1. Autocorrelation in Time Series KNNL – Chapter 12

2. Issues in Autocorrelated Data When error terms are correlated (not independent), problems occur when using ordinary least squares (OLS) estimates – Regression Coefficients are Unbiased, but not Minimum Variance – MSE underestimates  2 – Standard errors of regression coefficients based on OLS underestimate the true standard error –  Inflated t and F statistics and artificially narrow confidence intervals

3. First-Order Model - I

4. First-Order Model - II

5. Test For Independence - Durbin-Watson Test

6. Autocorrelation - Remedial Measures Determine whether a missing predictor variable can explain the autocorrelation in the errors Include a linear (trend) term if the residuals show a consistent increasing or decreasing pattern Include seasonal dummy variables if data are quarterly or monthly and residuals show cyclic behavior Use transformed Variables that remove the (estimated) autocorrelation parameter (Cochrane-Orcutt and Hildreth-Lu Procedures) Use First Differences Estimated Generalized Least Squares

7. Transformed Variables

8. Cochrane-Orcutt Method Start by estimating  in Model:  t =  t-1 + u t by regression through the origin for residuals (see below) Fit transformed regression model (previous slide) Check to see if new residuals are uncorrelated (Durbin- Watson test), based on the transformed model If uncorrelated, stop and keep current model If correlated, repeat process with new estimate r based on current regression residuals from the original (back transformed) model

9. Hildreth-Lu and First Difference Methods Hildreth-Lu Method – Find value of r (between 0 and 1) that minimizes the SSE for the transformed model by grid search – Apply the transformed analysis based on the estimated r First Differences Method – Uses  = 1 in transformed model (Y t ’ = Y t – Y t-1 X t ’ = X t – X t-1 ) – Set b 0 ’ = 0 and fits regression through origin of Y’ on X’ – When back-transforming:

10. Forecasting with Autocorrelated Errors