A Second Lesson in Econometrics

In an earlier post (here) I discussed John Siegfried's short piece titled "A First Lesson in Econometrics".

A reader of this blog "veli y" has drawn my attention to a very important follow-up piece by Damien Eldridge, of La Trobe University in Australia. His paper, "A Comment on Siegfried's First L"esson in Econometrics can be seen here

Thanks for the tip!

© 2013, David E. Giles

Society for Economic Measurement

Hat-Tip to Michael Belongia for drawing my attention to the Society for Economic Measurement.

Initiated by William Barnett, the Society will be holding its first conference next (Northern) summer.

Definitely worth checking out!

© 2013, David E. Giles

A Regression "Estimator" that Minimizes MSE

Let's talk about estimating the coefficients in a linear multiple regression model. We know from the Gauss-Markhov Theorem that, within the class of linear and unbiased estimators, the OLS estimator is most efficient. Because it is unbiased, it therefore has the smallest possible Mean Squared Error (MSE), within the linear and unbiased class of estimators.

However, there are many linear estimators which, although biased, have a smaller MSE than the OLS estimator. You might then think of asking: “Why don’t I try and find the linear estimator that has the smallest possible MSE?”