On the Importance of Knowing the Assumptions

I've been pretty vocal in the past about the importance of understanding what conditions need to be satisfied before you start using some fancy new econometric or statistical "tool". Specifically, in my post, "Cookbook Econometrics", I grizzled about so-called "econometrics" courses that simply teach you do "do this", do that", without getting you to understand when these actions may be appropriate.

My bottom line: you need to understand what assumptions lie behind such claims as "this estimator will yield consistent estimates of the parameters"; or "this test has good power properties" - preferably before you get too excited about using the estimator or test and you cause too much damage. In other words, it's all very well to understand what problems you face in your empirical work (simultaneity, missing observations, uncertain model specification, etc.), but then when you choose some tools to deal with these problems, you need to be confident that your choices will achieve your objectives.

Congratulations, to Thomas Sargent & Christopher Sims

By now, you'll all know that the 2011 Nobel Prize in Economics has been awarded to Thomas Sargent (New York University) and Christopher Sims (Princeton University). To say that this is well deserved and overdue, is an under-statement. Congratulations!

The Rise & Fall of Multicollinearity

Boris Kaiser in the Public Economics Working Group of the Department of Economics, University of Berne in Switzerland writes:

"As a frequent reader of your blog, I consider it my honour as well as my duty to point your attention to the following graph:

It shows the relative frequency of appearance of the word in the realm of the literature, contained in Google Books, over the last 50 years.  [1960-2011; DG]  Clicking on this link here, you can see how I generated the graph."

It seems that we're well on the way to the eradication of this grossly over-rated concept, as predicted in my earlier post, "The Second Longest Word in the Econometrics Dictionary". Thank goodness for that!

I'll explain my relief in a subsequent post. Meantime, "thanks a bunch for doing your duty, Boris"!

© 2011, David E. Giles

Erratum!

Back in May I posted a piece titled, "Gripe of the Day". With that post I provided some EViews code to run homoskedasticity tests for Logit and Probit models. Unfortunately, there was a small error in the code. This has now been fixed, and there is a note to this effect on the Code page for this blog, and in the original post.

The error affected the test results only at the second or third decimal places.

HT to eagle-eyed Sven Steinkamp at Universität Osnabrück  for bringing the error to my attention.

© 2011, David E. Giles

Predicting the 2011 Nobel Laureate in Economic Science

" "This is the Oscars for nerds," says Paul Bracher, a chemist at the California Institute of Technology"

(Daniel Strain, Science, 21 September, 2011)

I swore I wouldn't do it, but in the end I couldn't stop myself! Everyone else is having their say about next Monday's award of the Economics Nobel Prize, so I couldn't sit on the sidelines any longer.

I know it's proper name is "The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel", but I'm going to be lax here. The big announcement will come at around 1:00p.m. CST on Monday 10th October this year. That's around 4 a.m. that day if you're on the West coast, like me, or 7a.m. in New York. If you're not the lucky one to get the wake-up call, then you can watch the announcement, live, here. I'll be joining you!

So, who will the recipient(s) be this year?

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!