December 2nd is World Computer Literacy Day. The aim for the computer literacy day at ESOP is to spend some time together thinking and talking about how to use computers efficiently for doing reproducible research.

When and where?

The ESOP-room (1047), Wednesday December 2, 2009.

• 0915-1000: First session
• 1015-1100: Second session
• 1115-1200: Third session
• 1200-1315: Lunch
• 1315-1400: Fourth session
• 1415-1500: Fifth session

The topics:

These are the topics that I plan to talk about, more or less one topic per session. Although the rough outline will be more or less like this, I plan on expanding some points and migth make some other changes as well. If there are things you feel should be on the program, drop me a line!

I've put in links to some readings, but I don't expect anyone to have read these before the workshop. 

Don't rely on topics 2-4 being covered sequentially.

1. What are the different ways of understanding computing? Computer Science, Software Engineering and social studies of computing. Where are the real bottlenecks when economists deal with computers? Also: What every economist should know about computer science. Representation of data, the difference between math and computing, imperative vs functional and declerative programming.
   • Justin McCrary (2002). Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime: Comment American Economic Review, 92(4), 1236-1243.
   • Greg Wilson (2006). Where's the Real Bottleneck in Scientific Computing? American Scientist, 94(1), 5-6.
   • Greg Wilson (2009). How Do Scientists Really Use Computers? American Scientist 97(5), 8-10.
2. Workflow for data analysis. How to keep track of everything from back-of-the-envelope calculations to large environments of data and computation? It is well documented that professional, conscientious programmers create buggy software. How can we attempt to keep the worst bugs out of our economic calculations?
   • William G. Dewald, Jerry G. Thursby and Richard G. Anderson (1986). Replication in Empirical Economics: The Journal of Money, Credit and Banking Project American Economic Review, 76(4), 587-603.
   • ICPSR (2009). ICPSR Guide to Social Science Data Preparation and Archiving: 4th Edition.
   • J. Scott Long (2009). The Workflow of Data Analysis Using Stata TX: College Station, Stata Press.
   • B. D. McCullough, Kerry Anne McGeary and Teresa D. Harrison (2006). Lessons from the JMCB Archive Journal of Money, Credit and Banking 38(4), 1093-1107.
3. Software for economists, and why the spreadsheet should be banished from all economic analyses.
   • Giovanni Baiocchi (2007). Reproducible research in computational economics: guidelines, integrated approaches, and open source software Computational Economics 30(1), 19-40.
   • Arne Henningsen (2007). Tools for Collaborative Writing of Scientific LaTeX Documents PracTeX Journal, no 3.
   • Roger Koenker and Achim Zeileis (2009). On reproducible econometric research Journal of Applied Econometrics 24(5), 833-847.
   • Raymond R. Panko (1998/2008). What We Know About Spreadsheet Errors Journal of End User Computing's, Special issue on Scaling Up End User Development 10(2), 15-21. Revised May 2008.
4. Efficient statistical computing with statistical software. My own examples will be using Stata, but similar comands exist in all available software out there.
• Alan C. Acock (2008). A Gentle Introduction to Stata.
• Christopher F. Baum (2009). An Introduction to Stata Programming.
5. What every economist should know about software engineering.
   • Ben Collins-Sussman, Brian W. Fitzpatrick, C. Michael Pilato (2008). Version Control with Subversion.
   • Greg Wilson (2006). Software Carpentry: Getting Scientists to Write Better Code by Making Them More Productive Computing in Science & Engineering, November/December.

I plan to do some demonstrations with my computer using the projector, but don't expect you to bring your own.