Keynes and Econometrics

Regular readers of this blog will know that I think it's important for students of econometrics to know something about the history of the discipline. So, let's pick a big-name economist at random, and see how he fitted into the overall scheme of things econometric.

John Maynard Keynes completed his B.A. with first class honours at the University of Cambridge in 1904. His B.A. in mathematics, that is. Subsequently, he was placed twelfth Wrangler in the Mathematical Tripos of 1905.

Making a Name for Yourself!

So you want to make a name for yourself? One way for an up-and-coming young econometrician to do this would be to come up with a new estimator or test that everyone subsequently associates with your name. For example, the the Aitken estimator; the Durbin-Watson test; the Cochrane-Orcutt estimator; the Breusch-Pagan test; White's robust covariance matrix estimator, etc.

This can be a bit risky - your new inferential procedure might not "catch on" as well as you hope it will. Worse yet, someone else might come up with a similar idea around the same time, and steal your glory.  A much safer way to make a name for yourself is to be the first to prove a result that has hitherto had everyone baffled.

Estimating Models With "Under-Sized" Samples

"....and there is no new thing under the sun."

Ecclesiastes 1:9 (King James Bible)

The first part of my career was spent at New Zealand's central bank (the Reserve Bank of N.Z.), where I was heavily involved in the construction and use of large-scale macroeconometric models. By the mid 1970's our models involved more than 100 equations (of which about 50 were structural relationships that had to be estimated; and the rest were accounting identities). The basic investigative estimation was undertaken using OLS; and variants such as the Almon estimator for distributed lag models. Boy, this dates me!

Of course, we were well aware that OLS wasn't appropriate for the finished product. These models were simultaneous equations models, so OLS was inconsistent. Obviously, something more appropriate was need, but which estimator should we use?

Student's t-Test, Normality, and the Bootstrap

Is Student's t-statistic still t-distributed if the data are non-normal? I addressed this question - in fact, an even more general question - in an earlier post titled "Being Normal is Optional!". There, I noted that the null distribution of any test statistic that is scale-invariant, will be the same if the data come from the elliptically symmetric family distributions, as if they come from the normal distribution.

Are You in Need of Some Psychic Help?

Occasionally I bid for items on ebaY. Sometimes I'm successful.  A few years ago I bought an item in this way, and it turned out that the seller lived here in my home town. I arranged to collect my purchase from his house, and in the course of the transaction he passed me his business card.

Apart from his first name, and telephone number, the only other word on the front of the very colourful card was "PSYCHIC". I kid you not!

Communications With Economists

There's an interesting looking online conference coming up in November - the 16th to 18th of November, specifically. This free conference is courtesy of the Journal of Economic Surveys, and their publisher, Wiley.

The full title of the conference is "Communications With Economists: Current and Future Trends", and there will be three keynote videos; five papers with invited commentaries; workshops on academic publishing; a "reading room"; and lots of discussion.

Students can register, and of special interest to students and practitioners of econometrics will be the keynote video by Professor Sir David Hendry (University of Oxford), one of the foremost econometricians of our time.

Thanks to Les Oxley and the rest of the team at JOES.

Register here - I have!

© 2011, David E. Giles

R Meets Google

Always on the lookout for innovative and free ways to implement data analysis, I was intrigued by a post on Andrew Gelman's blog yesterday, titled "More Data Tools Worth Using From Google". It referred to a 2009 post, "How to Use A Google Spreadsheet as Data in R" (recently updated here) on the Revolutions blog. The title of the post tells it all.

Definitely worth knowing about! Free spreadsheet; free statistical software. What more could you want?

But wait ...... there's more!

In a comment from Zach on the Gelman blog this morning, I learned about Google Motion Charts With R. As Zach notes, it allows you to take data from R and graph it directly using the Google Motion charts API. You may find that it's fussy over which browser you use, but that's O.K. It's recent release, and the R package you need is on the CRAN site here.

Have fun!

© 2011, David E. Giles

Micronumerosity

In a much earlier post I took a jab at the excessive attention paid to the concept of "multicollinearity", historically, in econometrics text books.

Art Goldberger (1930-2009) made numerous important contributions to econometrics, and modelling in the social sciences in general. He wrote several great texts, the earliest of which (Goldberger, 1964) was one of the very first to use the matrix notation that we now take as standard for the linear regression model.

In one of  his text books, Art also poked fun at the attention given to multicollinearity, and I'm going to share his parody with you here in full. In a couple of places I've had to replace formulae with words. What follows is from Chapter 23.3. of Goldberger (1991):

P-Values of Differences of P-Values of Differences of....

An interesting post on Andrew Gelman's blog last week reminded us that we have to be careful to distinguish between the statistical significance of the difference between two "effects", and the difference between the significances of two separate effects. They're not the same thing, and it's the first of these that's usually relevant.

Let's re-phrase this, and put it in baby econometrics terms.

Econometrics and One-Way Streets

It's a nice sunny day out there, so you decide to get on your bike and pedal down the street a couple of blocks to your favourite ice cream parlour. Great idea! Except that it's a one-way street and you're pedalling against the traffic. Fortunately, on this particular day, most people have already headed for the beach, there are very few cars that have to avoid you, and somehow you make it to your destination in one piece. Whew!

Now, maybe your ears are suffering from some of the abuse you received along the way - maybe not. I guess it would depend on exactly who you encountered during your little trip. In any event, you savour your well-earned ice cream and feel pretty good about yourself, and life in general. You could have travelled the longer route, around the block, to avoid the one-way street, but gee, the end result was the same, so that's all that matters. Right?