## Wednesday, February 29, 2012

### Leap Year Econometrics

I guess there must be a few econometricians who were "leap year babies" - that is, born on 29 February. I'm not one of them, but a former co-author of mine was. This is the first February 29th since he passed away, and it's a good day to remember how much fun we had working together.

## Wednesday, February 22, 2012

### A Trick With Regression Residuals

Suppose that you've estimated an econometric model and you want to test the residuals for serial independence, or perhaps for homoskedasticity. The trouble is that for the model and estimator that you've used, your favourite computer package doesn't provide such tests. Is there a quick way of "tricking" the package into giving you the information that you want?

## Monday, February 20, 2012

### Computer Update

Now, this is really scary!!! (Even for nostalgia buffs.)

© 2012, David E. Giles

## Sunday, February 19, 2012

### Tables or Graphs?

Should I present my results in a table or in a graph? Both have their place, of course.

A recent post, titled "Some Notes on Making Effective Tables", on the Cross Validated Community Blog, makes some interesting points and provides some good advice.

The CVCB is, by the way, an overflow blog for Cross Validated Stack Exchange, ".... a collaboratively edited question and answer site for statisticians, data analysts, data miners and data visualization experts."

© 2012, David E. Giles

## Saturday, February 18, 2012

### Early Contributions to Training in Econometrics at the USDA

My colleague, Malcolm Rutherford, has recently published a really interesting paper on the history of statistical and economic education at the U.S. Department of Agriculture Graduate School. The school was founded in 1921, and exists to this day. As Malcolm explains, the USDA Graduate School played a seminal role in instruction in statistics in the 1930's, at a time when Econometrics was in its infancy.

## Friday, February 17, 2012

### The Neyman-Pearson Lemma: An Economic Perspective

In my graduate-level "Themes in Econometrics" course we've been talking recently about the Neyman-Pearson Lemma.  In 1933 Jerzy Neyman and Egon Pearson published one of the most important papers of modern statistics, referenced below. Specifically, they showed that we can use the likelihood ratio to construct the Most Powerful test (for a given significance level), when we are testing a point null hypothesis against a point alternative hypothesis. This set the scene for classical hypothesis testing as we practice it today.

## Thursday, February 16, 2012

### ANZecmet

The following was circulated yesterday by Rob Hyndman, at Monash University:

"ANZecmet is a mailing list intended for econometricians in Australia and New Zealand, but may be of interest to a wider audience. It provides a forum for exchanging views, posting technical questions and responses, job advertisements, conference announcements, new publications, and so on.

The ANZecmet mailing list used to be hosted by Monash University, but is now in the process of moving to a Google group. If anyone on this list would like to join, please head over to
http://groups.google.com/group/anzecmet/about and sign up."

Econometrics has a long-standing and very strong presence in Australia and New Zealand. I've signed up, and I hope some of you will too.

© 2012, David E. Giles

## Tuesday, February 14, 2012

### "Asymptotic" Properties of Estimators and Tests

We're so familiar with "large-sample" asymptotics as a way of characterizing the behaviour of our estimators and tests in econometrics, that we tend to forget that there are other, very interesting ways of evaluating their behaviour, and approximating small-sample behaviour.

I touched on this in an earlier earlier post when I discussed "small-sigma" (or "small error") asymptotics. However, that's by no means the end of the story.

## Sunday, February 12, 2012

### More on Shortest Confidence Intervals

I was (pleasantly) surprised by the number of "hits" my recent post on "Minimizing the Length of a Confidence Interval" attracted. As has often been the case, a lot of visitors came by way of  Mark Thoma's excellent blog, Economist's View. (Thanks, Mark!)

In that post one of the things I discussed was the issue of constructing a "shortest length" confidence interval in the case where the distribution of the pivotal statistic that's used to start off the interval is asymmetric. In such cases, we have a more difficult task on our hands than when the distribution is symmetric, and uni-modal. In response to this, we usually construct "equal tails" confidence intervals in the asymmetric case.

I'm not going to repeat the previous post! Instead, I'm going to share a few lines of R code that I've put together to deal with this issue in the case of an asymmetric distribution that's of great practical importance to econometricians.

## Wednesday, February 8, 2012

### More on the Equivalence of GLS & Other Estimators

In a very recent article, titled "Conditions for the Equality of the OLS, GLS and Amemiya-Cragg Estimators" (currently "in press" at Economics Letters), Cuicui Lu and Peter Schmidt present various conditions under which various regression estimators will be numerically equivalent.

## Tuesday, February 7, 2012

### On the Asymptotic Properties of Sample Means

Last month, in a post titled "Extracting the Correct Mean(ing) From the Data" (here), I discussed some aspects of the arithmetic, geometric, and harmonic sample means.

In a subsequent comment, I was asked if the geometric mean (GM) and harmonic mean (HM) are consistent estimators of E[X], the (arithmetic) mean of the population. My first reaction was that they are, but a little further reflection shows otherwise.

## Saturday, February 4, 2012

### Influential People in the "Big Data" Field

Yesterday, Haydn Shaughnnessy wrote a piece for Forbes titled, Who are the Top 20 Influencers in Big Data?

Fans of R will be delighted to see David Smith of Revolution Analytics up there at number 2!

Congratulations!

© 2012, David E. Giles

### Minimizing the Length of a Confidence Interval

Right now I'm teaching an introductory course on statistical inference for Economics students. We've been dealing with confidence intervals, starting off (as usual) with one for the mean of a Normal population.

For a given confidence level, the shorter the interval is, the more "informative" it is. The question that then arises is how to make the interval as short as possible, everything else being equal? Good question!