What I Learned Last Week

Somewhat to my surprise, last month I got a great response to my post, "My Must-Read List" (HT's to Mark Thoma & Tyler Cowen). This past week I learned a lot by reading some terrific new papers on a variety of econometrics topics. Here they are, with some commentary, and in no particular order:

Degrees of Freedom in Regression

Yesterday, one of the students from my introductory grad. econometrics class was asking me for more explanation about the connection between the "degrees of freedom" associated with the OLS regression residuals, and the rank of a certain matrix. I decided to out together a quick handout to do justice to her question, and it occurred to me that this handout might also be of interest to a wider group of student readers.

So, here's what I wrote.

How Good is Your Random Number Generator?

Simulation methods, including Monte Carlo simulation and various forms of the bootstrap, are widely used by econometricians. We use these tools to learn about the sampling distributions of our estimators and tests, especially in situations where a purely analytic approach is technically difficult.

For example, sometimes we're able to appeal to standard asymptotic (large sample) results - such as the central limit theorems, and the laws of large numbers - to figure out how good our inferences will be if the sample size is very large. However, when it comes to the question of how good they are when the sample size is quite small, the answer may not be so easily established.

In addition, when we come up with a new theoretical result in econometrics, most of us take the precaution of also simulating the result - as check on its accuracy.

Monte Carlo and bootstrap methods rely critically on our ability to generate "pseudo"-random numbers that have the characteristics that we ascribe to them. How often have you actually checked  if the random number generators in your favourite econometrics package produce values that are "random", and follow the distribution that you've asked for? Probably not often enough!

I follow John Cook's blog, The Endeavour. A couple of years ago he had a nice post titled, "How to test a random number generator". In that post, he links to a chapter of the same title that he wrote for the book, Beautiful Testing (edited by Tim Riley and Adam Goucher).

John's chapter is a short, but very valuable read, and I recommend it strongly.

© 2012, David E. Giles

Top 100 Economics Blogs

I was happy to learn this morning that the Economics Degree website has just released a list of Top Economics Sites for Enlightened Economists. There are 100 blogs in total, and the preamble notes:

"Listed in no specific order, these sites are a must-see for anyone who wants to be considered a quality, “enlightened” economist. Sites were selected based on a variety of factors including readership size, update frequency, information quality, and other awards received. "

I was even more pleased to see that this blog made the list (see number 71 & remember they're in particular order!), with the following, very kind, description:

"This is a high-quality blog with a strong econometrics focus. The posts are jam-packed with information and ideas, and are clearly intended for readers with a background in statistics or econometrics."

(Blush. Blush.)

There are some great sites for students and professionals alike on this Top 100 list.

Nice job!

© 2012, David E. Giles

Mathematics, Economics, & the Nobel Prize

With the announcement of this year's Nobel Prize in Economic Science less than a week away, here's a recent working paper that you'll surely enjoy: "The use of mathematics in economics and its effect on a scholar's academic career", by Miguel Espinosa, Carlos Rondon, and Mauricio Romero. (Be sure that you download the latest version - dated September 2012.)

Here's the abstract:

"There has been so much debate on the increasing use of formal mathematical methods in Economics. Although there are some studies tackling these issues, those use either a little amount of papers, a small amount of scholars or cover a short period of time. We try to overcome these challenges constructing a database characterizing the main socio-demographic and academic output of a survey of 438 scholars divided into three groups: Economics Nobel Prize winners; scholars awarded with at least one of six prestigious recognitions in Economics; and academic faculty randomly selected from the top twenty Economics departments worldwide. Our results provide concrete measures of mathematization in Economics by giving statistical evidence on the increasing trend of number of equations and econometric outputs per article. We also show that for each of these variables there have been four structural breaks and three of them have been increasing ones. Furthermore, we found that the training and use of mathematics has a positive correlation with the probability of winning a Nobel Prize in certain cases. It also appears that being an empirical researcher as measured by the average number of econometrics outputs per paper has a negative correlation with someone's academic career success." (Emphasis added; DG)

The first of the highlighted conclusions doesn't surprise me. I'm not sure that I like the second one, though!

© 2012, David E. Giles

Seminar Attendance: A Pep-Talk for Grad. Students

Grad. students are busy, busy, people. That's true, no matter what discipline or what institution we're talking about. They're busy with their course-work, comprehensive exams, research, drinking beer, working as teaching assistants and research assistants, maintaining relationships with partners and children..............Grad. students even get to sleep every now and then!

So, something has to give. One way to grab an extra two or three hours each week is to avoid attending, and participating in, the research seminars put on by your department. Is that a smart choice, though?

Dancing With the Econometricians

Let's talk about the two-step. Not the tango or the polka. The two-step!

More specifically let's talk about a particular two-step estimator that we use all of the time in econometrics. I want to clear up some misconceptions that I seem to encounter all too frequently when I read empirical "applied" papers.

Why is it that some people insist on using the term "Two Stage Least Squares" inappropriately? 

Let me explain what I mean.

Congratulations!

Congratulations to Ryan Godwin for successfully defending his Ph.D. dissertation yesterday. Ryan's dissertation was titled, "Econometric Analysis of Non-Standard Count Data", and you can find the abstract on the notice for his defense here.

 

Ryan is now on faculty in the Department of Economics at U. Manitoba.

I'm looking forward to working with Ryan in the future.

 

 

© 2012, David E. Giles

My "Must Read" List

I have to confess that the number of items on my list of papers that I really must read (very soon) is rather large. My excuse is the same as everyone else's - too many papers, too little time. However, here's a small selection of of some of the papers that I've added to that list recently:

The Journal of Econometric Methods

The first issue of the Journal of Econometric Methods is available online, and you can register for a FREE trial access if your library doesn't already subscribe to a package that includes this journal.

Edited by Jason Abrevaya, Bo Honoré, Atsushi Inoue, Jack Porter, and Jeff Wooldridge, the Journal of Econometric Methods promises to be a "must read" publication. For now, issues will be published once a year.

Here's an extract from the "Editorial" of the first issue: