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.

Turn on the Economy

"Turn on the economy". That's one of the invitations issued to (web) visitors to the museum of New Zealand's central bank - The Reserve Bank of New Zealand. Accepting this invitation will allow you to see a virtual version of Bill Phillips' famous MONIAC computer at work, and to "play" with the economy yourself.

Doesn't that appeal to you?

Bill Phillips - "the Indiana Jones of Economics" - gave us "the Phillips Curve", of course. However, the MONIAC computer was a revolutionary device that Bill used to demonstrate economic stabilization policy.

If you happen to be visiting New Zealand's capital city - Wellington - you can visit the Bank's (physical) museum, and see the MONIAC "in the flesh".

© 2013, David E. Giles

The Indiana Jones of Economics

All students of economics have heard about The Phillips Curve in one of its forms or another. The Phillips Curve is named after A. W. H. (Bill) Phillips, a remarkable New Zealander who made a number of fundamental contributions. His work, undertaken largely at the London School of Economics, dealt with stabilization policy, and modelling in continuous time, to name just two topics.

Bill Phillips was quite a character, and his varied life has been amply documented in various places. His entry in Wikipedia is a useful starting point, and the memorial piece written in 1978 by one of my former teachers, Brian Easton, is also a "must read" item.

Some time ago, I wrote about Bill in a post titled, "A Moniacal Economist", in reference to his famous MONIAC machines. These were hydraulic analogue computers that could be used to demonstrate the workings of the macro-economy.

In February of this year, the BBC Radio aired a piece about Bill Phillips, titled "The India Jones of Economics". In this 14-minute broadcast, Tim Hartford provides an interesting commentary of Bill's life and contributions to our discipline. You can download the broadcast - it's Episode 4, 6 February 2013 - from here.

More about Bill Phillips at a later date..............

© 2013, David E. Giles

My Recent Reading

Here are some of the papers that I have been reading in the past few days:

• Majid M. Al-Sadoon, 2013. Geometric and long run aspects of Granger causality. Discussion Paper, Barcelona Graduate School of Economics.
• David Ardia & Lennart Hoogerheide, 2013. GARCH models for daily stock returns: Impact of estimation frequency on value-at-risk and expected shortfall forecasts. Tinbergen Institute Discussion Paper.
• Otilia Boldia & Alastair R. Hall, 2013. Estimation and inference in unstable nonlinear least squares models. Journal of Econometrics, 172, 158-167. 
•  Kazuhito Higa, 2013. Estimating upward bias in the Japanese CPI using Engel's law. Working Paper, Hitotsubashi University.
• Anna Mikusheva, 2013. Survey on statistical inferences in weakly identified instrumental variables models. Applied Econometrics, 29, 117-131. 

© 2013, David E. Giles

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.