In Praise of T.A.s

With another teaching term completed, I'm reminded of how much we faculty members rely on our Teaching Assistants (T.A.s) This is especially true in the case of large undergraduate classes, where we'd be run off our feet without the invaluable input from these hard-working, often under-appreciated members of the teaching team.

Over the years, I've been especially fortunate to have worked with some very dedicated and conscientious T.A.s. Sometimes, being allocated to one of my courses wasn't their first choice. After all, introductory economic statistics isn't for everyone! None the less, they pitched in, worked hard, and the students in the courses were the beneficiaries. And so was I.

So, thank you all! And if you're a faculty member who relied on your T.A.s as much as I have, don't forget to let them know how important their work is, and how much it's appreciated.

© 2017, David E. Giles

Jan Kiviet's Book on Monte Carlo Simulation

Monte Carlo simulation is an essential tool that econometricians use a great deal. For an introduction to some aspects of Monte Carlo simulation, see my earlier posts here, here, and here. There are some follow-up posts on this coming up soon.

In the meantime, I was delighted to learn recently about an outstanding book on this topic by Jan Kiviet. The book is titled, Monte Carlo Simulation for Econometricians, and I strongly recommend it.

Of course, Jan's work will be familiar to many readers of this blog, and this book more than lives up to our expectations, given the author's excellent reputation.

Jan uses EViews to illustrate the various issues that are discussed in his book, making the material very accessible to students and researchers alike. 

This is a really nice contribution!

© 2017, David E. Giles

And the Winner is........

The Econometric Game for 2017 has concluded. For the second successive year, the winning team comes from Harvard University.

The "cases" that were used in the 2017 EG can be found here.

Congratulations to the winners, and to all of the other participating teams!

© 2017, David E. Giles

ARDL Models - From the Team at EViews

Today the team at EViews published an important post on their blog. It's titled, "Autoregressive Distributed Lag (ARDL) Estimation. Part 1 - Theory".

If you have an interest in ARDL modelling - and I know that there are lots of you out there - then this is a must read post.

And as you can tell from its title, there's also a follow-up post on the way. So, you should watch out for it.

If  you plan on doing any ARDL modelling, you really can't go past EViews, so take a look.

© 2017, David E. Giles

Read Some Econometrics this Month!

There are no April Fool's tricks in the following list of suggestions. 😐

• Antoine, B. and E. Renault, 2017. On the relevance of weaker instruments. Econometric Reviews, online.
• Golan, A. and A. Ullah, 2017. Interval estimation: An information theoretic approach. Econometric Reviews, online.
• Hendry, D. F. and P. C. B. Phillips, 2017. John Denis Sargan at the London School of Economics. Cowles Foundation Discussion Paper No. 2082, Yale University.
• Ozonur, D., H. T. K. Akdur, & H. Bayrak, 2016. Comparisons of tests of distributional assumption in Poisson regression model. Communications in Statistics - Simulation and Computation, online.
• Vijayamohanan, P. N., 2017. How do you interpret your regression coefficients? MPRA Paper No. 76867.
• Witmer, J., 2017. Bayes and MCMC for undergraduates. American Statistician, online.

© 2017, David E. Giles