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

Testing Up, or Testing Down?

Students are told that if you're going to go in for sequential testing, when determining the specification of a model, then the sequence that you follow should be "from the general to the specific". That is, you should start off with a "large" model, and then simplify it - not vice versa.

At least, I hope this is what they're told!

But are they told why they should "test down", rather than "test up"? Judging by some of the things I read and hear, I think the answer to the last question is "no"!

The "general-to-specific" modelling strategy is usually attributed to David Hendry, and an accessible overview of the associated literature is provided by Campos et al. (2005).

Let's take a look at just one aspect of this important topic. 

Rob Hyndman on Forecasting

If you have an interest in forecasting, especially economic forecasting, the Rob Hyndman's name will be familiar to you. Hailing from my old stamping ground - Monash University - Rob is one of the world's top forecasting experts. 

Without going into all of the details, Rob is very widely published, and also has a great blog, Hyndsight. He's author of the well-known  "forecast" package for R (version 5 just released); and the co-author of several important books.

Last year, Rob taught an on-line forecasting course, titled, "Time Series Forecasting Using R". It comprised 12 one-hour lectures, on the following topics (with exercises):

• Introduction to forecasting 
• The forecaster's toolbox 
• Autocorrelation and seasonality 
• White noise and time series decomposition 
• Exponential smoothing methods 
• ETS models 
• Transformations and adjustments 
• Stationarity and differencing 
• Non-seasonal ARIMA models 
• Seasonal ARIMA models 
• Dynamic regression 
• Advanced methods

The really good news? You can access these presentations right here!

© 2014, David E. Giles