Statistics MCQs, Data Analysis, Stat Software
Post about the presence of autocorrelation, reasons for autocorrelation, detection of autocorrelation, remedy, and test statistics of autocorrelation.
Durbin and Watson have suggested a test to detect the presence of autocorrelation which applies to small samples. However, the test is appropriate only for the first-order autoregressive scheme ($u_t = \rho u_{t-1} + \varepsilon_t$). Step by Step procedure for the Durbin-Watson Test Step 1: Null and Alternative Hypothesis The …
The existence and pattern of autocorrelation may be detected using a graphical representation of residuals obtained from ordinary least square regression. One can draw the following residual plot for the detection of autocorrelation: Detection of Autocorrelation from Residual Plots Note that the population disturbances $u_t$ are not directly observable, we …
To understand the first-order Autocorrelation, consider the multiple regression model as describe 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} + \varepsilon_t, …
In this post, we will discuss some important consequences of the existence of autocorrelation in the data. The consequences of the OLS estimators in the presence of Autocorrelation can be summarized as follows: Consequences of Autocorrelation on OLS Estimators it Exists Learn about Autocorrelation and Reasons for Autocorrelations Learn more …