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What are the consequences of autocorrelation on the OLS estimator?
The OLS estimators will be inefficient and therefore no longer BLUE. The estimated variances of the regression coefficients will be biased and inconsistent, and therefore hypothesis testing is no longer valid. In most of the cases, the R2 will be overestimated and the t-statistics will tend to be higher.
How do you solve autocorrelation in time-series?
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.
What causes auto correlation in time series regression?
There are many sources of auto-correlation in time series regression data. In many cases, the cause of autocorrelation is the failure of the analyst to include one or more important predictor variable in the model.
Can a regression analysis be done with no autocorrelation?
However, one of the assumptions of regression analysis is that the data has no autocorrelation. This can be frustrating because if you try to do a regression analysis on data with autocorrelation, then your analysis will be misleading.
When does the independence condition in a multiple linear regression fail?
One common way for the “independence” condition in a multiple linear regression model to fail is when the sample data have been collected over time and the regression model fails to effectively capture any time trends.
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.