Econometrics Lectures on YouTube

I'm always keeping my eyes open for new or different resources that I can integrate into my Economic Statistics and Econometrics courses. For example, as I've mentioned before in previous posts (here and here), I've been really pleased with what I've been able to achieve with Wolfram's cdf files.

In my undergrad. Statistical Inference course I also refer the students to some of the excellent mini-lectures by Keith Bower. I find his presentations to be clear and (very importantly) accurate.

If you check out YouTube you'll find a number of video presentations relating to the teaching of econometrics. To be honest, many of them don't particularly impress me. Maybe I'm just hard to please!

There are some exceptions to this, though, including David Hendry's 20111 lecture on Teaching Undergraduate Economics at Oxford, and the great series of videos of Mark Thoma in action in the classroom. 

© 2013, David E Giles

Burgernomics

Looking through the papers that are "in press" at Economics Letters today, I came across a paper by Anthony Landry, titled "Borders and Big Macs". (The link is to the working paper version.) Here's the abstract:

"I provide new estimates of border frictions for 14 countries using local, national, and international Big Mac prices. I find that borders generally introduce only small price wedges, far smaller than those observed across New York City neighboring locations."

This led me to wonder just how many academic papers have been written using the well-known "Big Mac Index" (BMI) that's published annually by The Economist magazine. I don't know the exact answer, but there are 39 listed on RePEc's IDEAS site.

That's a lot of burgers!

© 2013, David E. Giles

A Visual Proof That OLS is BLU

Back in the day (as they say), we had monochrome monitors on our P.C.'s. Do you remember the ghastly green or weird amber colours? Then, one bright day everything became multi-coloured! This is not just me reminiscing - this is leading up to an innovative proof of the Gauss-Markhov Theorem. Honestly!

In a post yesterday, I mentioned Ken White's sense of humour - that's Ken White, "The SHAZAM Man", as my kids used to affectionately call him. On one of his many visits in the late 1980's, Ken offered to give a talk to a group of students about using the SHAZAM econometrics package. (We had no money for software at the time, but thanks to Ken's outstanding generosity we always had the latest version of his package for everyone to use.)

The Frequent Regressor Club

My friend, Ken White, developed the SHAZAM econometrics package in 1977. Ken's a funny guy - that's to say, he has a great sense of humour.

On one of his many visits to Christchurch, New Zealand (when I was living there, many years ago) he gave me a wooden die that he'd had an artisan carve at the local Arts Centre in Chistchurch. On each of the six faces he'd had the guy put the name of an econometrics/statistics package - TSP, LIMDEP, GAUSS, RATS, and SHAZAM. Yes, I know that's only 5 names. The thing was, SHAZAM appeared on two of the faces! The idea was to roll the die to decide which package to use in your lab. class. I still have the die - much to the occasional bemusement of students who see it on my desk.

Granger Causality Testing Done Properly

I enjoy following David Stern's Stochastic Trend blog. David is Research Director at the Crawford School of Public Policy at the Australian National University. He's an energy and environmental economist who does some really interesting work - not my field at all, but I always enjoy reading what he has to say.

In his latest blog post, David links to a recent paper that he's co-authored with Robert Kaufman, from Boston University. The paper is titled, "Robust Granger Causality Testing of the Effect of Natural and Anthropogenic Radiative Forcings on Global Temperature". 

As I said, this isn't my field. However, if you want to see an example of Granger causality testing done well, you should take a look at this well-written paper.

Nice one!

© 2013, David E. Giles

When Will the Adjusted R-Squared Increase?

The coefficient of determination (R²) and t-statistics have been the subjects of two of my posts in recent days (here and here). There's another related result that a lot of students don't seem to get taught. This one is to do with the behaviour of the "adjusted" R² when variables are added to or deleted from an OLS regression model.

We all know, and it's trivial to prove, that the addition of any variable to such a regression model cannot decrease the R² value. In fact, R² will increase with such an addition to the model in general. Conversely, deleting any regressor from an OLS regression model cannot increase (and will generally reduce) the value of R².

Mark Thoma on "Replication"

Yesterday, in his Economist's View blog, Mark Thoma discussed the importance of replicating results in empirical economics. He's absolutely right, of course.

I'll leave you to read what had to say, but I especially liked his closing passage:

"One place where replication occurs regularly is assignments in graduate classes. I routinely ask students to replicate papers as part of their coursework. Even if they don't find explicit errors (and most of the time they don't), it almost always raises good questions about the research (why this choice, this model, what if you relax this assumption, there's a better way to do this,here's the next question to ask, etc., etc.). So replication does occur routinely in economics, and it is very valuable, but it is not a formal part of the profession the way it should be, and much of the replication is done by people (students) who generally assume that if they can't replicate something, it is probably their error. We have a lot of work to do on the replication front, and I want to encourage efforts like this."

At least one of my colleagues also assigns replication exercises in this way, and I really should do the same. Fortunately, more journals are either recommending or requiring that data-sets be made available as a condition of publication. The Journal of Applied Econometrics is one such journal, and we've recently been pushing in that direction with the Journal of International Trade & Economic Development.

This should become part of our culture.

© 2013, David E. Giles

When Can Regression Coefficients Change Sign?

Let's suppose that you've been running regressions happily all morning. It's sunny day, but what could be better than enjoying some honest-to-goodness econometrics? Suddenly, you notice that one of the estimated coefficients in your model has a sign that's the opposite to what you were expecting (from your vast knowledge of the underlying economics). Shock! Horror!

Well. it's really good that you're on the look-out for that sort of thing. Congratulations! However, something has to be done about this problem.

Being young, with good eyesight, you also happen to spot something else that's interesting. One of the other estimated coefficients has a very low t-statistic. You have a brilliant idea! If you delete the variable associated with the very small t-value, maybe the "wrong" sign on the first coefficient will be reversed. Is this possible?

All About Spherically Distributed Regression Errors

This post is based on a handout that I use for one of my courses, and it relates to the usual linear regression model,

                                  y = Xβ + ε

In our list of standard assumptions about the error term in this linear multiple regression model, we include one that incorporates both homoskedasticity and the absence of autocorrelation. That is, the individual values of the errors are assumed to be generated by a random process whose variance (σ²) is constant, and all possible distinct pairs of these values are uncorrelated. This implies that the full error vector, ε, has a scalar covariance matrix, σ²I_n. 

We refer to this overall situation as one in which the values of the error term follow a “Spherical Distribution”. Let's take a look at the origin of this terminology.

Good Old R-Squared!

My students are often horrified when I tell them, truthfully, that one of the last pieces of information that I look at when evaluating the results of an OLS regression, is the coefficient of determination (R²), or its "adjusted" counterpart. Fortunately, it doesn't take long to change their perspective!

After all, we all know that with time-series data, it's really easy to get a "high" R² value, because of the trend components in the data. With cross-section data, really low R² values are really common. For most of us, the signs, magnitudes, and significance of the estimated parameters are of primary interest. Then we worry about testing the assumptions underlying our analysis. R² is at the bottom of the list of priorities.