Star Wars

Today, Ryan MacDonald, a UVic Economics grad. who works with Statistics Canada, sent me an interesting paper by Abel Brodeur et al.: "Star Wars: The Empirics Strike Back". Who can resist a title like that!

The "stars" that are being referred to in the title are those single, double (triple!) asterisks that authors just love to put against the parameter estimates in their tables of results, to signal statistical significance at the 10%, 5% (1%!) levels. A table without stars is like champagne without bubbles!

Mark Thoma on Empirical Macro

Mark Thoma has a really nice post today on his blog, Economist's View. It's titled, "Empirical Methods and Progress in Macroeconomics".

Students of econometrics, and anyone doing empirical work in (macro)economics, would benefit from reading what Mark has to say about the use of historical data vs. experimental data.

I won't spoil the story by repeating it here, but his bottom line is:

"I used to think that the accumulation of data along with ever improving empirical techniques would eventually allow us to answer important theoretical and policy questions. I haven’t completely lost faith, but it’s hard to be satisfied with our progress to date. It’s even more disappointing to see researchers overlooking these well-known, obvious problems – for example the lack of precision and sensitivity to data errors that come with the reliance on just a few observations – to oversell their results".

© 2013, David E. Giles

Being Unbiased Isn't Everything!

When we first learn about estimation, we encounter various properties that estimators might possess. Unless your first course in statistics or econometrics takes a fully Bayesian stance, then these properties will be ones based on the sampling distribution of the statistic that is being used as the estimator.

There are plenty of unsettling things that can be raised against the notion of the sampling distribution, but let's put those to one side here. In elementary courses, attention usually focuses on just the mean and variance of an estimator's sampling distribution. I'm not endorsing this - it's just a fact of life.

UVic Economics Honours Class

With all of the great work that our Ph.D. and M.A. students are doing, it's easy to overlook an equally important group of students in our department. Each year we have a small group of undergraduate students taking our "Honours" program, and they deserve special mention at this time of year.

This week, with classes over, and final exams underway, the students in the Honours class are making presentations of the research that they've been undertaking over the past few months. Their research projects are always interesting and well executed. Past Honours students have gone on to some of the best doctoral programs in Canada, and have acquitted themselves extremely well.

Here are the presentations given yesterday and today:

And in the Red Corner.........

Here's a paper that I think all students of Econometrics will benefit from reading: "The Widest Cleft in Statistics - How and Why Fisher Opposed Neyman and Pearson", by Francisco Louçã (2008).

National Centre for Econometric Research

Australia has one, but Canada doesn't. What is it?

This Week's Reading

This past week I've been somewhat pre-occupied with the final exams for my undergraduate Economic Statistics course, and graduate Econometrics, courses. However, I've still managed to get some reading done, including the following miscellaneous papers:

Half a Million

Getting close to half a million page-views ........ Thanks!

© 2012; David E. Giles

Seminar on Pre-test Estimation & Testing

Last Friday I gave a seminar in the Department of Mathematics and Statistics, here at UVic. The Statistics seminar series is always very enjoyable, and I really enjoy interacting with this friendly and capable group.

My talk was titled, "The Effects of Prior Hypothesis Testing on the Sampling Properties of Estimators and Tests: An Overview". Preliminary test ( or pre-test) estimation (& testing) was a research topic that I was heavily involved in for about a decade, from the mid 1980's to the mid 1990's. A lot of that work was done with Judith Clarke. I've been looking at some related problems again recently.

If you're interested in this topic, you'll find the slides from my talk here.

© 2013, David E. Giles

Piecing the Puzzle Together

One of the things that we all hope for is that our students will make connections between the material they encounter in one course, and the things they learn about in another course. You shouldn't "forget" what you know about macroeconomics when you go to your econometrics class, etc. In other words, we hope that students will look for the "big picture" as they learn.

Admittedly, that's often easier said than done. When you're sitting in class listening to someone talking about a particular statistical test, it's hard to see how this might tie in with something that your micro. prof. was talking about last week.

Connecting the sots across different subjects in the one discipline is difficult enough, but it's even more difficult to do this across different disciplines while you're still learning the material. 

Today, I gave my last undergraduate "statistical inference" class for the the term. It couldn't have ended on a better note. Here's why.

After the class, one of the students stopped to ask a question - not about today's material, but about a paper he'd been reading in the journal, Economic Modelling. The student had been writing an essay for an English course and had chosen a topic relating to the Greek financial crisis. The paper he'd been reading was an applied econometrics piece, and he realized that this all related to what we'd been doing in our introductory treatment of the linear regression model. His specific question was "what is a VAR model, and how does it relate to a simple regression model?"

A good question, of course, but what was even more rewarding for me was to see him putting the pieces of the puzzle together, by thinking across the different subjects that he's studying. Good job!

© 2013, David E. Giles