Autocorrelation, also known as serial correlation, significantly impacts the interpretation of regression analysis. It refers to the correlation of a variable with itself over successive time intervals. When present in a dataset used for regression analysis, it violates one of the key assumptions: that the residuals (errors) are independent.
The presence of autocorrelation can lead to inefficient parameter estimates because standard errors tend to be underestimated. This underestimation increases the likelihood of Type I error, falsely rejecting the null hypothesis. Consequently, confidence intervals and prediction intervals may be narrower than they should be, giving an illusion of precision which is not truly there.
Moreover, if autocorrelation is ignored, it can result in misleading R-squared values. An inflated R-squared value might suggest a better fit model than actually exists, leading to incorrect interpretations and decisions based on those interpretations.
