R 2.14.0 Released

A Halloween treat for R users! Version 2.14.0 was released today. Among other things there are big improvements for parallel processing.

For a quick synopsis of the new "goodies", see the post on the Revolutions blog.

© 2011, David E. Giles

My Hero!

Keen-eyed followers of this blog may have noticed that if you view my complete profile you'll learn that one of my interests is "staying at home". Actually it's a (male side of the) family tradition - though my son, Matt, seems to be bucking the trend!

As accurate as this profile is, I do have a small confession to make. I stole the line about staying at home from Professor Sir Richard Stone - specifically, from his listing in Who's Who when he was still alive.

Quote of the Day

"Reality is a Dangerous Concept."

 Source: Jerzy Neyman: On Time Series Analysis and Some Related Statistical Problems in Economics. (A Conference With Dr. Neyman in the Auditorium of the Department of Agriculture, 10 April, 1937, 11 a. m., Dr. Charles F. Sarle presiding). In Lectures and Conferences on Mathematical Statistics, Delivered by J. Neyman at the United States Department of Agriculture in April 1937, Graduate School of the United States Department of Agriculture, Washington D.C., p. 112.

© 2011, David E. Giles

VAR or VECM When Testing for Granger Causality?

It never ceases to amaze me that my post titled "How Many Weeks are There in a Year?" is at the top of my all-time hits list! Interestingly, the second-placed post is the one I titled "Testing for Granger Causality". Let's call that one the number one serious post. As with many of my posts, I've received quite a lot of direct emails about that piece on Granger causality testing, in addition to the published comments.

One question that has come up a few times relates to the use of  a VAR model for the levels of the data as the basis for doing the non-causality testing, even when we believe that the series in question may be cointegrated. Why not use a VECM model as the basis for non-causality testing in this case?

If it Ain't Broke, Don't Fix It!

For the most part I like engineers. In fact, some of my best friends are engineers. However, there's always the exception that "proves" the rule!

Back in 1978 I was appointed to a chair (full professorship) in what was then the Department of Econometrics & Operations Research at Monash University, in Australia. It's now the Department of Econometrics & Business Statistics. It was, and still is, a terrific department to be associated with. My colleagues and our students were wonderful. and I had nine productive years there.

Sargent and Sims are Econometricians!

It's official! This year's Economics Nobel laureates, Thomas Sargent and Christopher Sims, are Econometricians! The latest issue of the Royal Statistical Society's Newsletter makes that very point, clearly and repeatedly!

So, back off you Empirical Macro. types! Chris Sims certainly teaches grad. courses in Econometric Theory and Time Series, and in his c.v. he describes his areas of research interest as: "econometric theory for dynamic models; macroeconomic theory and policy". Notice how econometric theory comes first? So there!

I admit that the case is less convincing in the case of Sargent - but it depends on how broadly you define "econometrics". The web page at NYU's Stern School certainly declares him to be a macroeconomist. But he's a past-President of the Econometric Society. And both he and Sims are Fellows of that august body.

That's close enough for me! ☺

© 2011, David E. Giles

A Moniacal Economist

"In 1958, A. W. H. Phillips published in Economica what was to become one of the most widely cited articles ever written in economics. To mark the 50th anniversary of the paper, the New Zealand Association of Economists and the Econometric Society hosted the conference “Markets and Models: Policy Frontiers in the A. W. H. Phillips Tradition” in July 2008."

(Economica, 2011, Vol 78, p.1)

The January  issue of the journal Economica this year was devoted to papers from the A. W. H. 50th Anniversary Symposium, in honour of the New Zealander, A. W. H. ("Bill") Phillips, well-known to economists for his development of the so-called "Phillips Curve". I strongly recommend this issue of the journal.

Cook-Book Statistics

In my post yesterday I wrote about a remark made by the well-known statistician, Michael Stephens. Here's another interesting comment from him, referring to his departure from post-World War II England for the U.S.:

"Out of the blue came an offer from Case Institute of Technology (now Case-Western Reserve) and off I went to Cleveland. Case had a huge Univac computer, all whirling tapes and flashing lights, and I decided to learn programming. Next to computing was Statistics, so I thought I would learn some of that too. I took some cook-book classes, and thought I was learning statistics. For the life of me, I can't understand why I didn’t wonder why the ratio of this to that would be called F and looked up on page 376."

(Michael Stephens, in A Conversation With Michael A. Stephens, by Richard Lockhart)

Seems I'm not alone when it comes to cook-book courses (and here)!

© 2011, David E. Giles

It's All in the Moments

On Thursday afternoons I'm usually to be found at the seminars organized by the Statistics group in our Department of Mathematics and Statistics. It's almost always the highlight of my week. Last week the Thursday gathering was replaced by a very special half-day event on the Friday: a mini-conference to honour the recent retirement of senior UVic statistician, Bill Reed.

On the Importance of Knowing the Assumptions

I've been pretty vocal in the past about the importance of understanding what conditions need to be satisfied before you start using some fancy new econometric or statistical "tool". Specifically, in my post, "Cookbook Econometrics", I grizzled about so-called "econometrics" courses that simply teach you do "do this", do that", without getting you to understand when these actions may be appropriate.

My bottom line: you need to understand what assumptions lie behind such claims as "this estimator will yield consistent estimates of the parameters"; or "this test has good power properties" - preferably before you get too excited about using the estimator or test and you cause too much damage. In other words, it's all very well to understand what problems you face in your empirical work (simultaneity, missing observations, uncertain model specification, etc.), but then when you choose some tools to deal with these problems, you need to be confident that your choices will achieve your objectives.

Congratulations, to Thomas Sargent & Christopher Sims

By now, you'll all know that the 2011 Nobel Prize in Economics has been awarded to Thomas Sargent (New York University) and Christopher Sims (Princeton University). To say that this is well deserved and overdue, is an under-statement. Congratulations!

The Rise & Fall of Multicollinearity

Boris Kaiser in the Public Economics Working Group of the Department of Economics, University of Berne in Switzerland writes:

"As a frequent reader of your blog, I consider it my honour as well as my duty to point your attention to the following graph:

It shows the relative frequency of appearance of the word in the realm of the literature, contained in Google Books, over the last 50 years.  [1960-2011; DG]  Clicking on this link here, you can see how I generated the graph."

It seems that we're well on the way to the eradication of this grossly over-rated concept, as predicted in my earlier post, "The Second Longest Word in the Econometrics Dictionary". Thank goodness for that!

I'll explain my relief in a subsequent post. Meantime, "thanks a bunch for doing your duty, Boris"!

© 2011, David E. Giles

Erratum!

Back in May I posted a piece titled, "Gripe of the Day". With that post I provided some EViews code to run homoskedasticity tests for Logit and Probit models. Unfortunately, there was a small error in the code. This has now been fixed, and there is a note to this effect on the Code page for this blog, and in the original post.

The error affected the test results only at the second or third decimal places.

HT to eagle-eyed Sven Steinkamp at Universität Osnabrück  for bringing the error to my attention.

© 2011, David E. Giles

Predicting the 2011 Nobel Laureate in Economic Science

" "This is the Oscars for nerds," says Paul Bracher, a chemist at the California Institute of Technology"

(Daniel Strain, Science, 21 September, 2011)

I swore I wouldn't do it, but in the end I couldn't stop myself! Everyone else is having their say about next Monday's award of the Economics Nobel Prize, so I couldn't sit on the sidelines any longer.

I know it's proper name is "The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel", but I'm going to be lax here. The big announcement will come at around 1:00p.m. CST on Monday 10th October this year. That's around 4 a.m. that day if you're on the West coast, like me, or 7a.m. in New York. If you're not the lucky one to get the wake-up call, then you can watch the announcement, live, here. I'll be joining you!

So, who will the recipient(s) be this year?

Keynes and Econometrics

Regular readers of this blog will know that I think it's important for students of econometrics to know something about the history of the discipline. So, let's pick a big-name economist at random, and see how he fitted into the overall scheme of things econometric.

John Maynard Keynes completed his B.A. with first class honours at the University of Cambridge in 1904. His B.A. in mathematics, that is. Subsequently, he was placed twelfth Wrangler in the Mathematical Tripos of 1905.

Making a Name for Yourself!

So you want to make a name for yourself? One way for an up-and-coming young econometrician to do this would be to come up with a new estimator or test that everyone subsequently associates with your name. For example, the the Aitken estimator; the Durbin-Watson test; the Cochrane-Orcutt estimator; the Breusch-Pagan test; White's robust covariance matrix estimator, etc.

This can be a bit risky - your new inferential procedure might not "catch on" as well as you hope it will. Worse yet, someone else might come up with a similar idea around the same time, and steal your glory.  A much safer way to make a name for yourself is to be the first to prove a result that has hitherto had everyone baffled.