Contents
What is autocorrelation in a regression model?
Autocorrelation refers to the degree of correlation between the values of the same variables across different observations in the data. In a regression analysis, autocorrelation of the regression residuals can also occur if the model is incorrectly specified.
What do you do with positive autocorrelation?
The observations with positive autocorrelation can be plotted into a smooth curve. By adding a regression line, it can be observed that a positive error is followed by another positive one, and a negative error is followed by another negative one.
When to add autocorrelation to a regression model?
Remedial measures for autocorrelation Time-ordered effects are often due to the missing key variables. When the long-term persistent effects cannot be captured by one or several predictor variables, a trend component can be added to the regression model, such as a linear/exponential trend, or indicator variables for seasonal effects.
What to do when autocorrelation of error terms is found?
When autocorrelated error terms are found to be present, then one of the first remedial measures should be to investigate the omission of a key predictor variable. If such a predictor does not aid in reducing/eliminating autocorrelation of the error terms, then certain transformations on the variables can be performed.
When to use a remedial measure for autocorrelation?
The Ljung-Box Q test statistic of 9.08 corresponds to a χ 1 2 p -value of 0.0026, so there is strong evidence the lag-1 autocorrelation is non-zero. When autocorrelated error terms are found to be present, then one of the first remedial measures should be to investigate the omission of a key predictor variable.
Is there an AR ( 1 ) model for partial autocorrelation?
We next look at a plot of partial autocorrelations for the data: To obtain this in Minitab select Stat > Time Series > Partial Autocorrelation. Here we notice that there is a significant spike at a lag of 1 and much lower spikes for the subsequent lags. Thus, an AR (1) model would likely be feasible for this data set.