Investing in Unix human capital will increase your productivity immensely over your career. I use A practical guide to Linux commands, editors, and shell programming, 2nd Edition by Mark G. Sobell in my class at the University of Chicago. It is available for purchase at Amazon.
Please read a little about Berry, Levinsohn, and Pakes’s (1995) model (aka BLP) because several lectures use it to illustrate proper numerical techniques. If you have a passing familiarity with BLP, you will enjoy the lectures more. The easiest place to start is Train’s Chapter 13 from the second edition of his discrete choice book (freely downloadable from the web). I have included this chapter in the list of papers.
Benjamin Skrainka’s Reading List
- Nevo, Aviv. (2000). “A Practitioner’s Guide to Estimation of Random-Coefficients Logit Models of Demand,”
Journal of Economics & Management Strategy, 9(4): 513-548. The classic introduction to BLP. Ignore the numerical details.
- Train, Kenneth E. (2009). “Endogeneity,” in Discrete Choice Methods with Simulation, Kenneth Train, Cambrige, UK: Cambridge University Press. A more recent and shorter introduction to BLP
- Dube, Jean-Pierre H., Fox, Jeremy T. and Su, Che-Lin. (2011). “Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation,” Chicago Booth School of Business Research Paper No. 11-41. Available at SSRN: http://ssrn.com/abstract=1338152 or http://dx.doi.org/10.2139/ssrn.1338152. Key paper on using MPEC to replace nested fixed point algorithms to provide more robust and credible computational results.
- Su, Che-Lin and Judd, Kenneth L. (2011). “Constrained Optimization Approaches to Estimation of Structural Models,” Unpublished manuscript, University of Chicago Booth School of Business. Using MPEC for constrainted optimization instead of nested fixed point
- Maliar, Serguei, Maliar, Lilia, and Judd, Kenneth. (2011). “Solving the multi-country real business cycle model using ergodic set methods,” Journal of Economic Dynamics and Control, 35(2): 207-228. GSSA algorithm for dynamic programming with large state space
- Goldberg, David. (1991). “What every computer scientist should know about floating-point arithmetic,” ACM Computing Surveys, 23(1): 5-48. What you really need to know about floating point.
- McCullough, B. D. and Vinod, H. D. (1999). “The Numerical Reliability of Econometric Software,” Journal of Economic Literature, 37(2): 633-665. An example of the dangers lurking out there when actually trying to estimate a model on a computer