Bayesian Model Selection - A Worked Example

Choosing between non-nested models can be challenging. A lot of statisticians and econometricians find that a Bayesian approach has a lot to offer when it comes to addressing this challenge. I'm certainly of that view myself.

Let me take you through an empirical example of Bayesian model selection - it involves alternative regression models for the demand for beer in Australia - and it's one that I use, sometimes, in class.

An Interview With Bradley Efron

You've all heard about the bootstrap, and you all know that it was Bradley Efron (Statistics, Stanford) who came up with the idea. (If I'm wrong, you can check this earlier post.)

On Twitter yesterday, Joe Blitzstein (Statistics, Harvard; @stat110) drew attention to this great Youtube video interview in which Brad discusses what led him to develop the bootstrap.

I like his opening words when asked how it all came about:

"It's a story about having good colleagues..."

Well, it probably is, but I'd call that a pretty modest response!

Enjoy the video.


© 2014, David E. Giles

Modelling Olympic Medal Wins

With the Sochi Winter Olympics now well underway, I was reminded of the empirical literature that has attempted to model the number of medals that different countries win.

Back in 2006, one of our students, Glen Roberts, wrote an excellent paper, titled "Accounting for Achievement in Athens: A Count Data Analysis of National Olympic Performance", on this topic. Glen's work was based on a term project that he undertook for my ECON 546, "Themes in Econometrics" course. This is an elective course for M.A. students, and it emphasises the thematic content of econometric methods - MLE, IV/GMM, Bayesian inference, etc.

Glen found that the empirical analyses of Olympic medal wins largely ignored the "count data" aspect of the problem. You can find plenty of references in Glen's paper. He then set about rectifying this situation, as the abstract to his paper describes:

The Statsguys on Data Analytics

It's good to see that more and more students of econometrics are taking an interest in "Data Analytics" / "Big Data" /"Data Science" literature. As I've commented previously, there's a lot that we can all learn from each other. Moreover, many of "boundaries" are very soft, and are more perceived than real.

So, I was delighted to see the arrival of The Statsguys, last month. (Hat-tip to the team at Quandl for alerting me to this.

A Blast From the Past

Doesn't time fly! Around the end of August 1994 Lief Bluck and I put together a web site for our department. That may not sound very interesting, but let's put things in perspective.

Our University didn't have a web site - not many universities did. The Department of Physics & Astronomy here at Uvic had one - that was it. There were, I believe only 6 economics groups in the world that had a website when we got in on the act. The best known one was that run by Hal Varian - then at the University of Michigan - and now Chief Economist at Google.

Our site was a modest affair. Here's the kicker - we had to show people how to use it, and persuade them that this WWW thing may be around for a while!

Recently, Lief discovered a hard copy of this old memo. that he circulated to members of our department a few weeks after we got things going:

Vintage Years in Econometrics - The 1960's

Remember that saying - "if you can remember the 60's you probably weren't there"? Well, with that said, and continuing from my earlier posts about vintage years for econometrics in the 1930's, 1940's, and 1950's, here's my take on the 1960's.

Once again, let me note that "in econometrics, what constitutes quality and importance is partly a matter of taste - just like wine! So, not all of you will agree with the choices I've made in the following compilation."

Conference on Macro & Financial Economics/Econometrics

The 10th BMRC-DEMS Conference is being held at Brunel University, London (U.K.), at the end of May this year. Details can be found here.

The conference themes include a number of important topics:

• Recent developments in time-varying and nonlinear models
• Economic dynamics and smooth transition modeling
• Structural breaks in financial time series
• Dynamic structural financial and macroeconometric modeling
• Macro-financial modeling using mixed frequency data
• Monetary policy and risk taking
• Fiscal policy, financial development and growth
• Macro-finance interface
• Asset pricing models with time-varying moments
• Financial markets volatility and macroeconomic activity
• Financial crash, stock, bond and commodity prices
• Modeling dynamic correlations during financial crises
• Boom-bust cycles and the linkage between financial and real activity
• Early warning indicators of economic and financial instability

There's an impressive line-up of keynote speakers:

• R. Baillie (Michigan State University)
• V. CorradiI (University of Surrey)
• C. Francq (University of Lille 3)
• M. Hallin (ECARES, Brussels)
• A. Harvey (University of Cambridge)
• D. Hendry (University of Oxford)
• G. Melard (ECARES, Brussels)
• P. Minford (Cardiff University)
• R. Taylor (University of Essex)
• S. Wright (Birkbeck College, University of London)
• P. Zaffaroni (Imperial College, London)
• J.-M. Zakoian (CREST, Paris)

© 2014, David E. Giles

The February Reading List

As always - there's lots of interesting reading out there. Here are my suggestions for this month:

• Advani, A. and Tymon Słoczyński, 2013. Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies.Discussion Paper No. 7874, IZA, Bonn.
• Flaig, G., 2012. Why we should use high values for the smoothing parameter of the Hodrick-Prescott filter.  CESifo Working Paper No. 3816, Department of Economics, University of Munich.
• Kiviet, J. F. and J. Niemzczyk, 2013.  On the limiting and empirical distribution of IV estimators when some of the instruments are actually endogenous. EGC Report No: 2013/11, Nanyang Techological University.
• Lütkepohl, H., A. Staszewska-Bystrova, and P. Winker, 2014. Confidence bands for impulse responses: Bonferroni versus Wald. (Updated.) SFB 649 Discussion Paper 2014-007.
• Lv, J. and J. S. Liu, 2013. Model selection principles in misspecified models. Journal of the Royal Statistical Society, B, 76, 141-167. 
• Skeels, C. L. and L. W. Taylor, 2013. Prediction after estimation. Economics Letters, 122, 420-422.
• Tserkezos, K., 2013. Temporal aggregation and Ramsey's (RESET) test for functional form: Results from empirical and Monte Carlo experiment. Mimeo., Department of Economics, University of Crete.

© 2014, David E. Giles

Econometrics at Monash University

My first academic position was in the (then) Department of Econometrics and Operations Research at Monash University ( in Melbourne, Australia). I was there for nine wonderful years from the mid 1970's to the mid 1980's.

Now re-named the Department of Econometrics and Business Statistics, the Monash group continues to rank among the very best in the world, as is evidenced by this recent score from IDEAS.

This makes me feel really good.

Great job!

© 2014, David E. Giles

Alexander Aitken

Alexander Aitken was one of New Zealand's greatest mathematicians - see my earlier post. As an econometrician, you may be very surprised how much you owe him!

Want to check out more about this amazing man? See www.nzedge.com/alexander-aitken/ .

© 2014, David E. Giles