Econometrics-I-15

Can be extremely bad gls vs ols the efficiency ratios

Info iconThis preview shows pages 19–25. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Can be extremely bad. GLS vs. OLS, the efficiency ratios can be 3 or more. A very important exception - the lagged dependent variable yt = xt + yt-1 + t. t = t-1 + ut,. Obviously, Cov[yt-1 ,t ] 0, because of the form of t. How to estimate? IV Should the model be fit in this form? Is something missing? Robust estimation of the covariance matrix - the Newey-West estimator. &#152;&#152;™™™ ™ 18/45 Part 15: Generalized Regression Applications GLS and FGLS Theoretical result for known - i.e., known . Prais- Winsten vs. Cochrane-Orcutt. FGLS estimation: How to estimate ? OLS residuals as usual - first autocorrelation. Many variations, all based on correlation of et and et-1 &#152;&#152;&#152;™™™ ™ 19/45 Part 15: Generalized Regression Applications Testing for Autocorrelation A general proposition: There are several tests. All are functions of the simple autocorrelation of the least squares residuals. Two used generally, Durbin-Watson and Lagrange Multiplier The Durbin - Watson test. d 2(1 - r). Small values of d lead to rejection of NO AUTOCORRELATION: Why are the bounds necessary? Godfrey’s LM test. Regression of et on et-1 and xt . Uses a “partial correlation.” &#152;&#152;&#152;™™™ ™ 20/45 Part 15: Generalized Regression Applications Consumption “Function” Log real consumption vs. Log real disposable income ( Aggregate U.S. Data, 1950I – 2000IV. Table F5.2 from text)---------------------------------------------------------------------- Ordinary least squares regression ............ LHS=LOGC Mean = 7.88005 Standard deviation = .51572 Number of observs. = 204 Model size Parameters = 2 Degrees of freedom = 202 Residuals Sum of squares = .09521 Standard error of e = .02171 Fit R-squared = .99824 <<<*** Adjusted R-squared = .99823 Model test F[ 1, 202] (prob) =114351.2(.0000)--------+------------------------------------------------------------- Variable| Coefficient Standard Error t-ratio P[|T|>t] Mean of X--------+------------------------------------------------------------- Constant| -.13526*** .02375 -5.695 .0000 LOGY| 1.00306*** .00297 338.159 .0000 7.99083--------+------------------------------------------------------------- &#152;&#152;&#152;™™™ ™ 21/45 Part 15: Generalized Regression Applications Least Squares Residuals: r = .91 &#152;&#152;&#152;™™ ™ 22/45 Part 15: Generalized Regression Applications Conventional vs. Newey-West +---------+--------------+----------------+--------+---------+----------+ |Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X| +---------+--------------+----------------+--------+---------+----------+ Constant -.13525584 .02375149 -5.695 .0000 LOGY 1.00306313 .00296625 338.159 .0000 7.99083133 +---------+--------------+----------------+--------+---------+----------+ |Newey-West Robust Covariance Matrix |Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X|...
View Full Document

{[ snackBarMessage ]}

Page19 / 46

Can be extremely bad GLS vs OLS the efficiency ratios can...

This preview shows document pages 19 - 25. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online