The Demand for Easter Eggs in China

I was going to begin this post by saying "Now, here's a book I wish I'd written." But I got cold feet!

The 2011-2016 Outlook for Demand for Chocolate Easter Eggs in Greater China can be yours for only $495 from Amazon.com. That's the paperback edition, of course. Come on - I know you're tempted. I mean to say - chocolate!

Check it out for yourself, and as with everything chocolate - enjoy!

© 2013, David E. Giles

In Memorium, Shayle Searle

New Zealand-born statistician, Shayle R. Searle passed away recently in Ithaca, N.Y. - for further information, see here.

A great interview with Shayle, by Martin Wells, appeared in Statistical Science in 2010. One passage in that interview especially caught my attention. This is where Shayle is describing his early involvement with actuarial mathematics, and he says:

"After the 1949 M.A. exams I took a job as assistant to the actuary at Colonial Mutual Life Assurance Company in Wellington. I had no office of my own, but merely a desk in a large room with some dozen or so retirees who, day in and day out, were checking the weekly premiums paid for what were called industrial policies—something like twenty-five cents a week. The actuary’s office was but a few steps across the hall."

In the summer of 1968, part-way through my own undergraduate studies in mathematics and statistics in New Zealand, I worked in that very same CML office in Wellington. For better or worse, the experience persuaded me that an actuarial life was not for me!

Of the several influential books that Shayle published, my all-time favourite is his Linear Models (Wiley, 1971). It's a volume that I turn to regularly. It's a classic that I can recommend without reservation!

© 2013, David E. Giles

The Statistical & Social Inquiry Society of Ireland

The Statistical and Social Inquiry Society of Ireland (SSISI) has been in continuous existence since 1847. 

In The Spirit of Earnest Inquiry (1997), Mary E. Daly outlines the history of the Society, and makes the following observations:

"When the Statistical Society was founded in the mid-nineteenth century, societies that used statistics as a mechanism for investigating social questions were very much in vogue in Britain, continental Europe and in the United States. Most of these no longer exist; others have evolved into strictly professional bodies which tend to be dominated by full-time academics. The Statistical and Social Inquiry Society has not only survived but thrived, partly because until recently the small size of Ireland and the relatively undeveloped nature of professions such as economists and sociologists precluded the emergence of specialist representative organisations.

......... The modern Society is dominated by statistically-minded economists and sociologists, many of them employed within the public service and private business. Although academic members have always been prominent it is ranks, the Society has provided a particularly useful platform for people from outside the universities who are interested in research. The growing technical complexity of the papers read to the Society, their use of elaborate econometric and statistical methods, reflect the evolution of the economics profession within Ireland, and the more professional approach adopted to economic and social inquiry. Regrettably the membership of the Society and its concerns have narrowed."

Last year, on St. Patrick's Day, I posted about the famous Irish statistician, Roy Geary, who made many important contributions to both mathematical statistics and economic statistics. Geary was President of the Statistical and Social Inquiry Society of Ireland at the time of its centenary, in 1947.

Enjoy a pint in celebration of all Irish econometricians and statisticians today!

© 2013, David E. Giles

Recent Research by Former Grad. Students

It's always great to see the latest research from former grad. students - no matter how long since they "left the nest". Here's what some of my former students have been up to recently.

I've limited the items to one per person, and (as far as I can tell) just the latest contribution. In the case of recent grads., the joint research that is mentioned is not related to their thesis/dissertation work.

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.