Sunday, September 1, 2019

Back to School Reading

Here we are - it's Labo(u)r Day weekend already in North America, and we all know what that means! It's back to school time.

You'll need a reading list, so here are some suggestions:

  • Frances, Ph. H. B. F., 2019. Professional forecasters and January. Econometric Institute Research Papers EI2019-25, Erasmus University Rotterdam.
  • Harvey, A. & R. Ito, 2019. Modeling time series when some observations are zero. Journal of Econometrics, in press.
  • Leamer, E. E., 1978. Specification Searches: Ad Hoc Inference With Nonexperimental Data. Wiley, New York. (This is a legitimate free download.)
  • MacKinnon, J. G., 2019. How cluster-robust inference is changing applied econometrics. Working Paper 1413, Economics Department, Queen's University.
  • Steel, M. F. J., 2019. Model averaging and its use in economics. Mimeo., Department of Statistics, University of Warwick.
  • Stigler, S. M., 1981. Gauss and the invention of least squares. Annals of Statistics, 9, 465-474. 
© 2019, David E. Giles

Tuesday, August 20, 2019

Book Series on "Statistical Reasoning in Science & Society"

Back in early 2016, the American Statistical Association (ASA) made an announcement in its newsletter, Amstat News, about the introduction of an important new series of books. In part, that announcement said:
"The American Statistical Association recently partnered with Chapman & Hall/CRC Press to launch a book series called the ASA-CRC Series on Statistical Reasoning in Science and Society. 
'The ASA is very enthusiastic about this new series,' said 2015 ASA President David Morganstein, under whose leadership the arrangement was made. 'Our strategic plan includes increasing the visibility of our profession. One way to do that is with books that are readable, exciting, and serve a broad audience having a minimal background in mathematics or statistics.' 
The Chapman & Hall/CRC press release states the book series will do the following:
  • Highlight the important role of statistical and probabilistic reasoning in many areas
  • Require minimal background in mathematics and statistics
  • Serve a broad audience, including professionals across many fields, the general public, and students in high schools and colleges
  • Cover statistics in wide-ranging aspects of professional and everyday life, including the media, science, health, society, politics, law, education, sports, finance, climate, and national security
  • Feature short, inexpensive books of 100–150 pages that can be written and read in a reasonable amount of time."
Seven titles have now been published in this series -

Measuring Society, by Chaitra H. Nagaraja (2019)
Measuring Crime: Behind the Statistics, by Sharon L. Lohr (2019)
Statistics and Health Care Fraud: How to Save Billions, by Tahir Ekin (2019)
Improving Your NCAA® Bracket with Statistics, by Tom Adams (2018)
Data Visualization: Charts, Maps, and Interactive Graphics, by Robert Grant (2018)
Visualizing Baseball, by Jim Albert (2017)
Errors, Blunders, and Lies: How to Tell the Difference, by David S. Salsburg (2017)

Readers of this blog should be especially interested in Chaitra Nagaraja's recently published addition to this series. Chaitra devotes chapters in her book to the topics of  Jobs, Inequality, Housing, Prices, Poverty, and Deprivation. I particularly like the historical perspective that Chaitra provides in this very readable contribution, and I recommend her book to you (and your non-economist friends). 

© 2019, David E. Giles

Wednesday, August 14, 2019

Check out What Happened at the 2019 Joint Statistical Meetings

Each year, the Joint Statistical Meetings (JSM) bring together thousands (6,500 this year) of statisticians at what's the largest gathering of its type in the world. The JSM represent eleven international statistics organisations, including the four founding organisations - The American Statistical Association (ASA), The International Biometric Society, The Institute of Mathematical Statistical, and The Statistical Society of Canada.

As a member of the ASA since 1973 I've attended a few of these meetings over the years, but unfortunately I didn't make it to the JSM in Denver at the end of last month. As always, the program was amazing.

Yesterday, the ASA released a searchable version of the 2019 program that contains downloadable files of the slides used by many of the speakers. You can find that version of the program here. When you go through the program, look for presentations that have blue (rectangular) "Presentation" button. Papers in sessions sponsored by the Business and Economic Statistics section of the ASA may be of special interest to you - but there's lots to choose from!

© 2019, David E. Giles

Tuesday, August 6, 2019

Including More History in Your Econometrics Teaching

If you follow this blog (or if you look at the "History of Econometrics" label in the word cloud in the right side-bar), you'll know that I have more than a passing interest in the history of our discipline. There's so much to be learned from this history. Among other things, we can gain insights into why certain methods became popular, and we can reduce the risk of repeating earlier mistakes!

When I was teaching I liked to inject a few historical facts/anecdotes/curiosities into my classes. I think that this brought the subject matter to life a little. The names behind the various theorems, tests, and estimators are those of real people, after all.

There are some excellent books on the history of econometrics, including those by Epstein (1987), Morgan (1990), and De Marchi and Gilbert (1991). (Also, see the short piece by Stephen Pollock, 2014.)

However, I think that we could do more in terms of making material about this history accessible to our students.

The Statistics community has gone much further in this direction, and we might take note of this.

The other day, Amanda Golbeck posted some very helpful links on the American Statistical Association's "History of Statistics Interest Group" community noticeboard.

Here's her posting in its entirety - and don't miss the first of her links:

"Why not include more history in your teaching? The History of Statistics Interest Group library has a collection of Activities for Classes:

We are pleased to let you know that Bob Rosenfeld has created 13 history of probability and statistics teaching modules, and he has kindly made them available for you to use in your classes! We hope you will find them to be useful.

Reading and Exercises on the History of Probability from the Vermont Mathematics Initiative, Bob Rosenfeld
Reading and Exercises on the History of Statistics from the Vermont Mathematics Initiative, Bob Rosenfeld
(Bob Rosenfeld was former Co-Director for Statistics and School-Based Research at the Vermont Mathenatics initiative, and the author of a number of books on the teaching of statistics to K-8 students. D.G.)

Most of Bob Rosenfeld's pieces are directly relevant to econometrics students. It would be nice to see more material about the history of our discipline that could be incorporated into introductory econometrics courses.


De Marchi, N. & C. Gilbert, 1990. History and Methodology of Econometrics. Oxford University Press, Oxford.

Epstein, R. J. 1987. A History of Econometrics. North-Holland, Amsterdam.

Morgan, M. S., 1991. The History of Econometric Ideas. Cambridge University Press, Cambridge.

Pollock, D. S. G., 2014. Econometrics - An historical guide for the uninitiated. Working Paper No. 14/05, Department of economics, University of Leicester.

© 2019, David E. Giles

Friday, August 2, 2019

Suggested Reading for August

Here are my suggestions for this month:
  • Bun, M. J. G. & T. D. Harrison, 2109. OLS and IV estimation of regression models including endogenous interaction terms. Econometric Reviews, 38, 814-827.
  • Dufour, J-M., E. Flachaire, & L. Khalaf, Permutation tests for comparing inequality measures. Journal of Business and Economic Statistics, 37, 457-470.
  • Jiao, X. & F. Pretis, 2018. Testing the presence of outliers in regression models. Available at SSRN:
  • Stanton, J. M., 2001. Galton, Pearson, and the peas: A brief history of linear regression for statistics instructors. Journal of Statistics Education, 9, 1-13.
  • Trafimow, D., 2019. A frequentist alternative to significance testing, p-values and confidence intervals. Econometrics, 7, 26.
© 2019, David E. Giles

Sunday, July 28, 2019

AAEA Meeting, 2019

The Agricultural and Applied Economics Association (AAEA) recently held its annual meeting in Atlanta, GA. You can find the extensive program here.

This year, I was fortunate enough to be able to attend and participate.

This was thanks to the kind invitation of Marc Bellemare, a member of the Executive Board of the AAEA, and (of course) a blogger whom many of you no doubt follow. (If you don't, then you should!) 

Marc arranged a session in which he and I talked about the pros and cons of The Cookbook Approach to Teaching Econometrics. The session was well attended, and the bulk of the time was devoted to a very helpful discussion-question-answer period with the audience.

As you'll know from some of my previous posts (e.g., here and here), I'm not a big fan of The Cookbook Approach - at least, not if it's the primary/sole way of teaching econometrics. Marc made the point that there's a place for this approach if it's adopted after more formal courses in econometrics. I'm in agreement with that.

I put together a few background talking-point slides for my short presentation. For what they're worth, you'll find then here.

I really enjoyed my time at the AAEA meeting, and I learned a lot. Thanks, Marc, and thank you to the participants!

© 2019, David E. Giles

Saturday, July 6, 2019

Seasonal Unit Roots - Background Information

A recent email query about the language that we use in the context of non-stationary seasonal data, and how we should respond to the presence of "seasonal unit roots", suggested to me that a short background post about some of this might be in order.

To get the most from what follows, I suggest that you take a quick look at this earlier post of mine - especially to make sure that you understand the distinction between "deterministic" seasonality" and "stochastic seasonality" in time-series data.

There's an extensive econometrics literature on stochastic seasonality and testing for seasonal unit roots, and this dates back at least to 1990. This is hardly a new topic, but it's one that's often overlooked in the empirical applications.

Although several tests for seasonal unit roots are available, the most commonly used one is that proposed by Hylleberg et al. (1990) - hereafter "HEGY". Depending on what statistical/econometrics package you prefer to use, you'll have at least some access to the HEGY test(s), and perhaps some others. For instance there are routines that you can use with R, stata, and Gretl.

The EViews package includes a rather complete built-in suite of different seasonal unit root tests for time series data with various periodicities - 2, 4, 5, 6, 7, and 12. This enables us to deal with trading-day weekly data, and calendar weekly data, as well as the usual "seasonal" frequencies. 

I'm not going to be going over the tests themselves here.

Rather, the objectives of this post are, first, to provide a bit of background information about the language that's used when we're talking about seasonal unit roots. For instance, why do we refer to roots at the zero, π, frequencies, etc.? Second, in what way(s) do we need to filter a time series in order to remove the unit roots at the various frequencies?

Let's begin by considering a quarterly time series, Xt (t = 1, 2, ........). We'll use the symbol "L" to denote the lag operator. So. L(Xt) = Xt-1; L2(Xt) = L(L(Xt)) = L(Xt-1) = Xt-2etc. In general, Lk(Xt) = Xt-k.

Monday, July 1, 2019

July Reading

This month my reading list is a bit different from the usual one. I've taken a look back at past issues of Econometrica and Journal of Econometrics, and selected some important and interesting papers that happened to be published in July issues of those journals.

Here's what I came up with for you:
  • Aigner, D., C. A. K. Lovell, & P. Schmidt, 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21-37.
  • Chow, G. C., 1960. Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591-605.
  • Davidson, R. & J. G. MacKinnon, 1984. Convenient specification tests for logit and probit models. Journal of Econometrics, 25, 241-262.
  • Dickey, D. A. & W. A. Fuller, 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072.
  • Granger, C. W. J. & P. Newbold,  1974. Spurious regressions in econometrics. Journal of Econometrics, 2, 111-120.
  • Sargan, J. D., 1961. The maximum likelihood estimation of economic relationships with autoregressive residuals. Econometrica, 29, 414-426. 
© 2019, David E. Giles

Friday, June 21, 2019

Consulting Can be Fun!

Over the years, I've done a modest amount of paid econometrics consulting work - in the U.S., New Zealand, Australia, the U.K., and here in Canada. Each job has been interesting, and rewarding, and I've always learned a great deal form the briefs that I've undertaken.

The other day, a friend asked me, "Which consulting job was the most fun?"

Actually, the answer was easy!

A few years ago I consulted for the Office of the Auditor General of Canada, in Ottawa. I was brought in because I had consulted for Revenue New Zealand on the issue of tax evasion, and I had co-authored a book on the Canadian "underground economy" with Lindsay Tedds.

So what was the consulting work with the Auditor General's office all about? Well, they were conducting an audit of what was then called Revenue Canada (now, the Canadian Revenue Agency). In other words, "the tax man"!

Although the report arising from this audit is a matter of public record, I won't go into it here. 

Suffice to say, what could be more fun that conducting an audit of your country's tax authority?

© 2019, David E. Giles

Thursday, June 20, 2019

2019 Edition of the INOMICS Handbook

I'm sure that all readers will be familiar with INOMICS, and the multitude of resources that they make available to economists.

The INOMICS Handbook, 2019 is now available, and I commend it to you.

This year's edition of the Handbook includes material relating to:
  • The gender bias in the field of economics
  • The soft skills you need to succeed as an economist
  • Climate change and how economics can help solve it
  • What makes a successful economist
  • An exclusive interview with Princeton Professor, Esteban Rossi-Hansberg
  • Winners of the INOMICS Awards 2019
  • Recommended study and career opportunities
© 2019, David E. Giles