terms should be constant. The error term is the difference between the predicted value by regression and the actual value for each observation.These error terms should show constancy of variance that is these error terms should not show any pattern in terms of variation but should be uniformly and constantly distributed around the regression line.
conditional Heteroskedasticity occurs when the error terms depends on the level of the independent variables so that if error terms show some relation with the independent variables that is they show regular upward trend or downward trend then there is correlation between the error terms and the level of independent variable. BP test is used to measure this correlation that is it measures the level of correlation of error terms with the independent variable if there is correlation present as identified by bp test then there is conditional Heteroskedasticity present. In case the error terms does not show any relation with the independent variables than there is no Heteroskedasticity and but there is Homoskedasticity which validates one of the assumption of the regression that the error terms shows constant variance.
i hope the above definition gives you some idea of what is Heteroskedasticity . thanks
Also what i understood for homosked is that the error terms show constant variance. Are we saying there is a correlation between these error terms and the regressor under our consideration? And this being an assumption of least squares.
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