First Order Autocorrelation (2020)
To understand the First Order Autocorrelation, consider the multiple regression model as described below $$Y_t=\beta_1+\beta_2 X_{2t}+\beta_3 X_{3t}+\cdots+\beta_k X_{kt}+u_t,$$ In the model above the current observation of the error term ($u_t$) is a function of the previous (lagged) observation of the error term ($u_{t-1}$). That is, \begin{align*}u_t = \rho u_{t-1} + …