Autocorrelation, the correlation of a signal with a delayed copy of itself, can significantly impact the reliability of statistical models. When present in data used for modeling, it violates the assumption of independence among observations, leading to biased parameter estimates and inflated standard errors. This results in unreliable confidence intervals and hypothesis tests. Autocorrelation also reduces the efficiency of estimators, making them less reliable. It’s crucial to detect and correct autocorrelation using methods like Durbin-Watson test or autoregressive integrated moving average (ARIMA) models to ensure model reliability.
