Where are You Now?

One of the great thrills of this job is working together with students as they learn about econometrics. I've been most fortunate to have been associated with quite a few students who have had enough interest in the subject to subsequently go on to undertake graduate research with me.

Along the way, I've worked with some great people. They've been talented, dedicated, and a lot of fun to be around. If they learned anything from me, then I'm grateful - because I certainly learned from them, at least ten-fold.

I thought it was time to mention these students and to acknowledge their achievements. So, I've added a new Former Students page to this blog.

My fear is that I may have omitted someone from this Graduate Students page. My hope is that I'll hear from those of you who want to update me on your career progress and current position.

© 2011, David E. Giles

Still Searching for the Number of Weeks in a Year

When I put up a post titled "How Many Weeks Are There in a Year" back in April, little did I know how many hits it would get. For a while I was intrigued to see that visitors kept arriving. They still do - every day, without fail.

Then I realized the reason why. It wasn't because the readers of this post were in search of econometric enlightenment. Oh no! There was a much more obvious reason. They genuinely want to know the answer to the question posed in the title of that post!

A quick look at the blog "stats" revealed that there are some popular web search strings that lead these poor souls, no doubt kicking and screaming, to this site.

Beware of Econometricians Bearing Spreadsheets

"Let's not kid ourselves: the most widely used piece of

software for statistics is Excel"

(B. D. Ripley, RSS Conference, 2002)

What a sad state of affairs! Sad, but true when you think of all of the number crunching going on in those corporate towers.

With the billions of dollars that are at stake when some of those spreadsheets are being used by the uninitiated, you'd think (and hope) that the calculations are squeaky clean in terms of reliability. Unfortunately, you'd be wrong!

A huge number of reputable studies over the years - ranging from McCullough (1998, 1999), to the special section in Computational Statistics & Data Analysis in 2008 - have pointed out some of the numerical inaccuracies in various releases of some widely used spreadsheets. With reputations at stake, and the potential for litigation, you'd again think (and hope) that by now the purveyors of such software would be on the ball. Not so, it seems!

An Overly Confident (Future) Nobel Laureate

For some reason, students often have trouble interpreting confidence intervals correctly. Suppose they're presented with an OLS estimate of 1.1 for a regression coefficient, and an associated 95% confidence interval of [0.9,1.3]. Unfortunately, you sometimes see interpretations along the following lines: 

• There's a 95% probability that the true value of the regression coefficient lies in the interval [0.9,1.3].
• This interval includes the true value of the regression coefficient 95% of the time.

So, what's wrong with these statements?

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