A Good Time to be an Economics Grad. Student?

Last Thursday, Neil Shah had this piece in the Wall Street Journal.

There'a a battle going on between the top U.S. economics departments for new and established talent. Of particular interest to economics grad. students, though:

"Established or not, economists are hot commodities. Last year, the average starting salary for new assistant economics professors was nearly $112,000 – the highest ever in inflation-adjusted terms and one of the highest across academic departments, according to the American Economic Association."

© 2012, David E. Giles

R Videos - and More

Some of us learn easily from the written word, but for most of us some visualization speeds up the process and generally helps with retention as well.

With that in mind I was delighted to see this nice list of free videos that demonstrate the use of R, posted on Ethan Fosse's blog, "Culture, Statistics, and Society".

Also of interest to econometricians will be Ethan's recent post outlining why he's dropped Stata completely in favour of R.

I'll be following his blog, for sure.

© 2012, David E. Giles

Bayes Estimators, Loss Functions, and J. M. Keynes

As a result of my recent post on Bayesian estimation of a simple consumption function, a few people emailed asking for proofs of the results that the Bayes estimator is the mean (a median) [a mode] of the posterior density, when the loss function is quadratic (absolute error) [zero-one].

Let's take a look at this, for the case of a single parameter.

Mathematicians and Economists

Tim Johnson posted a helpful comment on my recent post about Newton. This led tme to Tim's interesting blog called "Magic, Maths and Money".

I particularly enjoyed his two January posts there titled "Why Don't More Mathematicians See the Potential of Economics"; and "Why Don't More Economists See the Potential of Mathematics"

Good questions!

© 2012, David E. Giles

Newton and the Royal Mint

Sir Isaac Newton had a "day job" - Master of the Royal Mint, from 1699 until his death in 1727.

Stressful Times at Statistics Canada

An independent, well-funded, and well-staffed central statistical agency is a prerequisite for rational public policy-making. Statistics Canada is currently under a severe threat.

This is very bad news for those of us who use statistical data to do our jobs, but it's even worse news for the StatCan professionals who are directly affected.

© 2012, David E. Giles

Bias-Corrected MLEs

We all know that the Maximum Likelihood Estimator (MLE) is justified primarily on the basis of its desirable (large sample) asymptotic properties. Specifically, under the usual regularity conditions, the MLE is generally weakly consistent, asymptotically efficient, and its limit distribution is Normal. There are some important exceptions to this, but by and large that's what you get.

When it comes to finite-sample properties, the MLE may be unbiased or biased; efficient or inefficient; depending on the context. It can be a "mix and match" situation, even in the context of one problem. For instance, for the standard linear multiple regression model with Normal errors and non-random regressors, the MLE for the coefficient vector is unbiased, while that for the variance of the error term is biased.

As we often use the MLE with relatively small samples, evaluating (and compensating for) any bias is of some interest.

The Data Journalism Handbook

I found this post by Michael Blastland on The Guardian's DataBlog to be refreshing. It's good to see a collaborative international venture aimed at encouraging high quality data presentation and interpretation by journalists.

The Data Journalism Handbook was launched today, and it's a free open-source reference book.

© 2012, David E. Giles

Compulsory Reading for Seminar Attendees

I've aired some of my views on Economics seminars in the past - and especially how they compare with those in some other disciplines. For example, see here, here, and here.

Here's some homework reading from Peter Wood, to be completed before you go to your next Economics seminar!

Enjoy!

© 2012, David E. Giles

A Bayesian Consumption Function

What the title of this post is supposed to mean is: "Estimating a simple aggregate consumption function using Bayesian regression analysis".

In a recent post I mentioned my long-standing interest in Bayesian Econometrics. When I teach this material I usually include a simple application that involves estimating a consumption function using U.S. time-series data. I used to have this coded up for the SHAZAM package. EViews isn't appropriate as it doesn't include a numerical integration routine.

You could use BUGS, or some other package, but it's nice to see what is going on, step-by-step, when you're encountering this stuff for the first time.

The other day, I thought, "It's time to code this up in R". So, here we are!