Thursday, December 12, 2013

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: