Heteroskedasticity: Difference between revisions
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Heteroskedasticity | |||
Heteroscedasticity is the case if homoscedasticity is not fulfilled, which is one of the most important assumptions of the ordinary least squares (OLS) regression. | |||
One of the assumptions of the OLS regression is that the errors are normally and independently distributed. The assumption regarding the OLS regression assumes that the variance of the error terms stays constant over periods. | |||
In doubt, you should adopt that in your regression is heteroscedasticity and test if it is true or not regarding the reality. | |||
{{stub}} | {{stub}} | ||
=Definition:= | |||
Heteroskedasticity is defined as the residuals that don’t have the same variances in the model. That means that the difference in the true values of the residuals is not the same in every period. This causes the variance of the errors to depend on the independent variables, which causes an error om the variance of the OLS estimators and therefore in their standard errors. | |||
Var(ε)=σ_i^2≠ σ^2 | |||
=Consequences: = | |||
If you run your regression under the fact that there is heteroscedasticity you get unbiased values for your beta coefficients. That means there is no correlation between the explanatory variable and the residual. | |||
So, consistency and unbiasedness are still given if only the homoscedasticity assumption is violated. Overall, there is no impact on the model fit. | |||
But you get an impact on other parts: | |||
#The estimates of your coefficients are not efficient anymore | |||
# The standard errors are biased as the test statistics | |||
Due to wrong standard errors, our t-statistic is wrong, and we make any valid statement about their significance. For example, if the standard errors will be too small then it’s more unlikely to reject the null hypothesis. | |||
Thus, the inference, as well as efficiency, are affected. | |||
The results won’t be efficient anymore because they don’t have the minimum variance anymore. | |||
It’s very important to correct heteroskedasticity to get a useful interpretation of your model and to have a correct interpretation of statistical test decisions. | |||
=Reasons for Heteroscedasticity:= | |||
Often found in time series data or cross-sectional data. | |||
Reasons can be omitted variables, outliers in data, or incorrectly specified model equations. | |||
=How to find out if there is heteroskedasticity?= | |||
Revision as of 13:09, 26 October 2022
Heteroskedasticity Heteroscedasticity is the case if homoscedasticity is not fulfilled, which is one of the most important assumptions of the ordinary least squares (OLS) regression.
One of the assumptions of the OLS regression is that the errors are normally and independently distributed. The assumption regarding the OLS regression assumes that the variance of the error terms stays constant over periods. In doubt, you should adopt that in your regression is heteroscedasticity and test if it is true or not regarding the reality.
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This is an article stub. |
Definition:
Heteroskedasticity is defined as the residuals that don’t have the same variances in the model. That means that the difference in the true values of the residuals is not the same in every period. This causes the variance of the errors to depend on the independent variables, which causes an error om the variance of the OLS estimators and therefore in their standard errors. Var(ε)=σ_i^2≠ σ^2
Consequences:
If you run your regression under the fact that there is heteroscedasticity you get unbiased values for your beta coefficients. That means there is no correlation between the explanatory variable and the residual. So, consistency and unbiasedness are still given if only the homoscedasticity assumption is violated. Overall, there is no impact on the model fit.
But you get an impact on other parts:
- The estimates of your coefficients are not efficient anymore
- The standard errors are biased as the test statistics
Due to wrong standard errors, our t-statistic is wrong, and we make any valid statement about their significance. For example, if the standard errors will be too small then it’s more unlikely to reject the null hypothesis. Thus, the inference, as well as efficiency, are affected. The results won’t be efficient anymore because they don’t have the minimum variance anymore. It’s very important to correct heteroskedasticity to get a useful interpretation of your model and to have a correct interpretation of statistical test decisions.
Reasons for Heteroscedasticity:
Often found in time series data or cross-sectional data. Reasons can be omitted variables, outliers in data, or incorrectly specified model equations.
