Lawrence R. Klein, 1920-2013

One of the great figures of econometrics passed away yesterday. Lawrence Klein was the father of whole-economy macroeconometric modelling, and his massive contributions to this field earned him the Nobel Prize in 1980.

Klein created some of the earliest simultaneous equations models of the U.S. economy (e.g., see here), and he was the driving force behind countless such models for other economies around the world. Among other things, Klein was responsible for the foundation of Project LINK, in 1968. This ambitious endeavour now brings together econometric models for 78 countries to provide a "world econometric model".

Lawrence Klein shaped econometric modelling, and his passing marks the end of an amazing era.

Businessweek's obituary for Lawrence Klein can be found here.

© 2013, David E. Giles

A "Segmented" Regression Problem

Here's a little exercise for the students among you.

Suppose that we want to fit a least squares regression model that allows for a "break" in the underlying relationship at a particular sample value for the regressor(s). In addition, we want to make sure that the fitted model passes through that sample value.

In other words, we want to end up with a fitted model that gives a result such as this:

Here, the two segments of the regression line "join" when X=30. What's a simple way to achieve this?

© 2013, David E. Giles

Economics Nobel Prize, 2013

The waiting is over - the 2013 Nobel in Economics was announced this morning! Most deservedly, it has been awarded to Eugene F. Fama (U. Chicago), Lars Peter Hansen (U. Chicago), and Robert J. Shiller (Yale U.). The citation says: "For their empirical analysis of asset prices". 

For more details, see here.

It's really  nice to see the recognition of empirical research.

And let's not forget that Hansen gave us GMM estimation; and do you recall Shiller distributed lag models?

© 2013, David E. Giles

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