Contents
How do you test for serial autocorrelation?
You can test for autocorrelation with:
- A plot of residuals. Plot et against t and look for clusters of successive residuals on one side of the zero line.
- A Durbin-Watson test.
- A Lagrange Multiplier Test.
- Ljung Box Test.
- A correlogram.
- The Moran’s I statistic, which is similar to a correlation coefficient.
How do you cure autocorrelation?
There are basically two methods to reduce autocorrelation, of which the first one is most important:
- Improve model fit. Try to capture structure in the data in the model.
- If no more predictors can be added, include an AR1 model.
How do you test autocorrelation residuals?
Detect autocorrelation in residuals
- Use a graph of residuals versus data order (1, 2, 3, 4, n) to visually inspect residuals for autocorrelation. A positive autocorrelation is identified by a clustering of residuals with the same sign.
- Use the Durbin-Watson statistic to test for the presence of autocorrelation.
Why is autocorrelation a problem?
Autocorrelation can cause problems in conventional analyses (such as ordinary least squares regression) that assume independence of observations. In a regression analysis, autocorrelation of the regression residuals can also occur if the model is incorrectly specified.
What does autocorrelation mean?
Definition of autocorrelation. : the correlation between paired values of a function of a mathematical or statistical variable taken at usually constant intervals that indicates the degree of periodicity of the function.
What is autocorrelation statistics?
In statistics, the autocorrelation of a real or complex random process is the Pearson correlation between values of the process at different times, as a function of the two times or of the time lag.
What is the autocorrelation for a time series?
Autocorrelation is a mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals.