Comic-Book Econometrics

We often hear the term "Cookbook Econometrics" - usually used in a derogatory sense - and I've posted on this topic in the past. "Comic-Book Econometrics" is something completely different. It relates to a particular econometrics computing package, and a comic that I keep in my office.

No connection? Let's see......

On the Value of Econometric Training

I received the following piece from Angelo Melino today. I hadn't seen it before, and I just loved it! 

Angelo posts this for the students in his M.A. econometrics course, and I think it deserves to be brought to the attention of all of our students. You just never know!

A Must-Read Post for Econometrics Students

Confidence Intervals - and how to interpret them correctly?

Hopefully, the correct interpretation was emphasized, again and again, by your instructor is Intro. Stats. 100. Even so, it's alarming to see how many grad. students (and faculty!) still get it wrong.

The concept of a confidence interval was first introduced by Jerzy Neyman and his co-authors. I've posted about some of the history of this here, and noted that even a future Nobel Laureate didn't "get it" when Neyman presented the concept at a seminar!

With this in mind, a really nice post appeared on the Statistical Research blog today. Its title is, When Discussing Confidence Level With Others, and I strongly recommend it.

© 2013, David E. Giles

Large and Small Regression Coefficients

Here's a trap that newbies to regression analysis have been known to fall into. It's to do with comparing the numerical values of the point estimates of  regression coefficients, and drawing conclusions that may not actually be justified.

What I have in mind is the following sort of situation. Suppose that Betsy (name changed to protect the innocent) has estimated a regression model that looks like this:

               Y = 0.4 + 3.0X₁ - 0.7X₂ + 6.0X₃ +.....+ residual .

Betsy is really proud of her first OLS regression, and tells her friends that "X3 is two times more important  in explaining y than is X1" (or words to that effect).

Putting to one side such issues as statistical significance (I haven't reported any standard errors for the estimated coefficients), Is Betsy entitled to make such a statement - based on the earth-shattering observation that "six equals three times two"?

NBER-NSF Time-Series Conference

The 2013 NBER-NSF Time Series Conference is being hosted by the Federal Reserve Board, in Washington D.C. next month. You can read about this event here.

Even a cursory look at the conference program will convince you that there are some really interesting looking papers being presented by some top people at this conference.

Check out the program, and you'll see what I mean. I'm going to be contacting several of the authors for access to their papers and talks. 

© 203, David E. Giles

In Praise of a Good Abstract

When you're writing up your research, it's a good idea to keep in mind that a lot of the potential readers of your exciting new paper are going to be busy people. I'm not talking about journal editors and referees - they're busy too, but they have an obligation to read your paper carefully. 

The rest of us have no such obligation, so you have to convince us that your research results are as interesting and important to us as they are to you.

I read a lot of papers dealing with econometrics and various areas of statistics. I also "pass over" even more papers that come my way via emails, web pages, and the like. 

Sometimes I'm following particular researchers/authors because I know from past experience that their work will be of interest to me. Otherwise, the title might catch my eye, and then I'll go as far as reading the abstract, and maybe the concluding section. Depending on the impression I've gained by that stage, I may or may not read the paper itself.

I think that, in this respect, I'm pretty typical of most of my colleagues. So, that's why the abstract of your paper is crucially important.

dataZoa

You may have noticed that in the past few of days some promotional links for dataZoa^™ have appeared in the lower part of the right side-bar of this blog page.

Usually, I don't go in for this sort of thing. However, I decided to make an exception in this case. Just so you know - I was not asked to do this, I'm getting nothing out of this, and I have no financial interest in dataZoa^™.

I simply wanted to share information about a really, really, nice resource. So, let me tell you just a little about it, and then you can go and explore dataZoa™ for yourselves.

The Stats Chat Blog

Recently, I've begun following the Stats Chat blog. Run by the Department of Statistics at the University of Auckland - the largest statistics department in New Zealand or Australia (and the birthplace of R) - this blog apparently started in April of this year.

It's aim is:

"to foster discussion of data around us, particularly in the media, and build an archive of resources for the general public, journalists and teachers".

Although the posts may appear to have a heavy N.Z. orientation, the topics covered are definitely of interest to a broader audience. I especially recommend this blog to students with an interest in statistics and/or econometrics.

You'll see interesting material, presented by members of a talented statistics group that has a long history of excellence.

Footnote:

I love the Department's (and blog's) by-line:

"Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write." – H.G. Wells.

© 2013, David E. Giles

Great Data Charts Using WebGL

I had an email today from Matt Hergott, who wrote:

"I notice you place an emphasis on charts and graphs. Many analyses could be helped by your suggestion that software offer charts of the data before running a regression. Along these lines, you might want to look at my new website: www.artemis-econometrics.com .

It contains four interactive three-dimensional scenes pertaining to econometrics and finance. The first graph is a simulation that makes it easy to spot outliers in a regression with two explanatory variables. The 3-D interactivity is important because people generally need different perspectives to see where the residuals are located.

I programmed these charts in JavaScript and WebGL. Now that Microsoft has decided to include WebGL in the upcoming Internet Explorer 11, it means that all major desktop browsers will support WebGL in the near future. This could open up a new frontier in the communication of quantitative concepts and results."

Matt's charts are really impressive. I must confess I really didn't know anything about WebGL (my loss) until he brought it to my attention.

It looks as if there are exciting times ahead!

© 2013, David E. Giles

Unbiased Model Selection Using the Adjusted R-Squared

The coefficient of determination (R²), and its "adjusted" counterpart, really don't impress me much! I often tell students that this statistic is one of the last things I look at when appraising the results of estimating a regression model.

Previously, I've had a few things to say about this measure of goodness-of-fit  (e.g., here and here). In this post I want to say something positive, for once, about "adjusted" R². Specifically, I'm going to talk about its use as a model-selection criterion.