Missing Keys and Econometrics

There couldn't possibly be any connection between conducting econometric analysis and looking for your lost keys, could there? Or, maybe there could!

Jeff Racine (McMaster U.) put a nice little piece up on his web page at the start of this month. It's titled Find Your Keys Yet?, and has the sub-title "Some Thoughts on Parametric Model Misspecification". Jeff rightly points out some of the difficulties associated with the concept of "the true model" in econometrics, and the importance of specification testing in the games we play.

BTW, this ties in with "Darren's" comments on my earlier post, Cookbook Econometrics.

Students of econometrics - please read Jeff's piece. Teachers of econometrics - ditto!

© 2011, David E. Giles

Levelling the Learning Paying Field

Whenever it comes time to assign a textbook for a course, I get the jitters. It's the price tag that always gets to me! And if it gets to me, then surely it must result in gasps of disbelief from the students (and parents) who are affected by my choices.

Often, I can (and do) make sure that the one text I assign can be used for two back-to-back courses. Hopefully, that helps a bit.

However, the cost of textbooks can still be a sizeable burden. Then, when students go to re-sell their texts the following year, they discover that those pesky publishing houses have churned out new editions! Guess what that does to the re-sale value of last year's purchase?

Playing fields (or paying fields in this case) would be level if the world were flat. Right? Right! Ideally, flat and at a height of zero. Zero dollars! That's exactly what Flat World Knowledge is all about.

Reproducible Econometric Research

I doubt if anyone would deny the importance of being able to reproduce one's econometric results. More importantly, other researchers should be able to reproduce our results to verify (a) that we've done what we said we did; (b) to investigate the sensitivity of our results to the various choices we made (e.g., functional form of our model, choice of sample period, etc.); and (c) to satisfy themselves that they understand our analysis.

However, if you've ever tried to literally reproduce someone else's econometric results, you'll know that it's not always that easy to so - even if they supply you with their data-set. You really need to have their code (R, EViews, STATA, Gauss) as well. That's why I include both Data and Code pages with this blog.

MoneyScience

MoneyScience - which describes itself as "the community resource for Quantitative Finance, Risk Management and Technology Practitioners, Vendors and Academics" - has recently released version 3 of its site.

There's a great deal of interesting work going on in "Financial Econometrics", and this is one site that provides really good content and excellent networking facilities that will help keep econometricians up to speed with what is going on in the finance community at large.

I'm pleased to be feeding this blog to MoneyScience (here),  and you'll notice a new icon near the bottom of the right side-bar:

© 2011, David E. Giles

Innovations in Editing

I've posted in the past (here and here) about some of my experiences as an academic journal Editor. It has its ups and downs, for sure, but ultimately it's a rewarding job.

Preston McAfee (Yahoo! & Caltech) is currently the Editor of Economic Inquiry, where he's introduced some important innovations into the editorial process. He was formerly Co-Editor of American Economic Review. As well as being a highly respected economist, Preston has a wonderful way with words.

His thoughts on journal editing make excellent reading, for seasoned academics and newcomers alike. I particularly recommend Preston's piece in the American Economist last year - here.

Reference

McAfee, R. P. (2010). Edifying editing. American Economist, 55, 1-8.

© 2011, David E. Giles

Visualizing Random p-Values

Here's a follow-up to yesterday's post on Wolfram's CDF file format. In an earlier post (here) I discussed the fact the p-values are random variables, with their own sampling distribution.

A great way of visualizing a number of the points that I made in that post is to use the CDF file for the Mathematica app. written by Ian McLeod (University of Western Ontario). You can run it/download it from here, using Wolfram's free CDF Player (here).

You'll recall that a p-value is uniformly distributed on [0 , 1] if the null hypothesis being tested is true. Using Ian's app., here is an example of what you see for the case where you're testing the null of a zero mean in a normal population, against a 2-sided alternative:

The null hypothesis is TRUE. The sample size is n = 50, and the simulation experiment involves 10,000 replications. You can see that the empirical distribution is approaching the Uniform true sampling distribution.

When the true mean of the distribution is 2.615 (so the null hypothesis is FALSE), the sampling distribution of the (two-sided) p-value looks like this:

If you're not sure why the pictures look like this, you might want to take a look at my earlier post, ("May I Show You My Collection of p-Values?")

© 2011, David E. Giles

Interactive Statistics - Wolfram's CDF format

Many of you will be familiar with Wolfram Research, the company that delivers Mathematica, among other things. Last month, they launched their new Computable Document Format (CDF) - it's something I'm going to be using a lot in my undergraduate Economic Statistics course.

Here are a few words taken from their press release of July 21:

Themes in Econometrics

There are several things that I recall about the first course in econometrics that I took. I'd already completed a degree in pure math. and mathematical statistics, and along with a number of other students I did a one-year transition program before embarking on a Masters degree in economics. The transition program comprised all of the final-year undergrad. courses offered in economics.

As you'd guess, the learning curve was pretty steep in macro. and micro, but I had a comparative advantage when it came to linear programming and econometrics. So things balanced out - somewhat!

That Darned Debt!

What with the recent S&P downgrading of the U.S., and the turmoil in the markets, it's difficult to watch the T.V. or read a newspaper without being bombarded with the dreaded "D-word". I was even moved to pitch in myself recently by posting a piece about the issue of units of measurement when comparing debt (a stock) with GDP (a flow).

Yesterday, Lisa Evans had nice post titled 20 Outlandish and Informative Ways to Illustrate the U.S. National Debt. I think you'd enjoy it!

Lisa runs a site called Masters in Economics. It's devoted to assisting students who are thinking of undertaking a Masters degree in Economics as a stepping stone into a career in Economics. Programs at this level are more widely available than you might have thought.

I'm hoping that Lisa will be able to expand her database to include the many programs at this level in Canadian schools.

Meantime, keep an eye on her site and watch for her future posts.

© 2011, David E. Giles

Flip a Coin - FIML or 3SLS ?

When it comes to choosing an estimator or a test in our econometric modelling, sometimes there are pros and cons that have to weighted against each other. Occasionally we're left with the impression that the final decision may as well be based on computational convenience, or even the flip of a coin.

In fact, there's usually some sound basis for selecting one potential estimator or test over an alternative one. Let's take the case where we're estimating a structural simultaneous equations model (SEM). In this case there's a wide range of consistent estimators available to us.

There are the various "single equation" estimators, such as 2SLS or Limited Information Maximum Likelihood (LIML). These have the disadvantage of being asymptotically inefficient, in general, relative the "full system" estimators. However, they have the advantage of usually being more robust to model mis-specification. Mis-specifying one equation in the model may result in inconsistent estimation of that equation's coefficients, but this generally won't affect the estimation of the other equations.

The two commonly used "full system" estimators are 3SLS and Full Information Maximum Likelihood (FIML). Under standard conditions, these two estimators are asymptotically equivalent when it comes to estimating the structural form of an SEM with normal errors. More specifically, they each have the same asymptotic distribution, so they are both asymptotically efficient.