Project-Based Learning of Modern Econometrics

The U.K.  Economics Network is supported by, and housed at, the University of Bristol. It provides a wealth of resources for those teaching Economics.  These resources include material produced by various funded projects, including one by Steve Cook (Swansea University). His project (in 2010-11) was titled, "Project-Based Learning of Modern Econometrics. Here's Steve's overview:

Do Better Economic Models Lead to Better Forecasting?

Earlier this month I had a post drawing attention to a short video by David Hendry. Here's another one - this time titled, "Do Better Economic Models Lead to Better Forecasting?" 

© 2013, David E. Giles

Seven Deadly Sins

Xiao-Li Meng has an interesting piece in the September 2013 issue of the IMS Bulletin. (IMS = Institute of Mathematical Statistics). You'll find it on page 4, and it's titled "Rejection Pursuit".

In short, it's about the author's repeated efforts, as a young researcher, to get a particular paper published. The story has a happy ending, and Xiao-Li leaves us with a list of "Seven Deadly Sins of Research Papers, and Seven Virtues to Cultivate":

This looks like excellent advice, regardless of your discipline.

And yes, the article does have an econometric connection. If you read the article and you're interested in non-stationary time-series, you'll probably see the connection coming before the author mentions it!

© 2013, David E. Giles

Beyond MSE - "Optimal" Linear Regression Estimation

In a recent post I discussed the fact that there is no linear minimum MSE estimator for the coefficients of a linear regression model. Specifically, if you try to find one, you end up with an "estimator" that is non-operational, because it is itself a function of the unknown parameters of the model. It's note really an estimator at all, because it can't be computed.

However, by changing the objective of the exercise slightly, a computable "optimal estimator" can be obtained. Let's take a look at this.

Blogs on Resources for Economists

Nice to see that we're now listed on the list of Economics blogs on Resources for Economists.

Thanks!

© 2013, David E. Giles

So Much Good Reading........

Here are my latest reading suggestions:

• Choi, I., 2013. Panel Cointegration. Working Paper, Department of Economics, Sogang University, Korea.
• Davidson, R. and J. G. MacKinnon, 2013. Bootstrap tests for overdentification in linear regression models. Economics Department Working Paper No. 1318, Queen's University.
• Deng, A., 2013. Understanding spurious regression in financial econometrics. Journal of Financial Econometrics, in press.
• Feng, C., H. Wang, Y. Han, and Y. Xia, 2013. The mean value theorem and Taylor's expansion in statistics. The American Statistician, in press.
• Kiviet, J. F. and G. D. A. Phillips, 2013. Improved variance estimation of maximum likelihood estimation in stable first-order dynamic regression models. EGC Report No. 2012/06, Division of Economics, Nanyang Technical University.
• Lanne, M., M. Meitz, and P. Saikkonen, 2013. Testing for linear and nonlinear predicatability of stock returns. Journal of Financial Econometrics, 11, 682-705.

© 2013, David E. Giles

The History of Statistics in the Classrom

You've probably gathered already that I like to incorporate material relating to the history of econometrics, and the history of statistics, into my classroom material. I've always found that it adds perspective, and knowing something about the characters who've contributed to the development of the discipline brings the material to life.

A few years ago, Herbert David presented a paper at the Joint Statistical Meetings, titled "The History of Statistics in the Classroom". It discusses three big players - Laplace, Gauss, and Fisher. You can download a copy of the paper here.

© 2013, David E. Giles

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?”