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Introduction to Econometrics

Introduction to Econometrics. Lecture 8 Autocorrelation. Econometric problems. Topics to be covered. Overview of autocorrelation First-order autocorrelation and the Durbin-Watson test Higher-order autocorrelation and the Breusch-Godfrey test Dealing with autocorrelation

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Introduction to Econometrics

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  1. Introduction to Econometrics Lecture 8 Autocorrelation

  2. Econometric problems

  3. Topics to be covered • Overview of autocorrelation • First-order autocorrelation and the Durbin-Watson test • Higher-order autocorrelation and the Breusch-Godfrey test • Dealing with autocorrelation • Examples and practical illustrations

  4. Autocorrelated series and autocorrelated disturbances

  5. Overview of autocorrelation What is meant by autocorrelation The error terms are not independent from observation to observation – ut depends on one or more past values of u What are its consequences? The least squares estimators are no longer “efficient” (i.e. they don’t have the lowest variance). More seriously autocorrelation may be a symptom of model misspecification How can you detect the problem? Plot the residuals against time or their own lagged values, calculate the Durbin-Watson statistic or use some other tests of autocorrelation such as the Breusch-Godfrey test How can you remedy the problem? Consider possible model re-specification of the model: a different functional form, missing variables, lags etc. If all else fails you could correct for autocorrelation by using the Cochrane-Orcutt procedure or Autoregressive Least Squares

  6. First-order autocorrelation

  7. The sources of autocorrelation

  8. The consequences of autocorrelation

  9. Detecting autocorrelation

  10. The Durbin-Watson test

  11. More on the Durbin-Watson statistic

  12. Using the Durbin-Watson statistic

  13. Durbin-Watson critical values

  14. The Breusch-Godfrey (LM) test

  15. The Breusch-Godfrey test continued

  16. Dealing with autocorrelation • How should you deal with a problem of autocorrelation? • Consider possible re-specification of the model: • a different functional form, • the inclusion of additional explanatory variables, • the inclusion of lagged variables (independent and dependent) • If all else fails you can correct for autocorrelation by using the Cochrane-Orcutt procedure or Autoregressive Least Squares

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