June Reading List

Put away that novel! Here's some really fun June reading:

• Berger, J., 2003. Could Fisher, Jeffreys and Neyman have agreed on testing?. Statistical Science, 18, 1-32.
• Canal, L. and R. Micciolo, 2014. The chi-square controversy. What if Pearson had R? Journal of Statistical Computation and Simulation, 84, 1015-1021.
• Harvey, D. I., S. J. Leybourne, and A. M. R. Taylor, 2014. On infimum Dickey-Fuller unit root tests allowing for a trend break under the null. Computational Statistics and Data Analysis, 78, 235-242.
• Karavias, Y. and E. Tzavalis, 2014. Testing for unit roots in short panels allowing for a structural breaks. Computational Statistics and Data Analysis, 76, 391-407.
• King, G. and M. E. Roberts, 2014. How robust standard errors expose methodological problems they do not fix, and what to do about it. Mimeo., Harvard University.
• Kuroki, M. and J. Pearl, 2014. Measurement bias and effect restoration in causal inference. Biometrika, 101, 423-437.
• Manski, C., 2014. Communicating uncertainty in official economic statistics. Mimeo., Department of Economics, Northwestern University.
• Martinez-Camblor, P., 2014. On correlated z-values in hypothesis testing. Computational Statistics and Data Analysis, in press.

© 2014, David E. Giles

It's Not A Blog .......

This gem from @AcademicSay on Twitter today:

"It's not a blog. It's an independent open-access journal with a dedicated submission agreement."

© 2014, David E. Giles

Questions About Granger Causality Testing - The Fine Print

Judging by the number of hits, comments, and questions that I've had in relation to my various posts on testing for Granger (Non-) Causality, this seems to be a topic that a lot of followers find interesting. For instance, see the posts here, here, here, and especially here.

In the comments, and in a large number of related emails that I've received, several questions seem to recur, and I thought it would be worth addressing them in one place - right here, to be specific!

The following discussion relates to the (usual) case where there is the possibility that one or more of the time-series variables under consideration may be non-stationary, and some of the variables may be cointegrated. In such cases we have to be especially careful when we apply tests for Granger causality. The reasons for this, and for adopting a modified testing procedure, such as that proposed by Toda and Yamamoto (1995), or that of Dolado and Lütkepohl  (1996) and Saikkonen and Lütkepohl (1996), are laid out in this earlier post, and I won't repeat them here. I'll make the bold assumption that you've done your homework.

Unit Root Testing: Sample Size vs. Sample Span

The more the merrier when it comes to the number of observations we have for our economic time-series data - right? Well, not necessarily. 

There are several reasons to be cautious, not the least of which include the possibility of structural breaks or regime-switching in the data-generating process. However, these are topics for future posts. Here, I want to discuss a different issue - namely, the impact of data frequency on the properties of tests for the stationarity of economic time-series.

To be specific, let's consider the following question: "Which is better when I'm applying the (augmented) Dickey-Fuller test - 20 annual observations for the series, or 80 quarterly observations?"

A. L. Nagar

Earlier this year I had a post in memory of the eminent statistician and econometrician, Anirudh Nagar. His passing was a great loss to our profession. 

Today, I was pleased to learn about this site that honours A. L. Nagar's life and contributions. 

The obituary by my friend, Aman Ullah, is especially noteworthy.

© 2014, David E. Giles

Correlation - NOT Causation

"Correlation is NOT the same as causation".

I don't know how many times I've said it (haven't we all?) in class, to the T.V. announcer, ...........

Tyler Vigen is a grad. student at Harvard Law School. He has a fun site called Spurious Correlations. Here are a couple of examples:

As of today, there are more than 23,000 spurious correlation charts on Tyler's site. You can even sign up to get an RSS feed of a new spurious correlation every day, if you're so inclined (or even if you're standing up)!

  

(r = 0.9357)

(r = 0.9805)

Yes, the sample sizes are small; and yes, I'd like to see more economic examples. However, I can still see myself using this material in class!

© 2014, David E. Giles

Free Eprints of Our Paper

If you're interested in downloading a copy of my recent paper with Xiao Ling, "Bias Reduction for the Maximum Likelihood Estimator of the Parameters of the Generalized Rayleigh Family of Distributions", but don't have a subscription to Communications in Statistics, fear not!

The publisher (Taylor and Francis) will provide free downloads for up to 50 people from here.

Knock yourselves out!

© 2014, David E. Giles

More on the Properties of the "Adjusted" Coefficient of Determination

A while back I wrote about the fact that R² (the coefficient of determination for a linear regression model) is a sample statistic, and as such it has a sampling distribution. In that post and in follow-up posts here and here, I discussed some of the properties of that sampling distribution, and about the mean and variance of R² in certain circumstances.

Let's take that discussion a step further by comparing the MSE's of R² and its "adjusted" counterpart.

Interpreting Confidence Intervals

I enjoyed William M. Briggs' ("Statistician to the Stars") post today: "Frequentists are Closet Bayesians: Confidence Interval Edition". Getting your head around the (correct) interpretation of a confidence interval can be difficult for students. Try teaching it - and keeping a straight face! It's a challenge, to be sure. On such days, my Bayesian inclinations percolate to the top.

That being said, I thought it would be worth pointing newcomers to this blog to a post of mine from 2011 that relates closely to what Matt Briggs has to say. The post tells a tale from the early years at the USDA Graduate School, where Jerzy Neyman presented some guest lectures/seminars in the 1930's. You'll find it all here.

Students may take some comfort from the interchange that is reported in the latter part of the post.

Update: In the post I refer to a paper by my colleague, Malcolm Rutherford: "The USDA Graduate School: Government Training in Statistics and Economics, 1921-1945". It's now published in the December 2011 issue of the Journal of the History of Economic Thought (vol. 33, no. 4, pp. 419-447). If you don't have online access to this journal, the Working Paper version of the paper is available here.

© 2014, David E. Giles

Publications of George Box

I can't imagine that there are any econometricians who have not heard of George Box - if only in the context of Box-Jenkins time-series analysis, or the Box-Cox transformation. 

To honour his memory and his many contributions to statistics, the publishers Taylor and Francis have made available a free electronic resource that gathers together much of his work, edited by David M. Steinberg.

Here's how the publishers introduce the material: