Autocorrelation, also known as serial correlation, occurs when the residuals or error terms in a regression model are correlated across time or observations rather than being independent. In applied econometrics, this is particularly important in time series data, where past shocks or disturbances can influence current values, violating the classical assumption of the Ordinary Least Squares (OLS) model that errors are uncorrelated. Autocorrelation can lead to inefficient estimates, biased standard errors, and invalid hypothesis tests, even if the coefficients themselves remain unbiased. It often arises in economic data due to omitted variables,