Can you have heteroskedasticity and autocorrelation?
if a series is heteroskedastic, then it cannot be weakly stationarity, and so autocorrelation is not defined, if there is serial correlation, you’re assuming weak stationarity, and so heteroskedasticity is impossible.
What assumption does autocorrelation violate?
Serial correlation (or autocorrelation) is the violation of Assumption 4 (observations of the error term are uncorrelated with each other). This type of correlation tends to be seen in time series data.
What happens if multicollinearity exists?
Multicollinearity reduces the precision of the estimated coefficients, which weakens the statistical power of your regression model. You might not be able to trust the p-values to identify independent variables that are statistically significant.
What are the consequences of heteroskedasticity in autocorrelation?
Consequences of Heteroskedasticity. First, note that we do not need the homoskedasticity asssumption to show the unbiasedness of OLS. Thus, OLS is still unbiased. However, the homoskedasticity assumption is needed to show the e¢ ciency of OLS. Hence, OLS is not BLUE any longer. The variances of the OLS estimators are biased in this case.
Why is it important to check for heteroscedasticity?
Why is it important to check for heteroscedasticity? It is customary to check for heteroscedasticity of residuals once you build the linear regression model. The reason is, we want to check if the model thus built is unable to explain some pattern in the response variable Y Y, that eventually shows up in the residuals.
When to look for heteroscedasticity in a regression model?
It is customary to check for heteroscedasticity of residuals once you build the linear regression model. The reason is, we want to check if the model thus built is unable to explain some pattern in the response variable \\(Y\\), that eventually shows up in the residuals.
Which is normality test rejects no autocorrelation in errors?
Jarque-Bera Normality Test rejects normality in residuals. LM test with 4 lags rejects no autocorrelation in errors. ARCH-Test with 4 lags rejects null hypothesis. (I must accept lag selection in these two last tests was based on a rule of thumb, which I feel ends up being arbitrary)