Autocorrelation
Table Of Contents:
- What Is Autocorrelation?
- Assumption Of No Autocorrelation.
- Why No Autocorrelation Is Important?
- Common Causes Of Autocorrelation.
- Detecting Autocorrelation.
- Addressing Autocorrelation.
- Examples Of Autocorrelation In Residuals.
(1) What Is Autocorrelation?
- In linear regression, autocorrelation refers to the correlation of the residuals (errors) of the model with themselves, particularly in time-series data or data with a sequential nature.
- The assumption of no autocorrelation is one of the key assumptions for the validity of a linear regression model.
(2) Assumption Of No Autocorrelation.
(3) Why No Autocorrelation Is Important?
(4) Common Causes Of Autocorrelation.
- Omitted Variables:
- Missing important predictors that are correlated with the dependent variable.
- Misspecified Model:
- Using an incorrect functional form or missing time-lagged relationships in time-series data.
- Measurement Errors:
- Errors in data collection can introduce patterns in residuals.
- Time-Series or Sequential Data:
- Observations close in time or sequence are often correlated.
(5) Detecting Autocorrelation.
(6) Addressing Autocorrelation.
Add Lagged Variables:
- Include lagged dependent or independent variables to account for temporal effects.
Transform Variables:
- Use differencing or other transformations to remove trends or cycles.
Generalized Least Squares (GLS):
- Modify the regression approach to account for correlated residuals.
Use Time-Series Models:
- For strongly autocorrelated data, consider models like ARIMA (Auto-Regressive Integrated Moving Average) or similar time-series-specific models.
Clustered Standard Errors:
- Adjust standard errors to account for autocorrelation, particularly in panel data.