Data, Data, Everywhere.....

Data - the life-blood of econometrics - we can't live without them.!

So, thank goodness for the recent re-vamp of the the FED's FRED data site. And also the recent additions to the Quandl site.

© 2013, David E. Giles

How Science (Econometrics?) is Really Done

If you tweet, you may be familiar with #OverlyHonestMethods. If not, this link to Popular Science will set you on the right track. As it says: "In 140 characters or less, the info that didn't get through peer review."

Here are some beauties that may strike an accord with certain applied econometricians:

• "Our results were non-significant at p > 0.05, but they're humdingers at p > 0.1"
• "Experiment was repeated until we had three statistically significant similar results and could discard the outliers"
• "We decided to use Technique Y because it's new and sexy, plus hot and cool. And because we could."
• "I can't send you the original data because I don't remember what my excel file names mean anymore."
• "Non-linear regression analysis was performed in Graph Pad Prism because SPSS is a nightmare."
• "We made a thorough comparison of all post-hoc tests while our statistician wasn't looking."
• "Our paper lacks post-2010 references as it's taken the co-authors that long to agree on where to submit the final draft."
• "If you pay close attention to our degrees-of-freedom you will realize we have no idea what test we actually ran."
• "Additional variables were not considered because everyone involved is tired of working on this paper."
• "We used jargon instead of plain English to prove that a decade of grad school and postdoc made us smart."

Oh yes!!!!

© 2013, David E. Giles

A Talk With Lars Peter Hansen

Now that some of the commotion and excitement over this year's Economics Nobel Prize has died down a little, an informal chat with co-winner Lars Peter Hansen is definitely in order.

So, a hat-tip to Mark Thoma for alerting me to this interview of Hansen by Jeff Sommer in the New York Times, today. Jeff manages to get a comment about efficient markets from his interviewee.

And here's a comment that all students of econometrics should take to heart:

"The thing to remember about models is they’re always approximations and they will always turn out to be wrong at some point. When someone says all the models that economists use are wrong, well, in a sense that’s true. But you need to ask, are the models wrong in ways that are central to the questions, or are they wrong in ways that aren’t so central?

And so part of the task of statistical analysis is to look at models and try to figure out what the gaps are so that people will build better models in the future."

© 2013, David E. Giles

Calling All Forecasters!

The 34th International Symposium on Forecasting will be held in Rotterdam between 29 June and 2 July, 2014. Full details can be found here. 

I participated in the 2010 Symposium, in San Diego. It was a great meeting, very well run, and with a fabulous program. Definitely recommended!

© 2013, David E. Giles

Free EViews Tutorials

Free Eviews tutorials - but not from me, I'm afraid!

"Free" is good, and you should keep in mind that free introductory tutorials are indeed available from the distributors of the EViews package. You can find the details here.

You can even download all of the associated Powerpoint presentations, and data files, in a zip file. Nice!

© 2013, David E. Giles

The Econometric Game, 2014

Doesn't time fly. We must be having fun! It's time to start thinking about The Econometric Game once again - this time, the 2014 edition.

April  15 to 17 2014 are the dates to keep in mind, and once again Amsterdam will be the place to be. Apparently entries are rolling in!

More on this in due course.

© 2013, David E. Giles

The Stock Market Crash - VECM's & Structural Breaks

A few weeks ago, Roger Farmer kindly drew my attention to a recent paper of his - "The Stock Market Crash Really Did Cause the Great Recession" (here). To whet your appetites, here's the abstract:

"This note shows that a big stock market crash, in the absence of central bank intervention, will be followed by a major recession one to four quarters later. I establish this fact by studying the forecasting ability of three models of the unemployment rate. I show that the connection between changes in the stock market and changes in the unemployment rate has remained structurally stable for seventy years. My findings demonstrate that the stock market contains significant information about future unemployment."

Specification Testing for Panel Data Models

Recently, I received a query from Rolf Lyneborg Lund who asked for references to material on specification testing in the context of panel data models. After I responded to Rolf. it occurred to me that these references might also be of interest to others.

Going beyond the standard Hausman test for random versus fixed effects, here are some general references that may be helpful:

• Baltagi, B. H., 1998. Panel data methods. In A. Ullah and D. E. A. Giles (eds.), Handbook of Applied Economic Statistics, Marcel Dekker, New York.
• Baltagi, B. H., 1999. Specification tests in panel data models using artificial regressions. Annales d'Économie et des Statistiques, 55-56, 277-298.
• Lee, Y-J., 2005. Specification testing for functional forms in dynamic panel data models.
• Metcalf, G. E., 1996. Specification testing in panel data with instrumental variables. Journal of Econometrics, 71, 291-307.
• Park, H. M., 2011. Practical guides to panel data modeling: A step by step analysis using stata. Public Management and Policy Analysis Program, Graduate School of International Relations, International University of Japan.

In addition, there's quite an extensive literature on testing for unit roots and cointegration in panel data models. I won't attempt to summarize this literature here, but a useful, recent, summary is provided by:

• Chen, M-Y., 2013. Panel unit root and cointegration tests. Mimeo., Department of Finance, National Chung Hsing University.

© 2013, David E. Giles

Some Weekend Reading

Just what you need - some more interesting reading!

• Al-Sadoon, M. M., 2013. Geometric and long run aspects of Granger causality. Mimeo., Universitat Pompeu Fabra. (Forthcoming in Journal of Econometrics.)
• Barnett, W. A. and I. Kalondo-Kanyama, 2013. Time-varying parameter in the almost ideal demand system and the Rotterdam model: Will the best specification please stand up? Working Paper 335, Econometric Research Southern Africa.
• Delgado, M. S. and C. F. Parmenter, 2013, Embarrassingly easy embarrassingly parallel processing in R. Journal of Applied Econometrics, early view, DOI: 10.1002/jae.2362 .
• Doko Tchatoka, H., 2013. On bootstrap validity for specification tests with weak instruments. Discussion Paper 2013-05, School of Economics and Finance, University of Tasmania.
• Fisher, L. A., H-S. Huh, and A. R. Pagan , 2013, Econometric issues when modelling with a mixture of I(1) and I(0) variables. NCER Working Paper Series, Working Paper #97.
• Pesaran, H. H. and Y. Shin, 1998. Generalized impulse response analysis in linear multivariate models. Economics Letters, 58, 17-29.
• Warr, R. L. and R. A. Erich, 2013. Should the interquartile range divided by the standard deviation be used to assess normality? American Statistician, online, 
  DOI:
  10.1080/00031305.2013.847385 .
• Zhang, X. and X. Shao, 2013, On a general class of long run variance estimators. Economics Letters, 120, 437-441.

© 2013, David E. Giles

swirl: Learning Statistics & R

Most of us would acknowledge that getting up to speed with R involves a pretty steep learning curve - but it's worth every drop of sweat we shed in the process!

If you're learning basic statistics/econometrics, and learning R at the same time, then the challenge is two-fold. So, anything that will make this feasible (easy?) for students and instructors alike deserves to be taken very seriously.

Enter swirl - "statistics with interactive R learning" - developed at the Department of Biostatistics, Johns Hopkins University.  It's dead easy to download and install swirl - it just takes a few moments, and you're underway.

There are simple, interactive, lessons that introduce you to the essential concepts, and you have the option to watch related videos. If you need to take a break part way through a lesson then you can save what you've completed, and pick up from that point at a later time.

My guess is that students will find swirl appealing and very helpful.

© 2013, David E. Giles