Autocorrelation, the correlation of a variable with itself over successive time intervals, can significantly impact the standard errors of regression coefficients. When present in a dataset, it violates the assumption of independence among error terms in linear regression models. This violation leads to inefficient parameter estimates and incorrect standard errors, which are typically underestimated.
Underestimated standard errors increase the likelihood of falsely rejecting the null hypothesis (Type I error), as they inflate t-statistics. Consequently, this may lead to misleading conclusions about the significance of predictors.
To mitigate these effects, methods such as Durbin-Watson test for detecting autocorrelation, or using robust standard errors, autoregressive models, or Newey-West standard errors for correcting it, can be employed. These techniques help ensure more accurate inference from regression analysis when autocorrelation is present.
