Introduction to Applied Econometrics With R

I came across a January post from David Smith at Revolution Analytics, in his Revolutions blog. It's titled, An Introduction to Applied Econometrics With R, and it refers to a very useful resource that's been put together by Bruno Rodrigues of the University of Strasbourg. It's called Introduction to Programming Econometrics With R, and you can download it from here.

Bruno's material is a work in progress, but it's definitely worth checking out if you're looking for something to help economics students learn about R in an introductory statistics/econometrics course.

© 2015, David E. Giles

Applied Econometrics - 4 Volume Set

Back in 2012 I posted about a 4-volume set of readings, titled The Rise of Econometrics, edited by Duo Qin, and published by Taylor and Francis. That set appeared in January 2013.

In response to a comment on that post, Bill Greene has recently informed me that he is editing another comprehensive 4-volume set for T&F, due out later this year. This set is titled Applied Econometrics. It features 79 papers, in addition to the Editor's introduction.

The details of the Table of Contents can be found here.

This looks like a very comprehensive "must have" addition for any serious library.

© 2015, David E. Giles

Videos for EViews 9

The team at EViews has put together a great set of videos that highlight some of the new features in EViews 9.

You can find them here, and I strongly recommend them.

© 2015, David E. Giles

Introductory Statistics for Data Science

The latest issue of Chance contains a very timely article by Nicholas Horton, Benjamin Baumer, and Hadley Wickham. It's titled, "Setting the Stage for Data Science: Integration of Data Management Skills in Introductory and Second Courses in Statistics".

Ask yourself - "Is the traditional way that we teach introductory and second-level statistics courses really suited for preparing students for future work in modern data science?"

More specifically, do our undergraduate courses provide the data-related skills that are increasingly needed? The same question could be asked of undergraduate training in econometrics.

Horton et al. itemize five things which, in their opinion, deserve more attention in this context:

Edmond Malinvaud: A Tribute to his Contributions in Econometrics

I wrote this brief post just after Edmond Malinvaud passed away on 7 March of this year, at the age of 91. 

Peter Phillips' tribute to Malinvaud is a "must read" piece (see here).

Like Peter, I also used Malinvaud's text when undertaking my Masters-level studies in econometrics. It was demanding but ultimately exceptionally rewarding.

© 2015, David E. Giles

My Paper With Al Gol

My apologies for the broken link to my paper co-authored with Al Gol that was listed in the "April Reading" post on 1 April.

This has now been fixed.

© 2015, David E. Giles

My Favourite Book

Well, perhaps it's not really my favourite book, but it's certainly right up there with the most heavily thumbed tomes on my office bookshelf.

I'm referring to Tables of Integrals, Series and Products, by Gradshteyn and Ryzhik. I picked up a used copy of the 4^th ed. (1965) for about $5 some years ago at Powell's bookstore in Portland, and it's saved me more anguish and time than I can possibly estimate.

For example (click for LARGER version):

(Sample page)

The book is now in its 8^th edition (2014). You can download the 7th ed. (2007) on a pay-by-the-chapter basis from here, and you should be aware of the associated errata document.

I also came across this link.

© 2015, David E. Giles

Regression Coefficients & Units of Measurement

A linear regression equation is just that - an equation. This means that when any of the variables - dependent or explanatory - have units of measurement, we also have to keep track of the units of measurement for the estimated regression coefficients.

All too often this seems to be something that students of econometrics tend to overlook.

Consider the following regression model:

               y_i = β₀ + β₁X_1i + β₂x_2i + β₃x_3i + ε_i    ;    i = 1, 2, ...., n                   (1)

where y and x₂ are measured in dollars; x₁ is measured in Kg; and x₃ is a unitless index.

Because the term on the left side of (1) has units of dollars, every term on the right side of that equation must also be expressed in terms of dollars. These terms are β₀, (β₁x_1i), (β₂x_2i), (β₃x_3i), and ε_i.

In turn, this implies that β₀ and β₃ have units which are dollars; the units of β₁ are ($ / Kg); and β₂ is unitless. In addition, the error term, ε, has units that are dollars, and so does its standard deviation, σ.

What are some of the implications of this?

How (Not) to Interpret That p-Value

Thanks to my colleague, Linda Welling, for bringing this post to my attention: Still Not Significant.

I just love it! 

(Take some of the comments with a grain of salt, though.) 

© 2015, David E. Giles

Question from a Reader

Recently, I received an email from Ozan, who wrote:

"I’ve a simple but not explicitly answered question within the text books on stationary series. I’m estimating a model with separate single equations (I don’t take into account the interactions among them ). I’ve only non-stationary series in some equations (type 1), only stationary in some (type 2), and a combination of the both in the others (type 3). For the first two cases I apply the usual procedures and for the last case the Pesaran (2011) test. I want to find the short term effects of some variables on the others. I’ve two questions: 

1) If the Pesaran test turns out inconclusive or rejects cointegration, what’s the next step ? Differencing  all the series and applying an OLS? Or differencing only the non-stationary ones? Or another method?

2) As I mentioned I’m looking for the short-run effects. In the type 2 equations, I guess running an OLS in levels gives the long-run effects. Therefore I run an OLS in differences. Some claim that differencing an already stationary series causes problems. I’m confused. What do you think?"

Let's start out by making sure what Ozan means by "the usual procedures" for his "Type 1" and "Type 2" equations.

Inside the Econometric Game

Now that the Econometric Game, 2015 is over I can reveal the cases that the teams grappled with. The Case Makers were Bas Werker and Ramon van den Akker, both of Tilburg University.

You'll recall that thirty teams, from various parts of the world, played The Game this year. On Day 1, the teams received information about the topic, together with the data and the papers that they should read by way of preparation. As I mentioned here, the topic was "Longevity and Longevity Risk".

On Day 2, the competition began in earnest, with the teams being challenged with Case 1.

The top ten teams from Day 2 then moved to the final part of the competition. This involved working on Case 2 on the third day of The Game.

Now, what about the cases themselves?

And the Winners Are............

The results of the Econometric Game (AKA The World Championship of Econometrics) were decided in Amsterdam yesterday.

Here are the results for the 2015 competition:

First Place - Maastricht University

Second Place - University of Illinois at Urbana-Champaign

Third Place - Harvard University

Congratulations to each of these teams, and to all of the competing teams for a great event!

In her daily report to me, Nikki mentioned that the judges were unanimous about the placings, and she also commented that "...we are really happy that a Dutch team won."

I'll be following up with a final wrap-up post.

© 2015, David E. Giles

The Game Continues!

I'm pleased to be able to report on the events of the second day of The Econometric Game, 2015. Thanks, once again, to Nikki for providing the information for this post.

The end of Day 2 of the "Econometrics World Championships", today, saw the announcement of the ten teams that will go forward into the finals. Here they are:

• Universidad Carlos III de Madrid
• McGill University
• University of Illinois at Urbana-Champaign
• Harvard University
• University of Amsterdam
• Maastricht University
• Aarhus University
• University of Economics, Prague
• University of Copenhagen
• University of Antwerp

Great to see the McGill team doing well.

Nikki mentioned that there were loud cheers as each of the advancing teams was announced - especially in the case of the University of Copenhagen team (last year's winner). As you can see from the above list, they had to wait until nearly the end of the announcement before they knew their fate.

I also understand that the hosts (University of Amsterdam) are now in party mode - I bet they're not alone!

© 2015, David E. Giles

April Reading

April 1 already - time to update your reading list. Here are some suggestions:

• Baek, Y. I., J. S. Cho, and P. C. B. Phillips, 2015. Testing linearity using power transforms of regressors. 2015-RWP79, Economic Research Institute, Yonsei University.
• Gol, A. and D. E. Giles, 2015. HotGimmer: Random information. Working paper.
• Herwartz, H., N. Klein, and C. Strumann, 2015. Modelling hospital admission and length of stay by means of generalized count data models. Journal of Applied Econometrics, in press.
• Kabaila, P., R. Mainzer, and D. Farchione, 2015. The impact of a Hausman pretest, applied to panel data, on the coverage probability of confidence intervals. Economics Letters, in press.
• Lyhagen, J. and S. Ekberg, 2015. Beating the VAR: Improving Swedish GDP forecasts using error and intercept corrections. Journal of Forecasting, in press.
• Nadarajah, S., 2015. On the computation of Gauss hypergeometric functions. The American Statistician, in press.\
• Zhang, X. and A. Ullah, 2015. Group model averaging for finite sample size. Working Paper, Department of Economics, University of California, Riverside.

© 2015, David E. Giles