Contents
What causes serial correlation?
Serial correlation occurs in time-series studies when the errors associated with a given time period carry over into future time periods. For example, if we are prediciting the growth of stock dividends, an overestimate in one year is likely to lead to overestimates in succeeding years.
What is difference between Heteroskedasticity and Homoscedasticity?
is that homoscedasticity is (statistics) a property of a set of random variables where each variable has the same finite variance while heteroscedasticity is (statistics) the property of a series of random variables of not every variable having the same finite variance.
How do you explain Heteroscedasticity?
What Is Heteroskedasticity? In statistics, heteroskedasticity (or heteroscedasticity) happens when the standard deviations of a predicted variable, monitored over different values of an independent variable or as related to prior time periods, are non-constant.
How to remove serial correlation and heteroskedasticity?
Go to “Quick”, “Equation estimation”, then “Estimation settings” and set “ROBUSTLS” and then run the model. that way Eviews reports robust standard errors and at some of your coefficients should improve towards significance.
The positive serial correlation makes the ordinary least squares standard errors for the regression coefficients underestimate the true standard errors. Moreover, it leads to small standard errors of the regression coefficient, making the estimated t-statistics seem to be statistically significant relative to their actual significance.
How does heteroskedasticity affect the standard error test?
Heteroskedasticity introduces bias into estimators of the standard error of regression coefficients making the t-tests for the significance of individual regression coefficients unreliable. iv. More specifically, it results in inflated t-statistics and underestimated standard errors.
How does serial correlation affect the F statistic?
However, a positive serial correlation inflates the F-statistic to test for the overall significance of the regression as the mean squared error (MSE) will tend to underestimate the population error variance. This increases Type I errors (the rejection of the null hypothesis when it is actually true).