Close Encounters of the Math Kind

Alert readers of this blog may have noticed (front page) that I have an Erdös Number of 4. That's to say, I've published (several) papers co-authored with someone, who co-authored a paper with someone, who co-authored a paper with the mathematician Paul Erdös.

The Econometrics Channel

Coming very soon, to a YouTube near you - Numberphile!

A Real Econometrics Seminar

Last Friday we were treated to a particularly good seminar in our Department. Sílvia Gonçalves (here, at Université de Montréal) presented a paper, "Bootstrapping factor-augmented regression models" (joint with Benoit Perron). Yes, an actual Econometrics seminar!

Cointegration, Structural Breaks, and gretl

In a post in May I discussed testing for cointegration in the presence of structural breaks, and provided some EViews code to facilitate this. I then followed that up with another post in June that provided corresponding R code and a set of tables, both produced with Ryan Godwin.

Riccardo (Jack) Lucchetti, co-author of the (free) gretl econometrics package converted our code into gretl script, and kindly sent it me. Passing the script on to eveyone is long overdue - sorry about the delay, Jack!

VECMs, IRFs & gretl

In a comment on my post yesterday, "psummers" kindly pointed out that the free econometrics package, gretl, will also produce confidence intervals for Impulse Response Functions (IRFs) generated by a VECM.

I had an earlier post about gretl, and here is a very brief run-down on using it to produce those VECM-IRF confidence intervals.

Impulse Response Functions From VECMs

In the comments and discussion associated with an earlier post on "Testing for Granger Causality" an interesting question arose. If we're using a VAR model for constructing Impulse Response Functions, then typically we'll want to compute and display confidence bands to go with the IRFs, because the latter are  simply "point predictions". The theory for this is really easy, and in the case of EViews it's just a trivial selection to get asymptotically valid confidence bands.

But what about IRFs from a VECM - how do we get confidence bands in this case? This is not nearly so simple, because of the presence of the error-correction term(s) in the model. EViews doesn't supply confidence bands with the IRFs in the case of VECMs. What alternatives do we have?

R 2.14.0 Released

A Halloween treat for R users! Version 2.14.0 was released today. Among other things there are big improvements for parallel processing.

For a quick synopsis of the new "goodies", see the post on the Revolutions blog.

© 2011, David E. Giles

My Hero!

Keen-eyed followers of this blog may have noticed that if you view my complete profile you'll learn that one of my interests is "staying at home". Actually it's a (male side of the) family tradition - though my son, Matt, seems to be bucking the trend!

As accurate as this profile is, I do have a small confession to make. I stole the line about staying at home from Professor Sir Richard Stone - specifically, from his listing in Who's Who when he was still alive.

Quote of the Day

"Reality is a Dangerous Concept."

 Source: Jerzy Neyman: On Time Series Analysis and Some Related Statistical Problems in Economics. (A Conference With Dr. Neyman in the Auditorium of the Department of Agriculture, 10 April, 1937, 11 a. m., Dr. Charles F. Sarle presiding). In Lectures and Conferences on Mathematical Statistics, Delivered by J. Neyman at the United States Department of Agriculture in April 1937, Graduate School of the United States Department of Agriculture, Washington D.C., p. 112.

© 2011, David E. Giles

VAR or VECM When Testing for Granger Causality?

It never ceases to amaze me that my post titled "How Many Weeks are There in a Year?" is at the top of my all-time hits list! Interestingly, the second-placed post is the one I titled "Testing for Granger Causality". Let's call that one the number one serious post. As with many of my posts, I've received quite a lot of direct emails about that piece on Granger causality testing, in addition to the published comments.

One question that has come up a few times relates to the use of  a VAR model for the levels of the data as the basis for doing the non-causality testing, even when we believe that the series in question may be cointegrated. Why not use a VECM model as the basis for non-causality testing in this case?