It has been quite awhile since my last post. I can't say why. I'm no busier this semester than the last, aside from my discouraging hunt for summer employment. It goes without saying that now is not a particularly good time to be in the market for a job in finance.

But I digress. This semester I'm taking economics 250, a course in probability and statistics with economic applications. It's the first of two classes in quantitative methods for economists.

In an effort to calm students' outrage at textbook prices, my professor for economics 250 assigned "Statistics for Economists" (1972) by Ralph E Beals as the textbook for the course. On face, this is an absolutely brilliant idea. As my professor put it, the rules of probability haven't changed in the last 30 years. Thus, it makes no sense to spend $150 on a modern text when an older text, which is ostensibly the same, can be had for less than $20.

(As an aside, the Beals text was made available to students at a price of $17 in the form of a bound photocopy. I'm not sure who in the College's legal department approved this idea. Ralph Beals is still a living, breathing professor at Amherst, which means the textbook is still under copyright and will remain so for 70 years after his death.)

Of course something rather dramatic has changed over the last 30 years that pertains directly to statistics for economists: the price of computing power. According to data from the Dallas Fed, the price of 1 MHz of computing power in the 1970s was somewhere around $368,000 in 1970s dollars. Today, even looking at a relatively expensive computer, such as the new unibody MacBook Pro, the price of 1 MHz of computing power is only $0.41. Not to mention, it's much easier to program using an IDE and C than punchcards.

As such, most modern probability and statistics textbooks (and thus most probability and statistics courses) make extensive use of computers to run simulations and do data analysis.

This aspect of the text is not a problem per se. A professor could certainly augment the text with other material to make sure students leave the class with a working understanding of modern statistics software.

My professor, however, has chosen to subject the class to a hodgepodge of "simulations" in Microsoft Excel. As much as I love Microsoft's spreadsheet application, it's really no substitute for a real statistical analysis environment, such as R, Stata or SAS. And, given the limited scope of the simulations in Excel, students without any experience using Microsoft Excel learn how to use only a small set of relatively less useful tools in the software.

Running simulations can be extremely instructive if they're done well. But when it takes a half hour to setup a simulation with an n=100—a task that might take a few minutes even for the most inexperienced R programming—the simulation loses its pedagogical power. It makes it difficult, for instance, to illustrate the Central Limit Theorem.

In just a few minutes with Mathematica, for instance, I managed to whip up a dynamic, interactive simulator that ran anywhere from 1 to 100,000 Bernoulli trials and plotted the results in real time. I could then drag the n slider from 1 to 100,000 and watch as the PDF of the binomial distribution looked more and more like a normal distribution. And it only took a dozen or so lines of code! It's not rocket science!

I also take issue with the text at a more intrinsic level. Unlike most college-level probability and statistics courses, it does not require that students have even a little exposure to calculus. This confines the text to speaking about continuous probability distributions in vague generalities. Integrals are referred to as "shaded areas under the curve." Important subjects, like the Poisson, Beta and Gamma distributions are, as far as I can tell, not even mentioned in the text.

Once again, the best intentions do not necessarily produce the best results. I might be more forgiving of the text's lack of applications using modern software if my professor were more tech savvy. Unfortunately, he is not. This is not a problem per se. But it is annoying when I have to go out of my way to teach myself how to use R and Stata rather than consulting my professor or the assigned textbook.