The T. D. Dwivedi Memorial Lecture

Yesterday, I was greatly honoured to present the 2nd. invited T. D. Dwivedi Memorial Lecture, in the Department of Mathematics & Statistics at Concordia University, in Montreal. The late Try Dwivedi was instrumental in the establishment of statistics at Concordia, and he also played a leading role in the development of the statistical profession in Canada generally

I'm linked to Dr. Dwivedi through the work that each of us did with V. K. (Viren) Srivastava. So, I have a "Dwivedi number" of 2.  You'll find more about this in an earlier post about Viren here.

The talk that I gave was titled, "Bas Adjustment for Nonlinear Maximum Likelihood Estimators", and you can download my slides from here if you're interested. The material for the lecture was based on a research program that I've been involved with in recent years, jointly with Helen Feng, Ryan Godwin, Jacob Schwartz, and others. A previous post on this blog discussed some of this research.

I'd like to thank the Dwivedi family, Yogen Chaubey (Department Chair), and the faculty of the Department of Mathematics and Statistics at Concordia University, for the kind invitation, their outstanding hospitality, and for a memorable visit to Montreal.

© 2013, David E. Giles

A First Encounter With Monte Carlo Simulation

In my second-year undergraduate course on Statistical Inference for economists, I use Monte Carlo simulation with EViews to illustrate the notion of the "sampling distribution" of a statistic, such as an estimator. This is hardly unusual. However, before we get started I have to persuade the students that this whole Monte Carlo thing might actually work!

So, we go through an exercise where we use simulation to approximate the value of π.

You Can Quote Me on That

The other day I came across the Empirical Quotes page on Mark Byran's blog. Some of his quotes related specifically to econometrics, and I thought I'd share a few others. That certainly doesn't mean that I agree with them all!

In Praise of Quandl!

Data - the econometrician's life-blood! Can't function without it.

So, when a new source of data becomes available - especially one that's sophisticated, reliable, and FREE - it's time to sit up and take notice. Quandl is a recent Canadian start-up that delivers economic and financial time-series data, and then some.

It's an interesting business model. When you go to Quandl, you link to the original sources of the data, you know when the data were last updated, and you get some basic graphical analysis before you download the numbers. 

If you're an R, MATLAB, ........... user, it's a breeze to import the data and get busy with the analysis. I've tried out the associated R package, and it's just great. As you can probably tell - I'm hooked!

I can see myself using Quandl a lot for teaching purposes, as well as for my research, and I suspect that my students will be equally enthusiastic.

© 2013, David E. Giles

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?