Conference and Seminar Papers - From Both Sides of the Podium

Here are three somewhat related posts from Rob Hyndman, Professor of Statistics in the Department of Econometrics and Business Statistics at Monash University. Rob blogs at Hyndsight.

I liked them all, and I commend them to you:

1. Giving a Research Seminar (2008)
2. Attending Research Seminars (2009)
3. Asking Good Questions (2013) - a guest post by Eran Raviv

For my own take on these matters, you might want to check here, here, and here.

© 2013, David E. Giles

Conference on Computing in Economics & Finance

The 19th. International Conference on Computing in Economics and Finance is coming up in Vancouver, B.C., this week. There's an interesting program.

One of my M.A. students, Yanan Li, and I have a paper that Yanan will be presenting at the conference. 

The paper is titled "Modeling Volatility Effects Between Emerging Asian Stock Markets and Developed Stock Markets".

© 2013, David E. Giles

Happy 40th!

I'm not sure if this is a good day or a bad day! I was feeling fine, and then this reminder of my advancing age arrived in the mail.........

Has it really been that long?

© 2013, David E. Giles

Musical Econometrics

There's a recent paper by McIntosh et al. (2013), titled "Listen to Your Data: Econometric Model Specification Through Sonification" that I really enjoyed. It's thought-provoking and innovative, and it takes things a lot further than what I mentioned in a previous post on data sonification.

Here's the abstract:

Allocation Models With Bounded Dependent Variables

My post yesterday, on Allocation Models, drew a comment to the effect that in such models the dependent variables take values that must to be non-negative fractions. Well, as I responded, that's true sometimes (e.g., in the case of market shares); but not in other cases- such as the Engel curve example that I mentioned in the post.

The anonymous comment was rather terse, but I'm presuming that the point that was intended is that if the y variables have to be positive fractions, we wouldn't want to use OLS. Ideally, that's so. Of course, we could use OLS and then check that all of the within-sample predicted values are between zero and one. Better still, we could use a more suitable estimator - one that takes the restriction on the data values into account.

The obvious solution is to assume that the errors, and hence the y values, follow a Beta distribution, and then estimate the equations by MLE. As I noted in my response to the comment, the "adding up" restictions that are needed on the parameters will be satisfied automatically, just as they are under OLS estimation.

Here's a demonstration of this.

Paper With Jacob Schwartz

It was nice to get the final "acceptance" yesterday for a paper co-authored with former grad. student, Jacob Schwartz.

The paper, titled "Bias-Reduced Maximum Likelihood Estimation of the Zero-Inflated Poisson Distribution", and with Jacob as lead author, will appear in Communications in Statistics - Theory & Methods. You can download a copy of the paper from here.

Jacob has been in the Ph.D. program at UBC for a while now. It seems quieter around the computing lab. without him!

© 2013, David E. Giles

Allocation Models

An "allocation model" is a special type of multi-equation model that has some interesting properties. This type of model arises quite frequently in applied econometrics, and it's worth knowing about it. In this post I'll explain what an allocation model is, and explore some of the estimation results that arise.

Ms DOS

They say that, with children, it doesn't get "easier", it just gets "different". Well, I'm not so sure. I have four grown-up "children" - they're are all successfully following their dreams, and I couldn't be happier!

So, I hope that Emma doesn't mind if I share this story from the mid 1990's, when her age was in single digits.

The Adjusted R-Squared, Again

In an earlier post about the adjusted coefficient of determination, R_A², I mentioned the following results that a lot of students don't seem to be aware of, in the context of a linear regression model estimated by OLS:

1. Adding a regressor will increase (decrease) R_A² depending on whether the absolute value of the t-statistic associated with that regressor is greater (less) than one in value. R_A² is unchanged if that absolute t-statistic is exactly equal to one. If you drop a regressor from the model, the converse of the above result applies.
2. Adding a group of regressors to the model will increase (decrease) R_A² depending on whether the F-statistic for testing that their coefficients are all zero is greater (less) than one in value. R_A² is unchanged if that  F-statistic is exactly equal to one. If you drop a group of regressors from the model, the converse of the above result applies.

The first of these results is (effectively) stated as Therorem 3.1 in Greene (2012), but the proof is left as an exercise.

In a comment on my previous  post, I was asked if I could supply simple proofs of these results.

Connections Between Univariate Distributions

I've been enjoying Francis X. Diebold's blog, No Hesitations. The other day he had a nice post on statistical graphics, and I found myself nodding (affirmatively) as I read through it. I won't repeat his points here, save to say:

• I, too, am a great fan of Edward Tufte. I have a couple of his books, and I used to use Minard's Napoleon chart in my introductory descriptive statistics courses.
• I have a copy of the chart of univariate statistical distribution relationships (Leemis et al., 2008) on my office wall. I was delighted to learn, from Francis's blog, that an interactive version of this chart is available.

The interactive version is definitely worth taking a look at.

© 2013, David E. Giles