Autocorrelation, also known as serial correlation, can pose several issues in econometric modeling. It violates the assumption of independence among error terms, leading to inefficient Ordinary Least Squares (OLS) estimators. This inefficiency results in wider confidence intervals and less reliable hypothesis tests. Autocorrelation may also cause bias in coefficient estimates if it’s coupled with model misspecification or omitted variables. Furthermore, autocorrelation can lead to misleading R-squared values, making models appear better than they are.
