This class has three goals. You are going to study and learn some fundamental techniques in econometrics and statistics so that you can use them in your future research. You are also going to learn some of the basic theoretical concepts in econometrics so that you can understand new techniques when you encounter them in future classes and later in your career. Finally, you’re going to learn how to use a computer to do statistical and econometric analysis.
If you have questions about the course material, the best times to address them are in the scheduled lectures or during office hours. We can probably resolve questions or concerns about the course administration over email, but if you have urgent questions please call me or stop by my office.
The class will meet twice a week for almost two hours. The next table lists the most important times and dates. If you have any conflicts please let me know as soon as possible.
Your final grade will be based on homework and a take-home final exam; each counts for 50% of your grade.
You are allowed to work on the homework assignments in groups, but you must write up your own version of the answers. Specifically, you should not turn in an answer that is word-for-word identical to a classmate’s. Even if the argument is essentially the same, you should write your own explanation of the answer. This is true for computer code as well.
You are not allowed to discuss the final exam with other students at all; it must be entirely your own work.
The weekly review session will be used primarily for discussion of the homework and practice questions, but will also be used to present new material that supplements the regular lecture.
You need to learn how to program a computer to do statistical and econometric analysis. We’re going to use the programming language R in this class—it is a specialized programming language that is designed for sophisticated data analysis. It has three advantages over other statistical packages: it is very extensible, so designing and using new estimators is easy; the graphics it produces are excellent; and it is free (other packages have their own advantages as well, obviously). Also, I use R in my own research so my advice on programming is more likely to be useful than if we were to use another language. You can download the latest version of R from the website http://www.r-project.org. There are free manuals at the same website.
The required textbooks are Gal97, CB02, Gre12, and KZ08. Gal97 is an excellent overview of probability and asymptotic theory. CB02 gives a good explanation of basic statistics. Gre12 will be a useful reference later in your career and covers many econometric estimators. KZ08 is relatively cheap and is also available online through the library (we have an institutional subscription to SpringerLink, which is the publisher’s website for e-books). You may want to save pdf versions of its chapters to your computer instead of purchasing the book from the bookstore. You should also download and install the R package that accompanies this book, called the AER package.
George Casella and Roger L. Berger. Statistical Inference. Duxbury, 2nd edition, 2002.
Ronald Gallant. An Introduction to Econometric Theory. Princeton University Press, 1997.
William H. Greene. Econometric Analysis. Prentice Hall, 7th edition, 2012.
Christian Kleiber and Achim Zeileis. Applied Econometrics with R. Springer, 2008.
Freedman (2009) covers the intuition behind regression, is less technically demanding than the textbooks, and is exceptionally well written and insightful. Miller (2005) explains how to write up and present empirical research. Miller has written another book (2004, The Chicago Guide to Writing about Numbers) but all of its material is contained in Miller (2005). Tufte (1990, 2001) discusses effective statistical graphics and maps for displaying data. Polya (1945) discusses mathematical problem solving.
David A. Freedman. Statistical Models: Theory and practice. Cambridge University Press, revised edition, 2009.
Jane E. Miller. The Chicago Guide to Writing about Multivariate Analysis. University of Chicago Press, 2005.
George Polya. How to Solve It. Princeton University Press, 1945.
Edward R. Tufte. Envisioning Information. Graphics Press, 1990.
Edward R. Tufte. The Visual Display of Quantitative Information. Graphics Press, 2nd edition, 2001.
You’ll be required to buy Hayashi (2000) next semester, so you may want to buy it now as another source of material (it uses Generalized Method of Moments as an organizing principle for this material). Bruce Hansen has a free Econometrics textbook on his website. I’m also in the process of converting my lecture notes into a textbook, but it is quite disorganized now and is unlikely to be much help; source code and pdfs are available on GitHub (also see http://www.econometricslibrary.org).
EFLP. Econometrics Core. Econometrics Free Library Project, http://www.econometricslibrary.org, in progress.
Fumio Hayashi. Econometrics. Princeton University Press, 2000.
Bruce Hansen. Econometrics. Available at http://www.ssc.wisc.edu/~bhansen/econometrics/, in progress.
I am going to assume that you’ve taken undergraduate Econometrics when I teach the class. If you haven’t, or if it’s rusty, you may want to consult an undergraduate textbook for a less technical introduction to some of the material. Wooldridge (2012) is a good option. If you have not taken even an introductory statistics class (covering basic probability, sample averages, t-tests, etc.) you should buy and read Freedman, Pisani, and Purves (2007) ASAP (you can read it this weekend). You may want to purchase older editions of these books since they are somewhat expensive.
David A. Freedman, Robert Pisani, and Roger Purves. Statistics. W.W. Norton & Company, fourth edition, 2007.
Jeffrey M. Wooldridge. Introductory Econometrics: A modern approach. Thomson Southwestern, fifth edition, 2012.
This would also be a good time for you to begin reading the Journal of Economic Literature and paying attention to working papers in your field announced at http://nep.repec.org.
You should try to understand many different approaches to data analysis and modeling. These books span several different philosophies and make excellent vacation reading (many of these authors have other books that I’d also recommend, but it’s a long list as is). In alphabetical order:
Joshua D. Angrist and Jörn-Steffen Pischke. Mostly harmless Econometrics: An empiricist’s companion. Princeton University Press, 2009. (Also see the symposium in the Spring, 2010 issue of the Journal of Economic Perspectives and the articles in the June, 2010 issue of the Journal of Economic Literature.)
William S. Cleveland. Visualizing Data. Hobart Press, 1993.
Andrew Gelman and Jennifer Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2006.
John Geweke. Complete and Incomplete Econometric Models. Princeton University Press, 2010.
Frank E. Harrell. Regression Modeling Strategies. Springer, 2001.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. Springer, 2nd Edition, 2009. Also available online (with supplemental material) at http://www-stat.stanford.edu/~tibs/ElemStatLearn/
Edwin T. Jaynes. Probability Theory: the Logic of Science. Cambridge University Press, 2003.
Charles F. Manski. Identification for Prediction and Decision. Harvard University Press, 2007.
Paul R. Rosenbaum. Design of Observational Studies. Springer, 2010.
I’ll assign specific readings before each lecture during the semester. This is a tentative and optimistic plan:
Read Fis26, Fre91, and Ros05 for general background (over Thanksgiving break, if not earlier) and Gre12 chapters 1–10 (some chapters are tied to specific lectures, some are not) as well as the following papers.
CK94: David E. Card and Alan B. Krueger. Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. American Economic Review, 84:772–793, 1994. http://ideas.repec.org/a/aea/aecrev/v84y1994i4p772-93.html
Fis26: Ronald A. Fisher. The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33:503–513, 1926. http://digital.library.adelaide.edu.au/dspace/handle/2440/15191
Fre91: David A. Freedman. Statistical models and shoe leather. Sociological Methodology, 21:291–313, 1991. http://www.jstor.org/stable/270939
Hor01: Joel L. Horowitz. The bootstrap. Handbook of econometrics, 5:3159–3228, 2001. http://ideas.repec.org/h/eee/ecochp/5-52.html
ImW07: Guido W. Imbens and Jeffrey M. Wooldridge. Lecture notes for “What’s new in Econometrics?” Available online, http://www.nber.org/WNE/WNEnotes.pdf 2007.
LP05: Hannes Leeb and Benedikt M. Pötscher. Model selection and inference: Facts and fiction. Econometric Theory 21(1):21–59, 2005. http://ideas.repec.org/a/cup/etheor/v21y2005i01p21-59_05.html
Pol98: Dimitris N. Politis. Computer-intensive methods in statistical analysis. Signal Processing Magazine, IEEE, 15(1):39–55, 1998. http://math.ucsd.edu/~politis/SPprimer-289.pdf
RSW08: Joseph P. Romano, Azeem M. Shaikh, and Michael Wolf. Formalized data snooping based on generalized error rates. Econometric Theory, 24(2):404–447, 2008. http://ideas.repec.org/a/cup/etheor/v24y2008i02p404-447_08.html
Ros05: Paul R. Rosenbaum. Observational study. Encyclopedia of Statistics in Behavioral Science, 3:1451–1462, 2005. http://www-stat.wharton.upenn.edu/~rosenbap/BehStatObs.pdf
RS09: Paul R. Rosenbaum and Jeffrey H. Silber. Sensitivity analysis for equivalence and difference in an observational study of Neonatal Intensive Care Units. Journal of the American Statistical Association, 104, 501–511. http://www-stat.wharton.upenn.edu/~rosenbap/senequivdif.pdf
Rub08: Donald B. Rubin. For objective causal inference, design trumps analysis. Annals of Applied Statistics, 2(3):808–840, 2008. http://arxiv.org/abs/0811.1640
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The class will follow Iowa State University’s policy on academic dishonesty. Anyone suspected of academic dishonesty will be reported to the Dean of Students Office, http://www.dso.iastate.edu/ja/academic/misconduct.html.
This material can be provided to you in alternative format. Anyone who anticipates difficulties with the content or format of the course due to a physical or learning disability should see me immediately in order to work out a plan. You may also want to contact the Disability Resources (DR) office, located on the main floor of the Student Services Building, Room 1076 or call them at 515-294-7220.
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