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

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, X_t (t = 1, 2, ........). We'll use the symbol "L" to denote the lag operator. So. L(X_t) = X_t-1; L²(X_t) = L(L(X_t)) = L(X_t-1) = X_t-2; etc. In general, L^k(X_t) = X_t-k.

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

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

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

More Tributes to Clive Granger

As a follow-up to my recent post, "Clive Granger Special Issue", I received an email from Eyüp Çetin (Editor of the European Journal of Pure and Applied Mathematics).

Eyüp kindly pointed out that "......... actually, we published the first special issue dedicated to his memory exactly on 27 May 2010, the first anniversary of his passing at https://www.ejpam.com/index.php/ejpam/issue/view/11 

We think this was the first special issue dedicated to his memory in the world. The Table of Contents may be found here https://www.ejpam.com/index.php/ejpam/issue/view/11/showToc .

Another remarkable point that we also published some personal and institutional tributes and some memorial stories for Sir Granger that never appeared elsewhere before at 

https://www.ejpam.com/index.php/ejpam/article/view/805 .

Some institutions such as Royal Statistical Society, Japan Statistical Society and University of Canterbury have sent their tributes to this special volume." 

© 2019, David E. Giles

Clive Granger Special Issue

The recently published Volume 10, No. 1 issue of the European Journal of Pure and Applied Mathematics takes the form of a memorial issue for Clive Granger. You can find the Table of Contents here, and all of the articles can be downloaded freely.

This memorial issue is co-edited by Jennifer Castle and David Hendry. The contributed papers include ones that deal with Forecasting, Cointegration, Nonlinear Time Series, and Model Selection.

This is a fantastic collection of important survey-type papers that simply must read!

© 2019, David E. Giles

Reading Suggestions for June

Well, here we are - it's June already.

Here are my reading suggestions:

• Abadie, A., S. Athey, G. Imbens, & J. Wooldridge, 2017. When should you adjust standard errors for clustering? Mimeo.
• Berk, R., A. Buja, L. Brown, E. George, A. K. Kuchibhotla, W. Su, & L, Shazo, 2019. Assumption lean regression. American Statistician, in press.
• Ghosh, T., M. Ghosh, & T. Kubokawa, 2019. On the loss robustness of least-square estimators, American Statistician, in press.
• Gustafsson, O. & P. Stockhammar, 2019. Variance stabilizing filters. Communications in Statistics - Theory and Methods, in press.
• Kocherlakota, N. R., 2019. A near-exact finite sample theory for an instrumental variable estimator. Mimeo. (Hat-tip to Frank Diebold.)
• Panagiotelis, A., G. Anathasopoulos, R. J. Hyndman, B. Jiang, & F. Vahid, 2019. Macroeconomic forecasting for Australia using a large number of predictors. International Journal of Forecasting, 35, 613-633.

© 2019, David E. Giles

Update on the "Series of Unsurprising Results in Economics"

In June of last year I had a post about a new journal, Series of Unsurprising Results in Economics (SURE).

If you didn't get to read that post, I urge you to do so. 

More importantly, you should definitely take a look at this piece by Kelsey Piper, from a couple of days ago, and titled, "This economics journal only publishes results that are no big deal - Here’s how that might save science".

Kelsey really understands the rationale for SURE, and the important role that it can play in terms of reducing publication bias, and assisting with replicating results.

You can get a feel for what SURE has to offer by checking out this paper  by Nick Huntington-Klein and Andrew Gill that they are publishing.

We'll all be looking forward to more excellent papers like this!

© 2019, David E. Giles

May Reading List

Here's a selection of suggested reading for this month:

• Athey, S. & G. W. Imbens, 2019. Machine learning methods economists should know about. Mimeo.
• Bhagwat, P. & E. Marchand, 2019. On a proper Bayes but inadmissible estimator. American Statistician, online.
• Canals, C. & A. Canals, 2019. When is n large enough? Looking for the right sample size to estimate proportions. Journal of Statistical Computation and Simulation, 89, 1887-1898.
• Cavaliere, G. & A. Rahbek, 2019. A primer on bootstrap testing of hypotheses in time series models: With an application to double autoregressive models. Discussion Paper 19-03, Department of Economics, University of Copenhagen.
• Chudik, A. & G. Geogiardis, 2019. Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables. Globalization Institute Working Paper 356, Federal Reserve Bank of Dallas.
• Reschenhofer, E., 2019. Heteroscedasticity-robust estimation of autocorrelation. Communications in Statistics - Simulation and Computation, 48, 1251-1263.

© 2019, David E. Giles