EDUCATION ASPECT.

ECONOMETRICS

Autocorrelation refers to error term one observation related to or affected by the error term of another observation in other words it correlated to it. There is no similar number of features between autocorrelation and heteroscedasticity. It occurs in data when the error term of a regression forecasting model is correlated.

2. Consequences of autocorrelation.

v The estimates of the regression coefficients no longer have a minimum variable property and may be inefficient.

v The variance of the square error terms may be greatly underestimated by the mean sequence error value.

v The true standard deviation of the estimated regression coefficient is seriously underestimated.

v The confidence intervals and test using T and E distributed are no longer strictly applicable.

v As ∑e² is affected then R² is also affected.

v The ordinary square estimators will be inefficient and therefore no longer BLUE.

v The OLS estimators are still unbiased and consistent. This is because both unbiasedness and consistency do not depend on assumption 6 which is in this case violated.

v The estimated variances of the regression coefficients will be biased and inconsistent, and therefore hypothesis testing is no longer valid. In most of the cases, the R² will be overestimated and the t-statistics will tend to be higher.

                                                                                       BYE