The Sage on the Stage

As a new teaching semester gets underway, it's interesting to ask: "Is there still a case to be made for the traditional academic lecture as an effective aid to learning?" 

New Year Resolutions

Well, here we go again! It's time of year that we make all of those resolutions - the ones that usually get broken before the holiday decorations have been packed away. Not this year, though!

Congratulations to Donald Fraser!

Canadian statistical icon, Donald A. S. Fraser, has been appointed an Officer of the Order of Canada (O.C.) in the New Year's Honours list that was announced yesterday. Fraser has been Professor in the Department of Statistics at the University of Toronto since 1949, and he's received numerous international awards. His highly influential work has resulted in seven books and over 250 peer-reviewed papers (see his c.v.), and he has supervised 55 Ph.D. students.

Officers of the Order of Canada are appointed for their "lifetime of achievement and merit of a high degree, especially in service to Canada or to humanity at large."

How appropriate!

© 2011, David E. Giles

BIG Data

"Big Data" = data that come in amounts that are too large for current computer hardware and software to deal with. That sounds like fun!

When is the OLS estimator BLU?

Or, if you prefer, "When do the OLS and GLS estimators coincide?"

O.K., so you think you know the answer to this one? My guess is that you know a sufficient condition, but probably not a necessary and sufficient condition, for the OLS and GLS estimators of the coefficient vector in a linear regression model to coincide. Let's see if I'm right!

Just Crantastic!

If you're an econometrician and you don't use the R software environment - for at least some of your work, then you're missing out on all sorts of (free!) opportunities.

Festive Fun for all of the Family?

Courtesy of the Royal Statistical Society - The Great Significance Christmas Quiz!!

Information and Entropy Econometrics

The eminent physicist Ed. Jaynes (1957a) wrote:

"Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum entropy estimate. It is least biased estimate possible on the given information; i.e., it is maximally noncommittal with regard to missing information."

In other words, when we want to describe noisy data with a statistical model, we should always choose the one that has Maximum Entropy.

"An Information Theoretic Approach to Econometrics"

George Judge & Ron Mittelhammer have a new book, hot off the press: An Information Theoretic Approach to Econometrics (CUP, 2012).

Reported "Accuracy" for Regression Results

In a recent post I posed the question: "How many decimal places (or maybe significant digits) are appropriate when reporting OLS regression results?"