Non-linear Functions of Non-Stationary Data Can be Stationary

I was at a conference the other day, and Peter Phillips made the comment that if we take the Sine or Cosine of a non-stationary time-series, then the Dickey-Fuller test will suggest that the transformed series is stationary. More specifically, this happens if the sample size is large enough.

That got me thinking, and searching, and eventually I came across a paper by Chien-Ho Wang and Robert M. de Jong (see the reference below). Indeed, they establish precisely the result that Peter was referring to.

Counting the Flowers - Again!

We're at it again! The annual (city of) Victoria Flower Count runs from today until 4 March. 

I attacked addressed this horticultural icon in a very early post on this blog a couple of years ago, and again last year. Basically, the flower count is an excuse for the people of this town to poke fun at those who live (?) in the real part rest of Canada. We remind them that while they're still attacking the snow and ice with a shovel and pick, we're picking the flowers that have already begun to bloom.

Here's my prediction for this year:

 1,475,588,000 blooms (forecast standard error = 2,257,716,000 blooms !)

 See the EViews workfile in the "Code" page for this blog for details.

© 2013, David E. Giles

Econometrics in New Zealand

Econometrics is alive and well in New Zealand! 

This comes as no surprise to me, given the long and illustrious history of the discipline in that country. and the continuation of that tradition was in evidence this past week at the 23rd meeting of the New Zealand Econometrics Study Group.

I mentioned in an earlier post that I was attending the meeting again this year, after an unconscionable gap of more than a decade. I'm really pleased that I was able to be there, at the University of Auckland, together with other participants from Japan, Korea, Australia, the U.K., Canada, and the U.S. - as well as many from New Zealand, of course.

The meeting was of the type that I really enjoy - a small group of enthusiasts sharing the multiple tasks of presenting and discussing papers, and chairing the sessions. It was great t meet new people, to see how the econometrics community is faring in New Zealand, and to catch up with former colleagues, former students, and students of former students!

The program covered a wide range of interesting material in econometric theory and applied econometrics. There was a bit of an emphasis on time-series econometrics and financial econometrics.  Empirical macroeconomics was well represented, but there wasn't the usual over-kill of empirical microeconomics papers. Not that I'm complaining about that - it was a breath of fresh air for me personally!

There were 25 presented papers, the quality of which was excellent, and these (not just the abstracts) are available here.

The highlight for me was set of presentations by the younger participants. These were really outstanding!

Prizes were presented for the best papers from the latter authors, and these went to ?Lorenzo Ductor (Massey University, N.Z.) and Yae in Baek (Yonsei University, Korea). Ole Maneesoonthorn (University of Melbourne) and Gael Price and Katy Bergstrom (both of the Reserve Bank of N.Z.) received well-deserved "honourable mentions". It must have been very difficult for the adjudication panel to separate all of these fine papers and presentations.

My thanks to Peter Phillips, Dimitris Margaretis, Taesuk Lee,  and everyone else involved in the organization and support of this excellent conference; and my congratulations to them for continuing to foster and promote the New Zealand tradition of excellence in econometrics.

I'll be back - if you'll have me!

© 2013, David E. Giles

Gretl Conference 2013

It was good to hear today from Riccardo (Jack) Luccetti, one of the developers of the Gretl econometrics package.

Jack wrote to draw my attention to the Gretl Conference 2013 that is being held in Oklahoma City in June of this year. This is the first time that the conference is being held in Nth. America.

Not sure if I will be able to make it, but it is very tempting!

© 2013, David E. Giles

Working as a Statistician at Google

If you love doing empirical work, you've probably wondered what it's like to work at an organization such as Google, where the term "Big Data" takes on a whole new meaning.

If so, you'll enjoy reading Jeff Leek's "Interview with Nick Chamandy, statistician at Google", on the Simply Statistics blog. Nick provides some interesting insights into the life of the statisticians/data analysts at Google, and the culture surrounding their work.

Here are a few excerpts:

"When posting job opportunities, we are cognizant that people from different academic fields tend to use different language, and we don’t want to miss out on a great candidate because he or she comes from a non-statistics background and doesn’t search for the right keyword. On my team alone, we have had successful “statisticians” with degrees in statistics, electrical engineering, econometrics, mathematics, computer science, and even physics. All are passionate about data and about tackling challenging inference problems." ..................................

"Our data sets contain billions of observations before any aggregation is done. Even after aggregating down to a more manageable size, they can easily consist of 10s of millions of rows, and on the order of 100s of columns." ......................

"In the vast majority of cases, the statistician pulls his or her own data — this is an important part of the Google statistician culture. It is not purely a question of self-sufficiency. There is a strong belief that without becoming intimate with the raw data structure, and the many considerations involved in filtering, cleaning, and aggregating the data, the statistician can never truly hope to have a complete understanding of the data."  .........................

I definitely approve of that philosophy.

Enjoy!

© 2013, David E. Giles

I Think It's Them!

The other day I was refereeing a paper, and I thought back to an earlier post from April of last year - "Is it Me or is it Them ??"

'Reminiscing', you say?

Hardly - it's just that I'm still reading far too many empirical (usually micro.) papers in which the econometric "analysis" leaves me shaking my head.

I'm not going to repeat the previous post (you can read it for yourselves), and I certainly can't afford another session with Jane right now.

However, I'm pleased to be able to report that I do think I'm making some progress with my issues!

I've decided that it's not me - it's them!

(That feels better.)

© 2013, David E. Giles

N.Z. Econometrics Study Group

Later this month I'll be participating at the 23^rd annual meeting of the New Zealand Econometric Study Group. As is often the case, the meeting is being held in Auckland - specifically at the University of Auckland.

The NZESG was spear-headed by Yale-based New Zealander Peter Phillips, and he's done a huge amount over the years to promote the continued excellence of the N.Z. econometrics scene.

At this month's meeting I'll be talking about some of my joint work (with Helen Feng and Ryan Godwin) on bias reduction in the context of maximum likelihood estimation. (See here for more on this topic.) It seems I'm also chairing a session and discussing a Peter's presentation of his paper, "On Confidence Intervals for Autoregressive Roots and Predictive Regression ".

It's been quite a while since I was able to get to an NZESG meeting, so I'm really looking forward to catching up with old friends, and seeing what's happening with the always-flourishing N.Z. econometrics community.

I'll report further on this on my return from New Zealand.

© 2013, David E. Giles

Befriend a Book

Books are pretty special to most academics - and to many, many other readers as well. Some of us are especially interested in old books whose very publication tells the story of the history and development of our discipline.

For example, I'm pleased to own copies of Aitken (1942), Aitken (1949), and (courtesy of a former grad. student) Turnbull and Aitken (1932). Some years ago, in Australia, I had some luck at a garage sale and picked up terrific copies of Karl Pearson's Early Statistical Papers (edited by his son, Egon Pearson, in 1948), The Selected Papers of E. S. Pearson, and Contributions to Mathematical Statistics. The latter volume comprises a large number of R. A. Fisher's papers, edited by W. A. Shewhart in 1950.

In 2009, the Royal Statistical Society began a program that they called "Befriend a Book". The objective is to raise funds to assist in the conservation of important books in the historical collection held by the Society.

The latest (February, 2013) issue of the RSS Newsletter contains an item that describes the progress that's being made with "Befriend a Book". For example, the first book to be restored was a copy of de Moivre's The Doctrine of Chances (1838). There are also some interesting pictures of old books. (At least, I found them to be interesting!)


References

Aitken, A. C., 1942.  Determinants and Matrices, 2nd. ed.. Oliver and Boyd, Edinburgh.

Aitken, A. C., 1949, Statistical Mathematics, 6th. ed., Oliver and Boyd, Edinburgh.

Turnbull, H. W. & A. C. Aitken, 1932, An Introduction to the Theory of Canonical Matrices, Blackie & Son, London. 

© 2013, David E. Giles

Granger Causality

It's interesting, to me, that the posts on this blog that have received (and continue to receive) the most hits are those relating to Granger causality. Or, more correctly, testing for Granger non-causality.

The top one of all time remains, "Testing for Granger Causality". (Maybe it's the catchy title?) Then, just behind "How Many Weeks Are There in a Year" (which has nothing to do with causality - at least, not in any  obvious sense), comes "VAR or VECM When Testing for Granger Causality?"

Moreover, in addition to the many comments/questions that are published with those posts, I get numerous emails on this topic - almost on a daily basis.

Of course, some of these are pretty predicable - essentially, they are asking me to do give them a research project; tell them how to write their paper; or else they want to me to tell them how to complete an assignment for some course they're taking!

But then there are the many, many thoughtful emails that ask interesting questions, and raise all sorts of issues that get me thinking. I really enjoy responding to as many of these as I can manage.

So, I've been thinking.

Is there a demand for a short monograph on testing for Granger causality, with the emphasis on the practice, not the theory. In other words, a "how to do it properly" book for non-specialists, with lots of real-data examples.

Any thoughts on this?

• Is there a need?
• What format should it take - printed or e-book?
• Does this sounds like something that might interest you and/or your students?

I'll be interested to see your feedback.

© 2013, David E. Giles

How Many Econometricians Does it Take?

In the December 2012 issue of Significance, a monthly magazine now published jointly by the American Statistical Association and the Royal Statistical Society, there's an interesting article titled, "Statistics of Statisticians: Critical Masses for Research Groups".

In this article, Ralph Kenna and Beretrand Berche discuss the idea of critical mass when it comes to the size of research groups in various disciplines. They explain how the so-called "Ringelmann effect" in sociology can be tested, and how this leads to measures of an upper bound on group sizes, "... above which research quality either tends not to improve or the rate of improvement starts to level out."