Your textbook mentions heteroskedasticity- and autocorrelation- consistent standard errors. Explain why you should use this option in your regression package when estimating the distributed lag regression model. What are the properties of the OLS estimator in the presence of heteroskedasticity and autocorrelation in the error terms? Explain why it is likely to find autocorrelation in time series data. If the errors are autocorrelated, then why not simply adjust for autocorrelation by using some non-linear estimation method such as Cochrane-Orcutt?

Question

Quizplus · Accepted Answer

The Answer of Your textbook mentions heteroskedasticity- and autocorrelation- consistent standard errors. Explain why you should use this option in your regression package when estimating the distributed lag regression model. What are the properties of the OLS estimator in the presence of heteroskedasticity and autocorrelation in the error terms? Explain why it is likely to find autocorrelation in time series data. If the errors are autocorrelated, then why not simply adjust for autocorrelation by using some non-linear estimation method such as Cochrane-Orcutt?

Your Textbook Mentions Heteroskedasticity- and Autocorrelation- Consistent Standard Errors