Which test is used to check for the presence of first-order autocorrelation?

The Durbin-Watson test is specifically designed to detect the presence of first-order autocorrelation in the residuals from a linear regression model. Autocorrelation occurs when the residuals (errors) in a regression analysis are correlated with each other, particularly when the correlation is based on previous periods (i.e., first-order).

In modeling time series data or any data where order matters, it’s crucial to ensure that the residuals are independent because autocorrelation can lead to inefficiencies in estimates and unreliable statistical tests. The Durbin-Watson statistic ranges from 0 to 4, where a value around 2 suggests no autocorrelation, values below 2 indicate positive autocorrelation, and values above 2 indicate negative autocorrelation.

Other tests listed, such as the Kolmogorov-Smirnov test, Shapiro-Wilk test, and Levene’s test, serve different purposes. The Kolmogorov-Smirnov test is used for comparing sample distributions and assessing goodness of fit, the Shapiro-Wilk test is used to test for normality of distribution, and Levene’s test is utilized to check for equality of variances across groups. Therefore, they are not appropriate for detecting autocorrelation. The focus