More on Regression & Causality

Now back in town, I enjoyed seeing this thoughtful blog post from William M. Briggs. It relates to own recent post, Regression & Causation.

This is a big topic, with lots to be said, from various perspectives.
 

© 2012, David E. Giles

Regression & Causation

Recently I read, with interest, a thought-provoking paper by Bryan Chen & Judea Pearl. The paper is titled, "Regression and causation: A critical examination of econometrics textbooks".

Here's the abstract:

"This report surveys six influential econometric textbooks in terms of their mathematical treatment of causal concepts. It highlights conceptual and notational differences among the authors and points to areas where they deviate significantly from modern standards of causal analysis. We find that econometric textbooks vary from complete denial to partial acceptance of the causal content of econometric equations and, uniformly, fail to provide coherent mathematical notation that distinguishes causal from statistical concepts. This survey also provides a panoramic view of the state of causal thinking in econometric education which, to the best of our knowledge, has not been surveyed before."

Some Recent Papers on Granger Causality

My various posts on testing for Granger non-causality seem to have been quite popular with readers of this blog.

 

For example, see here, here, here, here, and also see the Word-Count block in the right side-bar of this page.

 

The literature involving applications of non-causality testing continues to grow, even though it is now 43 years since Granger's seminal paper on the subject.  Regrettably, some of these applications are lacking in various respects, but there are many that are really excellent examples of  applied econometric analysis.

 

Contributions to the various theoretical and methodological issues surrounding the Granger causality literature also continue to emerge. Here are just a few such papers that have emerged in recent months.

• Matthieu Droumaguet & Tomasz Wozniak, 2012, "Bayesian testing of Granger causality in Markov-switchng VARs
• Christophe Hurlin & Elena Dumitrescu, 2012, "Testing for Granger non-causality in heterogeneous panels"
• Yushi Li, 2012, "Estimating long memory causality relationships by a wavelet method
• Joachim Wilde, 2012, "Effects of simultaneity on testing Granger-causality - A cautionary note about statistical problems and economic misinterpretations"
• Tomasz Wozniak, 2012, Granger-causal analysis of VARMA-GARCH models"

These papers cover a lot of important ground, and they're well worth taking a look at if you have an interest in testing for Granger non-causality.

 

  

© 2012, David E. Giles

Assistant Prof. Position at UVic

Not my usual sort of post, I know, but I just wanted to get it out there that my department (Economics, at the University of Victoria, on the West coast of Canada) is looking to hire a tenure-track Assistant Professor. The details of the position are available here.

 

We've only just got permission to hire, so we're really scrambling to catch up with this year's job market. Anything that you can do to get the word out to likely applicants, placement officers, etc. would be a great help to us.

 

Any enquiries should be addressed directly to econfacultysearch@uvic.ca.

 

 

© 2012, David E. Giles

Econometric Modelling With Time Series

That sounds like a snappy title, but it's been taken already!

There's a new econometrics book that's about to be released that looks really interesting. It's titled, Econometric Modelling With Time Series: Specification, Estimation and Testing. To be published by Cambridge University Press next month, this volume caught my eye, not only because of its title, but also because one its co-authors is a former Monash U. colleague of mine, Vance Martin (now at the University of Melbourne). Vance is joined by co-authors Stan Hurn and David Harris.

Is the Cochrane-Orcutt Estimator Unique?

One of the work-horses of econometric modelling is the Cochrane-Orcutt (1949) estimator, or some variant of it such as the Beach-MacKinnon (1978) full ML estimator. The C-O estimator was proposed by Cochrane and Orcutt as a modification to OLS estimation when the errors are autocorrelated. Those authors had in mind errors that follow an AR(1) process, but it is easily adapted for any AR process.

I've blogged elsewhere about the the historical setting for the work by Cochrane and Orcutt.

Given the limited computing power available at the time, the C-O estimator was a pragmatic solution to the problem of obtaining the GLS estimator of the regression coefficients, and approximating the full ML estimator. Students of econometrics will be familiar with the iterative process associated with the C-O estimator, as outlined below.

The use of this estimator leads to some interesting questions. Is this iterative scheme guaranteed to converge in a finite number of iterations? Is there a unique solution to this convergence problem, or can multiple local solutions (minima) occur?

Assessing Heckman's Two-Step Estimator

Good survey papers are worth their weight in gold. Reading and digesting a thoughtful, constructive, and well-researched survey can save you a lot of work. It can also save you from making poor choices in your own research, or even from "re-inventing the wheel".

For these reasons, The Journal of Economic Surveys is a great resource. Over the years it has published some really fine peer-reviewed survey articles, many of which I've benefited from personally.

Another piece of good news is that Wiley (the journal's publisher) makes a number of the most highly-cited articles available for free.

Failing the "Sniff Test"

If it looks like garbage, and smells like garbage, it probably is garbage! Insert any four-letter word of your choice, as long as it begins with "S" or "C", in place of "garbage".

HT to my former colleague, Peter Cribbett, for drawing my attention to this little gem:

A Very Personal Thank You

Just two words, but from the heart. It being Remembrance Day, I'm led to reflect on the contributions and sacrifices that my father, Albert Thomas Giles made for me. An infantryman in the British army (three times wounded) in World War II he also sacrificed a great deal for the education of his children.

 

Inadvertently, Bert was also influential in my becoming an econometrician.

Granger Causality Testing in R

Today just gets better and better!

I had an email this morning from Christoph Pfeiffer, who follows this blog. Christoph has put together some nice R code that implements the Toda-Yamamoto method for testing for Granger causality in the context of non-stationary time-series data.

Given the ongoing interest in the various posts I have had (here, here, here & here) on testing for Granger causality, I'm sure that Christoph's code will be of great interest to a lot of readers.

Thanks for sharing this with us, Christoph.

© 2012, David E. Giles