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

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!!!

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

Listening to your Data

The latest issue of Significance Magazine (a joint publication of the Royal Statistical Society, and the American Statistical Association), includes an interesting article by Ethan Brown and Nick Bearman. It's titled, "Listening to Uncertainty: Information That Sings". 

The article is about "sonification" - listening to your data!

Some Properties of Non-linear Least Squares

You probably know that when we have a regression model that is non-linear in the parameters, the Non-Linear Least Squares (NLLS) estimator is generally biased, but it's weakly consistent. This is the case even if the model has non-random regressors and an additive error term that satisfies all of the usual assumptions.

In addition, even if the model’s errors are normally distributed, the NLLS estimator will have a sampling distribution that is non-normal in finite samples, and the usual t-statistics will not be Student-t distributed in finite samples.

In this post I'll illustrate these, and some other results, by using a simple Monte Carlo experiment.

Central Limit Theorems

When we first encounter asymptotic (large sample) theory in econometrics, one of the most important results that we learn about is the Central Limit Theorem.  Loosely speaking we learn that if we aggregate together enough values that are sampled randomly from the same distribution, with a finite mean and variance, then this aggregate starts to behave as if it is normally distributed.

However, too few courses make it clear that this "classical" central limit theorem is just one of several such results. The one that assumes independently and identically distributed values is actually the Lindeberg-Lévy Central Limit Theorem. There are other, related, results that deal with less restrictive situations.

Viren Srivastava

Recently, a reader of this blog asked I could provide some information about the late V.K. Srivastava, and the substantial contributions that he made to econometrics and to statistics generally.

I'm more than happy to oblige, as Virendra (Viren) was a good friend of mine, a treasured co-author, and a very caring and humble individual.