Time for Some More Reading!

With the weekend upon us once again, it's time to settle down with the papers - the econometrics research papers, that is. Here are my latest picks:

• Cook, S., D. Watson, and L. Parker, 2014. New evidence on the importance of gender and asymmetry in the crime-unemployment relationship. Applied Economics, 46, 119-126.
• Fan, J., F. Han, and H. Liu, 2013. Challenges of big data analysis. Mimeo.
• Hashmi, A. R., 2014. Competition and innovation: The inverted-U relationship revisited. Review of Economics and Statistics, in press.
• Juselius, K., N. F. Moller, and F. Tarp, 2104. The long-run impact of foreign aid in 36 African countries: Insights from multivariate time series analysis. Oxford Bulletin of Economics and Statistics, in press.
• Li, R., D. K. J. Lin, and B. Li, 2013. Statistical inference in massive data sets. Applied Stochastic Models in Business and Industry, 29, 399-409.
• Sanderson, E. and F. Windmeijer, 2013. A weak instrument F-test in linear IV models with multiple endogenous variables. CEMMAP Working Paper CWP58/13, The Institute for Fiscal Studies.

© 2013, David E. Giles

Data Do Not Imply Science

As a follow-up to my recent post on Big Data, I recommend today's post by Jeff Leek on the Simply Statistics blog. It's titled. 'The key word in "Data Science is not Data, it is Science'.

Jeff says:

"Most people hyping data  science have focused on the first word: data. They care about volume and velocity and whatever other buzzwords describe data that is too big for you to analyze in Excel. .........

But the key word in data science is not "data"; it is "science". Data science is only useful when the data are used to answer a question. That is the science part of the equation. The problem with this view of data science is that it is much harder than the view that focuses on data size or tools. It is much, much easier to calculate the size of a data set and say "My data are bigger than yours"......"

Right on, Jeff!

© 2013, David E. Giles

When Everything Old is New Again

We see it with clothing styles. Not just hemline lengths, but also the widths of jacket lapels and guy's ties. How wide should the trouser legs be? Cuffs or no cuffs? Leave your clothes in the closet long enough, and there's a good chance they'll be back in style some day!

And so it is with econometrics. Here are just a few examples:

Random Variable?

A big HT to Ryan MacDonald for drawing this quote to my attention:

"While writing my book (Stochastic Processes, 1953) I had an argument with Feller. He asserted that everyone said "random variable" and I asserted that everyone said "chance variable." We obviously had to use the same name in our books, so we decided the issue by a stochastic procedure. That is, we tossed for it and he won."

Joe Doob, in Statistical Science 12 (1997), No. 4, page 307.

Added - thanks to Arthur Charpentier for this link..

© 2013, David E. Giles

The Washing Machine Repairman

Here's a fun quote.

"As I remember, Bill X fixed my washing machine. My husband, Harry X, brought him home to talk economics after a Cambridge dinner in hall and they walked in on my frustration with the washer. I met a slight-statured, quiet man who modestly asked if he could help. He tried something with a screw-driver which may have worked - or perhaps it didn't work - and went back to talking economics'"

Who were  "Harry" and Bill?

© 2013, David E. Giles

Econometrics and "Big Data"

In this age of "big data" there's a whole new language that econometricians need to learn. Its origins are somewhat diverse - the fields of statistics, data-mining, machine learning, and that nebulous area called "data science".

What do you know about such things as:

• Decision trees 
• Support vector machines
• Neural nets 
• Deep learning
• Classification and regression trees
• Random forests
• Penalized regression (e.g., the lasso, lars, and elastic nets)
• Boosting
• Bagging
• Spike and slab regression?

Probably not enough!

If you want some motivation to rectify things, a recent paper by Hal Varian will do the trick. It's titled, "Big Data: New Tricks for Econometrics", and you can download it from here. Hal provides an extremely readable introduction to several of these topics.

He also offers a valuable piece of advice:

"I believe that these methods have a lot to offer and should be more widely known and used by economists. In fact, my standard advice to graduate students these days is 'go to the computer science department and take a class in machine learning'."

Interestingly, my son (a computer science grad.) "audited" my classes on Bayesian econometrics when he was taking machine learning courses. He assured me that this was worthwhile - and I think he meant it! Apparently there's the potential for synergies in both directions.

© 2013, David E. Giles

The International Association for Applied Econometrics

Here's an organisation that deserves promoting - The International Association for Applied Econometrics. What more can I say?

Well, I had better add something!

First:

"The aim of the Association is to advance the education of the public in the subject of econometrics and its applications to a variety of fields in economics, in particular, but not exclusively, by advancing and supporting research in that field, and disseminating the results of such useful research to the public."

Second:

There next Annual Conference will be held in London, U.K., in June 2014, and the line-up of keynote speakers is impressive. Submissions of papers are due by 1 February 2014, and there is a nice prize for the best paper presented by a graduate student.

© 2013, David E. Giles

Do You Have a Tattoo?

Significance Magazine  is a joint publication of the Royal Statistical Society and the American Statistical Association. The "News" section of the latest issue (which can be read by subscribers) contains an item titled, "More Than Skin Deep". It's about a mathematics teacher who has an interesting tattoo:

In case you need an interpretation, it reads:  

                                                          

The item concludes:

"It attracts attention. Often on a beach someone will say something like 'You're either a math teacher or in a really, really odd motorcycle gang.'

Why should statisticians lag behind? A bottle of champagne to the first reader who can show a permanent tattoo of Bayes' theorem - preferably on a part of the anatomy that we can decently reproduce."

Needless to say, this got me thinking! Are there any Econometrics tattoos out there that I should be aware of?

© 2013, David E. Giles

Lawrence R. Klein Memorial Prize

Nobel Laureate Lawrence R. Klein passed away in October of this year in Philadelphia. (See here.) 

Empirical Economics has established a prize in his honour given for the best empirical paper published in the last two years in the journal. An upcoming issue of Empirical Economics will include an obituary written by Badi Baltagi.

© 2013, David E. Giles

A Bayesian View of P-Values

"I have always considered the arguments for the use of P (p-value) absurd. They amount to saying that a hypothesis that may or may not be true is rejected because a greater departure from the trial was improbable: that is, that it has not rejected something that has not happened'"

H. Jeffreys, 1980. Some general points in probability theory. In A Zellner (ed.), Bayesian Analysis in Probability and Statistics. North-Holland, Amsterdam, p. 453.

© 2013, David E. Giles