NBER Summer Institute 2012

Recently, I checked out the site for the NBER Summer Institute 2012 - Econometric Methods for Demand Estimation.

 

There, you'll find eight videos of some of the lectures presented by Ariel Pakes (Harvard) and Aviv Nevo (Northwestern). The slides that accompany the lectures are also available for downloading.

 

The topics covered in the vieo lectures are:

 

Pakes -

• The primitives of static demand models
• Confronting the precision problem, the information in prices, implications for use of hedonics
• Incorporating micro data
• Moment inequalities in demand analysis

Nevo -

• Estimation of static discrete choice models using market level data
• Applications and choice of IV's
• Measurement of consumer welfare
• Dynamic demand

I learned a lot from these lectures, and I hope that you find them interesting too!

© 2012, David E. Giles

So, What is Econometrics?

Over the years there have been many attempts to define what we mean by the term "Econometrics". I guess we all have our favourites. Mine comes from one of the most influential econometricians of our time - David Hendry:

"Unfortunately, I must now try to explain what "econometrics" comprises. Do not confuse the word with "econo-mystics" or with "economic-tricks", nor yet with "icon-ometrics". While we may indulge in all of these activities, they are not central to the discipline. Nor are econometricians primarily engaged in measuring the heights of economists."
 

On Crime and Punishment

Quite regularly, I take a look at the "Graphic Detail" blog that's published each business day on The Economist's website. Many of the graphs, maps and infographics that they produce are rather interesting.

 

Today's one is taken from a recent study, "Divergent Effects of Beliefs in Heaven an Hell on National Crime Rates", published by Azim Shariff and Mijke Rhemtulla  in the open-access journal, Plos One.

 

Here's the abstract from that paper:

The Cauchy Estimator & Unit Root Tests

As we all know, there's more than one way to estimate a regression equation. Some of the estimators that we frequently use include OLS, GLS, IV, GMM, LAD, and ML. Some of these estimators are special cases of some of the others, depending on the circumstances.

But have you ever used the Cauchy estimator? Probably not, even though it's been around (at least) since 1836.

Visualization Methods

Data visualization is an important part of any statistical analysis, including econometric modelling. This is a point I've made before, I know (here, for example). However, it's worth repeating the message from time to time. The Visual-Literacy website has a fun item that summarizes some of the different ways of conveying information in a visual manner: Periodic Table of Visualization Methods.

To be sure, not all of them are relevant to econometricians, but I thought it was kinda fun!

(H-T to John Cook and his @StatFact)

© 2012, David E. Giles

Topp-Leone Distribution

In May, I posted about bias-correcting maximum likelihood estimators (MLEs), and I referred to a series of related papers that I've been authoring/co-authoring in recent times.

I've just completed another such paper - this one relates to the estimation of the scale parameter in the so-called Topp-Leone (1955) distribution. You can access the paper here; and the abstract explains why this distribution is especially interesting, and summarizes the main results:

"The Topp-Leone distribution is attractive for reliability studies as it has finite support and a bathtub-shaped hazard function. We compare some properties of the method of moments, maximum likelihood, and bias-adjusted maximum likelihood estimators of its shape parameter. The last of these estimators is very simple to apply and dominates the method of moments estimator in terms of relative bias and mean squared error."

Economic Forecasting

I really enjoyed this post, titled Economic Forecasting: Is Google Trends the Future?, by Livio Di Matteo, on the Worthwhile Canadian Initiative blog.

The title speaks for itself.

© 2012, David E. Giles

"The Rise of Econometrics"

Readers of this blog will know that I have an (untrained) interest in the history of econometrics.

Even so, I'm afraid I don't see myself spending $1,300 to buy the set of volumes going by the name of this post's title, when Routledge publishes it in December!

 

Edited by Duo Qin, you'll get 1,777 pages, and:

"The set provides an authoritative one-stop resource to enable users to understand what has shaped econometrics into its current form. With a full index and comprehensive introductions to each volume, newly written by the editor, the collection also provides a synoptic view of many current key debates and issues."
 

 Hopefully I can persuade the UVic Library to get on board!

 

In any case, the Table of Contents for this 4-volume set provides a fine reading guide for serious students of econometrics. Take a look, and then do some bedtime reading!

 

© 2012, David E. Giles

On Becoming a Sportsmetrician

Wouldn't you know it!? No sooner had I posted about Analysing Olympic Medal Data than the latest issue of AmstatNews hit my (snail) mailbox. AmstatNews is the monthly magazine for members of the American Statistical Association.

In the STATtr@ck section, there's a nice article by Jim Albert, titled "Preparing for a Career as a Sports Statistician: Two Interviews With People in the Field".

Take a look if you have any inclination of  becoming a Sportsmetrician.

© 2012, David E. Giles

Analysing Olympic Medal Data

So, the London Olympics are over - with the Paralympics still to come, of course. Sports, and events such as the Olympic Games, generate lots of lovely data. It's also usually "hard" data. So, there's a cottage industry out there comprised of statisticians of all shapes and forms who love to work sports data.

The American Statistical Association has a Section for Statistics in Sport, publishes the Journal of Quantitative Analysis in Sports, and provides access to some interesting sports data-sets.