More Sandwiches, Anyone?

Consider this my Good Deed for the Day!

A re-tweet from a colleague whom I follow on Twitter brought an important paper to my attention. I thought I'd share it more widely.

The paper is titled, "Small-sample methods for cluster-robust variance estimation and hypothesis testing in fixed effect models", by James Pustejovski (@jepusto) and Beth Tipton (@stats-tipton). It appears in The Journal of Business and Economic Statistics.  

You can tell right away, from its title, that this paper is going to be a must-read for empirical economists. And note the words, "Small-sample" in the title - that sounds interesting.

 Here's a compilation of Beth's six tweets:

Extreme Value Modelling in Stata

David Roodman wrote to me today, saying:

"I don’t know if you use Stata, but I’ve just released a Stata package for extreme value theory. It is strongly influenced by Coles’s book on EVT and the associated ismev package for R. Using maximum likelihood, it fits the generalized Pareto distribution and the generalized extreme value distribution, the latter including the extension to multiple order statistics. It also offers various diagnostic plots. There are already many sophisticated R packages for EVT. I suppose mine offers accessibility…and small-sample bias corrections. It can do the Cox-Snell correction for all the models, including with covariates (citing you for GPD, and promising a write-up for the rest). It also offers bias correction based on a parametric bootstrap. I’ve confirmed the efficacy of both bias corrections through simulations, for the GPD and GEV. I’m still tweaking the simulations, and they take time, but I hope to soon post some graphs based on them. The GPD results closely match yours.

Comments welcome. Please circulate to others who might be interested. To install the package in Stata, type “ssc install extreme”.  The help file contains clickable examples that reproduce most results in the Coles book. The web page is https://ideas.repec.org/c/boc/bocode/s457953.html."

The work on the Cox-Snell bias correction for the Generalized Pareto Distribution that David is referring to is Giles et al. (2015). You can find an earlier post about this work here, and you can download the paper here.

Update, 28/1/2015: See more at David's blog.

Reference

Giles, D. E., H. Feng, and R. T. Godwin, 2015. Bias-corrected maximum likelihood estimation of the parameters of the generalized Pareto distribution. Communications in Statistics - Theory and Methods, in press.

© 2015, David E. Giles

Can Your Results be Replicated?

It's a "given" that your empirical results should be able to be replicated by others. That's why more and more journals are encouraging or requiring that authors of such papers "deposit" their data and code with the journal as a condition of acceptance for publication.

That's all well and good. However, replicating someone's results using their data and code may not mean very much!

Can You Actually TEST for Multicollinearity?

When you're undertaking a piece of applied econometrics, something that's always on your mind is the need to test the specification of your model, and to test the validity of the various underlying assumptions that you're making. At least - I hope it's always on your mind!

This is an important aspect of any modelling exercise, whether you're working with a linear regression model, or with some nonlinear model such Logit, Probit, Poisson regression, etc. Most people are pretty good when it comes to such testing in the context of the linear regression model. They seem to be more lax once they move away from that framework. That makes me grumpy, but that's not what this particular post is about.

It's actually about a rather silly question that you sometimes encounter, namely: "Have you tested to see if multicollinearity is a problem for your results?"

I'll explain why this isn't really a sensible question, and why the answer to the question in the title for this post is a resounding "No!"

Robust Standard Errors for Nonlinear Models

André Richter wrote to me from Germany, commenting on the reporting of robust standard errors in the context of nonlinear models such as Logit and Probit. He said he 'd been led to believe that this doesn't make much sense. I told him that I agree, and that this is another of my "pet peeves"!

Yes, I do get grumpy about some of the things I see so-called "applied econometricians" doing all of the time. For instance, see my Gripe of the Day post back in 2011. Sometimes I feel as if I could produce a post with that title almost every day!

Anyway, let's get back to André's point.

Attention, Stata Users

I've mentioned the Econometrics by Simulation blog before. Although it's still relatively new, it's had some great posts, and Francis Smart is doing a terrific job, as is reflected in the way that the page view numbers are building up.

Definitely worth a look, especially (but not only) if you're a Stata user.

© 2012, David E. Giles

EconometricsbySimulation

Francis Smart, a grad. student at Michigan State U., has written to me about his blog - EconometricsbySimulation.

As Francis says, "It is primarily a resource for students who want to learn new methods."

Francis has some nice Stata examples posted, and I'll certainly be following his blog.

© 2012, David E. Giles