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Econ 239D / Fall 2011 / Duke University / Derek S. Brown

ECON 239D.001 Introduction to Econometrics (Masters level) Duke University Fall 2011

Lecture: Mondays 6:15-8:45pm, Social Sciences 136 Instructor: Derek S. Brown Email: [email protected] or [email protected] Phone: 919-316-3514 Office hours: Before/after lecture + Fridays 3:30-5:00pm in EcoTeach (SocSci 138 Conf. Room) (Note: location for 10/28 and 11/4 is TBD and will only meet on request) TA/Recitation leader: Wenjing Wang

[email protected] office hours Wednesday, 12:00-1:00pm, Econ Grad Lounge (SocSci)

Required text: Greene, W.H. Econometric Analysis. 7th ed., Prentice Hall/Pearson (2011/2012). Older editions are OK, but you may need to cross-reference concepts with a copy of the latest edition. An electronic text ($80) is at:

http://www.coursesmart.com/IR/3523947/9780131395398 Optional text: Kennedy, P. A Guide to Econometrics. 6th ed., Wiley/Blackwell (2008). There is no

difference between 5th and 6th editions. Any edition is extremely helpful, filling in the gaps around concepts and providing tips in a very readable format.

Website: courses.duke.edu (Blackboard) Problem sets, notes, and other materials will be posted here regularly. Overview: This course is designed to be either a 2nd course in econometrics for M.A. students who have had one or more undergraduate econometrics courses, or an advanced introduction for students with no prior econometrics but a strong mathematics and statistics foundation. Students will be introduced to the major quantitative techniques that economists use to test models, study economic behavior, evaluate policies, and relationships between variables. Our goal is to learn enough methods and theory to be able to:

Understand and differentiate between major econometric techniques. In particular, which are appropriate or inappropriate in different circumstances

Conduct basic econometric analysis of cross-sectional and time-series data using a standard software package (Stata)

Be able to read and understand the main concepts of a quantitative economics research paper As appropriate, continue to

o Econ 360 (Vocational Skills for Empiricists), as appropriate o Econ 341 (Ph.D. econometrics) and beyond

Because students enter with a variety of backgrounds, and will also continue in several different paths, we will cover a lot of materialand also move quickly. Prerequisites: Calculus and basic probability & statistics. As Econ 239 is being redesigned to emphasize M.A.-level rigor, experience with linear algebra is assumed. Please talk with me if you are unsure. Recitation: A weekly session with the TA will be held to review concepts from the lectures, work through problems, and answer your questions. Three sections are listed, but they will be collapsed into a single session after drop/add ends (Sept. 9): Wednesdays 4:25-5:40pm in SocSci 229.

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Grading: 25% Midterm 50% Final exam 25% Paper Exams: The midterm exam cannot be rescheduled; if missed, the final exam will be weighted for both exams. The final will only be adjusted in extraordinary circumstances and with University permission. Problem Sets: When combined with lecture and the text, the best way to learn econometrics is by doing theoretical and empirical exercises. Greene is filled with questions at the end of most chapters. A day or two after most lectures, I will distribute a list of exercises. We will not collect or grade the problem sets, but these exercises are very appropriate for discussion and review in your recitation section or office hours. Some questions may reappear on exams. Working in groups is encouraged. Computing and Software: You may use any software package that you wish to complete problems and your class paper. Learning Stata, MATLAB, and SAS is extremely helpful regardless of your long-term educational and career path. MA students should be able access all of these from Dept. of Economics computing resources (http://dialog.econ.duke.edu/help/). Stata is also available in select OIT computer labs such as Old Chem Basement (Room 01) and Soc-Psych 133 (https://oit.duke.edu/comp-print/labs/locations/). Duke has site licenses for several of these if you wish to purchase a personal copy at reduced rates, but that is not necessary. Other options include GAUSS, R, JMP, Excel, SPSS, and LIMDEP, to name a few. If you can fit it into your schedule, I recommend learning more about computing and empirical research by taking Econ 360 (Vocational Skills for Empiricists).

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Paper: Working through problems and the text will help you to acquire a lot of tools, but there is no substitute for putting what youve learned into practice with real data. This is especially true for M.A. students who may not continue into Ph.D. coursework. To help you learn more about econometrics, and how it is actually applied, you will develop and write a short (6-12 pages) empirical paper on a topic of your choice. For full credit, your paper must:

State a clear and simple research question (e.g., What is the effect of obesity on wages?) Describe a basic econometric model (or two) that you will run, written in both plain language,

and appropriate econometric formulas from class and/or the text Identify a data source and how you prepared it for analysis Include a short table of summary statistics (means or distribution of the dependent &

independent variables) Describe your econometric results Identify and describe appropriate specification/assumption tests (e.g., heteroskedasticity,

endogeneity, autocorrelation) Draw a short conclusion Include a brief reference list: a few papers on a related topic, relevant econometric theory, and

a link to the data source Include an appendix with your programming code and econometric output

The paper will be challenging. You may have to learn new software and data manipulation skills. Your TA and I cannot show you everything that you will need. Further, some of the content and tools that you wish to use do not come until the 2nd half of the course. Notes: A replication (simplified) of a published or working paper is fine, as long as your content is new, you

acknowledge the source, and you do not contact the author for the data. Your paper should be original to this course. Do not reuse something that you may have completed

at another institution. You may not use example datasets (Stata, Greene, UCLA, or other textbooks) for the same

application or estimator that these authors used them for. You may apply them in a different context. (If reusing one of these, let me know in advance about the data source is, how it was used where you found it, and what different model or application you propose.)

You will not be evaluated on grammar and style, but please try to write your best. To ensure adequate progress, the following milestones should be met by class time (6:15pm) on these Mondays: 10/3 Data source & general topic identified (1 page) 10/31 Table of summary statistics of your data (1 page) 11/21 Econometric model/estimator selected (1 page) 11/28 Draft paper review opportunity Ill review and return comments to you by the next class. If you miss these dates, you wont be penalized on the final paper, but I will not be able to provide you with comments. Friday 12/9, 5pm Papers due by email. Late papers will be deducted 10% for each 24-hour period.

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Schedule (subject to change) PROBABILITY & STATISTICS & LINEAR ALGEBRA REVIEW/FOUNDATION Date Class Content Greene 8/29 1 Introduction, linear/matrix algebra 1, Appendix A 9/5 2 Probability & statistics Appendix B 9/12 3 Probability & statistics, sampling, linear/matrix algebra Appendix B,C,A LINEAR REGRESSION (OLS) 9/19 4 Linear regression 2, 3 9/26 5 Least squares models 3, 4 10/3 6 Hypothesis tests

Paper: data/topic 4, 5

10/10 NO CLASS FALL BREAK. Recitation and office hours will be held.

10/17 7 MIDTERM EXAM Possible short lecture other models

7?

BEYOND OLS 10/24 8 Endogeneity, instrumental variables (IV), heteroskedasticity

Office hours moved/TBD this week. 8, 9

10/31 9 Heteroskedasticity, panel data Paper: summary statistics Office hours moved/TBD this week.

9, 11

11/7 10 Panel data, generalized method of moments (GMM), maximum likelihood estimation (MLE)

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11/14 11 Limited dependent variables 17-18, 19 11/21 12 Time series analysis

Paper: model 20-21

11/28 13 Time series analysis Paper: draft review

20-21

12/5 NO CLASS open review session 12/9 Friday

PAPERS DUE email by 5:00pm

12/18 Sunday

FINAL EXAM: 7-10pm

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OTHER RESOURCESNOT REQUIRED, BUT I MAY DRAW FROM THESE OCCASIONALLY Other good undergraduate econometrics texts:

Wooldridge, J. Introductory Econometrics: A Modern Approach. Stock, J., Watson, M. Introduction to Econometrics. Goldberger, A.S. Introductory Econometrics

Advanced econometrics texts:

Wooldridge, J. Econometric Analysis of Cross Section and Panel Data. Hayashi, F. Econometrics. Hamilton, J. Time Series Analysis. Gallant, A.R. An Introduction to Econometric Theory.

Mathematics texts:

Strang, G. Linear Algebra and Its Applications. Casella, G., Berger, R. Statistical Inference.

Stata

Stata reference manuals Stata website: www.stata.com (See Support/Statalist) UCLA Academic Technology Services: http://www.ats.ucla.edu/stat/

o Many other software programs are also presented at this site (e.g., SAS, R) o Several examples from Greene, Wooldridge, and other statistical texts are shown in

Stata at this website: http://www.ats.ucla.edu/stat/Stata/examples/ A SAS Users Guide to Stata:

http://www.cpc.unc.edu/services/computer/presentations/sas_to_stata MATLAB

MATLAB reference manuals http://www.mathworks.com/ Duke Econ computing site provides many helpful links:

https://help.econ.duke.edu/wiki/help:linuxmatlab and http://dialog.econ.duke.edu/help/

econ239 f11 1.original

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