Question

1. What is First Order Autocorrelation and what effect does it have on the Least Squared Estimates of Linear regression model?

Answer 1

Ans 1. Auto correlation also known as serial orrelation is the correlation of a signal with a delayed copy of itself as a function of delay. It is the similarity between observations as a function of the time lag between them. Serial correlation occures in time-series studies when the errors associated with a given time period carry over into future time periods. With first order serial correlation, errors in one time period are correlated directly with errors in the ensuing time period. Example for autocorrelation is one who runs a regression with two prior trading sessions rturns as the indipendent variables and the current returns as the dependent variable. He finds that returns one day prior have a positive autocorrelation of 0.7, while the returns two days prior have a positive autocorrelation of 0.3

Autocorrelation is a common problem in time series regression. Like other violation of the classicl assumptions, we can view autocorrelation as a regression "ill-ness". When auto correlation is present, the error term observation follow a pattern. Such pttern tell us that something wrong. Auto correlation can cause problems in least squares regression that assume independence of observation. In a regression analysis, autocorrelation of the regression residuals can also occur if the model incorrectly specified