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