Sunday, June 25, 2017

Instrumental Variables & the Frisch-Waugh-Lovell Theorem

The so-called Frisch-Waugh-Lovell (FWL) Theorem is a standard result that we meet in pretty much any introductory grad. course in econometrics.

The theorem is so-named because (i) in the very fist volume of Econometrica Frisch and Waugh (1933) established it in the particular context of "de-trending" time-series data; and (ii) Lovell (1963) demonstrated that the same result establishes the equivalence of "seasonally adjusting" time-series data (in a particular way), and including seasonal dummy variables in an OLS regression model. (Also, see Lovell, 2008.)

We'll take a look at the statement of the FWL Theorem in a moment. First, though, it's important to note that it's purely an algebraic/geometric result. Although it arises in the context of regression analysis, it has no statistical content, per se.

What's not generally recognized, however, is that the FWL Theorem doesn't rely on the geometry of OLS. In fact, it relies on the geometry of the Instrumental Variables (IV) estimator - of which OLS is a special case, of course. (OLS is just IV in the just-identified case, with the regressors being used as their own instruments.)

Implicitly, this was shown in an old paper of mine (Giles, 1984) where I extended Lovell's analysis to the context of IV estimation. However, in that paper I didn't spell out the generality of the FWL-IV result.

Let's take a look at all of this.

Friday, June 23, 2017

Unit Roots & Structural Breaks

The open-access journal, Econometrics (of which I'm happy to be an Editorial Board member), has recently published a special issue on the topic of "Unit Roots and Structural Breaks". 

This issue is guest-edited by Pierre Perron, and it includes eight really terrific papers. You can find the special issue here.

© 2017, David E. Giles

Wednesday, June 7, 2017

Marc Bellemare on "How to Publish in Academic Journals"

If you don't follow Marc Bellemare's blog, you should do.

And if you read only one other blog post this week, it should be this one from Marc, titled, "How to Publish in Academic Journals". Read his slides that are linked in the post.

Great advice that is totally applicable to anyone doing research in econometrics - theory or applied.

© 2017, David E. Giles

Saturday, June 3, 2017

June Reading List

Here are some suggestions for you:
  • Ai, C. and E. C. Norton, 2003. Interaction terms in logit and probit models. Economics Letters, 80, 123-129.
  • Hirschberg, J. and J. Lye, 2017. Inverting the indirect - the ellipse and the Boomerang: Visualizing the confidence intervals of the structural coefficient from two-stage least squares. Journal of Econometrics, in press.
  • Kim, I. and S. Park, 2017. Likelihood ratio tests for multivariate normality. Communications in Statistics - Theory and Methods, in press.
  • Knotek, E. S. and S. Zaman, 2017. Financial nowcasts and their usefulness in macroeconomic forecasting. Working Paper 17-02, Federal Reserve Bank of Cleveland.
  • Marczak, M. and V. Gom√©z, 2017. Monthly US business cycle indicators: A new multivariate approach based on a band-pass filter. Empirical Economics, 52, 1379-1408.
  • Sherwood, C. and D. W. Kwak, 2017. New insights into an old problem - enhancing student learning outcomes in an introductory statistics course. Applied Economics, in press.
© 2017, David E. Giles

Tuesday, May 23, 2017

Staying on Top of the Literature

Recently, 'Michael' placed the following comment on one of my posts:
"Thanks for sharing this interesting list of articles! I'm wondering, how do you go about finding these types of articles to read? Are you a subscriber to these publications/do you regularly check for new updates online? I'd like to start keeping more up to date with academic articles, but I'm not sure where to start." 
Well, that's a good question, Michael. And I'm sure that there are many undergraduate students and non-academics who wonder the same thing when it comes to keeping up with the latest developments in econometrics. (I've phrased it that way because I'm also sure that grad. students will be getting appropriate advice on this, and other matters from their supervisors.)

Let's take a step back in time first.


Friday, May 19, 2017

The EViews Blog on ARDL - Part 3

As I mentioned in this recent post, the EViews team had a third blog post on ARDL modelling up their sleeves. The said post appeared a few days ago, here.

It's a real gem! The flow-chart and the detailed application are fabulous - I wish I could have come up with this myself.

Read it, read it................

© 2017, David E. Giles

When Everything Old is New Again

Some ideas are so good that they keep re-appearing again and again. In other words, they stand the test of time, and prove to be useful in lots of different contexts – sometimes in situations that we couldn’t have imagined when the idea first came to light.

This certainly happens in econometrics, and here are just a few examples that come to mind.

Tuesday, May 9, 2017

Bounds Testing & ARDL Models - More From the EViews Team

The team at EViews has just released another post about ARDL modelling on their blog. This one is titled, "AutoRegressive Distributed Lag (ARDL) Estimation. Part 2 - Inference". This post is a follow-up to one that they wrote last month, and which I commented on here.

Given by the number of comments and requests that I get about this topic, these two posts from EViews are "must read" items for a lot of you.

And the great news is that there's a third post on the way, and this one will focus on implementing ARDL Modelling/Bounds Testing in EViews,

Great job!
© 2017, David E. Giles

Friday, May 5, 2017

Here's What I've Been Reading

Here are some of the papers that I've been reading recently. Some of them may appeal to you, too:
© 2017, David E. Giles

Tuesday, April 18, 2017

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