Autocorrelation arises when errors are correlated across time or observations from "summary" of Introduction to Econometrics by Christopher Dougherty
Autocorrelation is a phenomenon that occurs when errors in a regression model are correlated across time or observations. This correlation violates one of the fundamental assumptions of classical linear regression analysis - that the errors are independently and identically distributed. When autocorrelation is present, it can lead to biased and inefficient parameter estimates, as well as incorrect standard errors and hypothesis tests. In a time series context, autocorrelation often arises due to the presence of some underlying pattern or structure in the data that is not accounted for in the regression model. For example, if the errors in a time series model exhibit a trend or cyclical pattern, this can result in autocorrelation. Similarly, if the errors exhibit a seasonal pattern or some form of long-term dependency, autocorrelation may be present. Autocorrel...
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