Sunday, November 25, 2012

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?

Sunday, November 18, 2012

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.

Wednesday, November 14, 2012

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:

Sunday, November 11, 2012

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.

Wednesday, November 7, 2012

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

Former Students

It's always great to see our former grad. students making great progress with their chosen careers. Singling out individuals for special mention may be a little risky. But what the heck!!!

Monday, November 5, 2012

Bayesian Exercises

In the Advanced Topics in Econometrics course that I'm teaching this semester, one of the topics we're covering is Bayesian Econometrics. I've blogged a little on this topic before - e.g., here, here, here, and here.

If you want some practice exercises on Bayesian inference, you may be interested in this set of problems, as well as the assignment that my class is working on currently.

There's not much "econometric" content to the questions - they're more broadly statistical in nature. However, they cover some of the key ideas associated with this topic. Solutions will be posted later.

We're also looking at computational issues, such as MCMC. More on the latter in a different post, perhaps.

© 2012, David E. Giles