Papers I've Been Reading

Here are some of the papers I've been reading over the past week. Hopefully, they'll also be of interest to readers of this blog.

In no particular order:

• M. M. Al-Sadun, 2013. Geometric and long run aspects of Granger causality.
• D. F. Hendry & F. Pretis, 2013. Some fallacies in econometric modelling of climate change.
• M. Blazejowski & J. Kwiatowski, 2013. Bayesian model averaging and jointness measures for gretl.
• P. Lavergne, 2013. Model equivalence tests in a parametric framework.
• D. I. Stern & K. Enflo, 2013. Causality between energy and output in the long-run. (Added, 16/03/2013: also see this paper.)
• J. M. Wooldridge, 2012. Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables.

© 2013, David E. Giles

Daylight Saving Time - A Natural Experiment

Early this morning, most of North America "sprang forward" to embrace the coming of Spring an hour sooner than we otherwise would have done. In Canada, the province of Saskatchewan was the notable exception. Ironic really - I'd have thought that with their climate they'd be pleased to get out of winter's icy grip an hour sooner!

Several researchers have noted that the phenomenon of daylight saving time offers a rather nice "natural experiment". We didn't always abide by this custom; now we do. At least, most us do, but some of us don't. In (most of) Canada and the U.S., the duration of DST was extended a couple of years ago.

What a great opportunity to use some econometric modelling to address questions such as: "Does the use of daylight saving reduce energy usage?" After all, as I understand it, this was one of the primary motivations for the introduction of this minor time warp. This just screams out "difference-in-differences" analysis!"

Here are a few links to some studies that have addressed the above question:

• Kellogg, R. & H. Wolff, 2007. Does extending daylight saving time save energy. Evidence from an Australian experiment. IZA DP No. 2704.
• Kotchen, M. J. & L. E. Grant, 2011. Does daylight saving time save energy? Evidence from a natural experiment in Indiana. Review of Economics and Statistics, 93, 1172-1185. (2008 Working Paper here.)
• Nadarjaze, R., H. Sadeghi, & Y. Gohli, 2012. An econometric analysis of the impact of daylight saving time (DST) on electric energy consumption in Iran. Quarterly Energy Economics Review, 8, 145-160.

Are there any econometric studies that focus on the "Saskatchewan versus the rest of Canada" aspect of this? I'm not aware of any, off hand. If there are, then I'd love to hear about them.

If not, then this would make a nice student project.

© 2013, David E. Giles

ARDL Models - Part I

I've been promising, for far too long, to provide a post on ARDL models and bounds testing. Well, I've finally got around to it!

"ARDL" stands for "Autoregressive-Distributed Lag". Regression models of this type have been in use for decades, but in more recent times they have been shown to provide a very valuable vehicle for testing for the presence of long-run relationships between economic time-series.

I'm going to break my discussion of ARDL models into two parts. Here, I'm going to describe, very briefly, what we mean by an ARDL model. This will then provide the background for a second post that will discuss and illustrate how such models can be used to test for cointegration, and estimate long-run and short-run dynamics, even when the variables in question may include a mixture of stationary and non-stationary time-series.

Measuring the Quality of an Estimator

In which, with almost no symbols, I encourage students and practitioners to question what they've been taught............

When it comes to introducing our students to the notion of the "quality" of an estimator, most of us begin by observing that estimators are functions of the random sample data, and hence they are "statistics" in the literal sense. As such, estimators have a probability distribution. We give this distribution a special name - the "sampling distribution" of the estimator in question.

It's understandable that students sometimes find the concept of the sampling distribution a little tricky when they first encounter it. After all, it's based on a "thought game" of sorts. We have to consider the idea of repeatedly drawing samples of a fixed size, for ever, constructing the statistic in question, and then keeping track of all of the possible values that the statistic can take, together with the relative frequency of occurrence for each value. A Monte Carlo experiment is the obvious way to introduce students to this concept.

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