February: my crazy teaching month

I’m having a crazy week of teaching this week. I’m teaching two lectures on fisheries economics tomorrow; an R practical in fisheries economic modelling and a lecture on dynamic programming on Wednesday; and a practical in dynamic programming in R and a lecture on ecosystems complexity on Thursday. Meanwhile I’ll be hosting a guest lecture on Wednesday (a speaker from Shell on energy policy) and on Friday (a speaker from Statistics Netherlands on environmental statistics). Next week I’m teaching two lectures on green national accounting, one on fisheries economics (different course), one on cost-benefit analysis, and hosting another two guest lectures.

It’s like this every year, but I enjoy it as much as it is exhausting. February is a very short teaching term at my university (don’t get me started on the merits, or lack thereof, of our academic calendar), where students are supposed to study one course, full time, in three to four weeks. I contribute to two courses in this term: one MSc course about natural resource economics and one at BSc level where we present six current topics in environmental economic policy from the viewpoint of academics and that of the real world. In the first course I get to tell students all about the topics I deal with most of my research time (fisheries economics, dynamic optimisation, ecosystem management); in the second course I can invite speakers to discuss the nitty-gritty of environmental policy-making with students. What’s not to like?
By the way, in addition to the R practical I revised my introductory text on dynamic programming after seeing how it worked at last year’s practical (not at all). As always, comments are welcome.

Why (for the time being) I’m sticking with R

I’m a big fan of open source software. OK, I know the Dutch have a reputation for being stingy but let’s face it: much of the software we use in economics (Stata, Matlab, Maple) is terribly expensive. So the only time I can use these programs is at the office (which, I admit, should be considered a healthy thing). To be able to work on my laptop when I’m at home (or in a hotel room, or in an airplane, for that matter) I try to work as much as I can with their open source equivalents as much as I can.

One of the programmes I’ve been using is R (a horrible name to Google for by the way), but in a sort of on-and-off way. It is less user-friendly than Matlab, much slower than Matlab, and contains fewer possibilities for statistical analysis than Stata. So I’m still fiddling around with programming languages like C++ (probably even faster than Matlab, but rabidly user-hostile) and Python (more user-friendly than C++, and perhaps as fast as Matlab) for calculations.

Slowly, however, I’m coming round to R, in my teaching as well as in my research, for a number of reasons:

  • Marine biologists use it a lot, and using the same software helps the communication – it also makes it more likely that you can ask a close colleague how this @#%! package works.
  • By the same token: some of my students, i.e. those who have taken marine ecology modelling courses, know it already.
  • I can use it in my environmental valuation classes (statistics) as well as in my resource economics classes (modelling), so that again, some students in one course know it from another course I’m teaching.
  • It seems that R finally has a decent package to do conditional logit and probit (or, as others call it, alternative-specific multinomial logit and probit).

If only they could make it a lot faster, because it is too slow for value function iteration.