Autocorrelation can be tested using statistical methods such as the Durbin-Watson test, Ljung-Box test, and Breusch-Godfrey test. The Durbin-Watson test is a simple method that checks for first-order autocorrelation by comparing the differences between values at different time points. A value close to 2 suggests no autocorrelation while values approaching 0 or 4 indicate positive or negative autocorrelation respectively.
The Ljung-Box test extends this concept to higher orders of autocorrelation, testing whether any group of autocorrelations of a time series are different from zero. Similarly, the Breusch-Godfrey test also tests for higher order autocorrelation but it’s more flexible in terms of the types of models it can handle.
Visual methods like plotting the data or creating an autocorrelation plot (ACF) can also provide insights into potential autocorrelation. In an ACF, if the bars cross the confidence interval line, it indicates significant autocorrelation at those lags.
