Cross Section Econometrics Syllabus [ECON 407/PUBP 615]

Fall 2018

Instructor: Rob Hicks
Office: Tyler Hall 252
Class Time: MW 3:30 - 4:50pm
Classroom: Tyler Hall 219

Course Summary

In this course we will explore econometric techniques for testing microeconomic theories at the individual or firm level. With the advent of computers and associated data on economic behavior, the past few decades has seen an explosion of applied economic research using a wide range of techniques for this type of data- termed cross section data and cross section econometrics. In this course, you will learn about these techniques, will learn to be an educated consumer of econometric research, and will apply these techniques to real data. We will also derive many of the properties of the statistical techniques used in this course, but primarily at the end of the course you will

  • Understand the strengths and weaknesses of cross section techniques
  • Know how to test the validity of modeling assumptions
  • Know the proper econometric technique for a wide variety of Cross-Section settings


  • Office Hours : T 4:00 - 6pm or by appointment
  • Asking Questions: Substantive questions about course material or coding in Stata (or R) must be submitted to the Cross Section issue tracker at Before using the issue tracker, you must create an account on gitlab (free). Use your William and Mary email address so that I can add you to the user list. Part of Problem Set 1 requires you to file an issue at this site, so create your account ASAP.
  • Email Policy : For other types of questions like setting up meetings, logistical issues, etc., I will respond to emails but only if they contain the tag `[ECON407]` or `[PUBP615]` in the subject line or you use the email exactly as specified above. If not, the google will likely delete your email. Substantive questions about course material/coding issues will be ignored.
  • Grades: Your grade will be based on five exercises (1 @ 5% and 4 @ 10% each), one mid-term (25%), a final exam (30%).
    • The problem sets will consist of a mix of theoretical and practical econometrics. In each (after the first one), you will be given a dataset and will need to conduct an econometric analysis thinking critically about which technique to employ as well as key tests that should be run. Your document should include clear interpretations of your results, tables with clear variable names, and be well-formatted with code, tables, and writeup combined in a convenient (for me) way. You will be responsible for posting to the blackboard assignment a markstat (for stata) or Rmarkdown (for R) file that completely generates your analysis: a flowing narrative containing code, writeup, and results. For any part of the problem sets requiring hand-written math excercises, you can turn those in on paper. Early on in class, I will demo how this works for stata. The handout for reproducible research can be found here and is produced by this stata source code in stata using markstat.
    • The mid-term is scheduled for Oct 17 just after fall break. Unfortunately, I can't reschedule either the mid-term or the final the exam, so if this time doesn't work for you please drop the course.

Important Dates

Date Item
Aug 29 First day of class
Oct 13 - 16 Fall Break
Oct 17 Mid-Term
Nov 21 - 25 Thanksgiving
Dec 7 Last day of this class
Dec 11 T (9am - 12pm) Final Exam

Policy on Late Assignments

  • Final Exam: University policy will not allow me to reschedule the final exam (see the Dean of Students for exceptions).
  • Course assignments must be turned in on time. Late work will be accepted for up to two additional days (with Saturday and Sunday counting as 1 day in total) with a letter grade deduction for each late day. After two days, late assignments will not be accepted. See below for some examples:
    Due Date Turned in Your Grade Your Grade after Penalty
    Tuesday Thursday A C
    Thursday Saturday or Sunday A C
    Tuesday Friday A F (not accepted)
    Thursday Monday A F (not accepted)

Hardcopy Policy

For all problem sets, in addition to the file you post to blackboard, you will be responsible for turning in a hardcopy version of your work at the end of class. You may give it to me in person, put it in my box in Tyler Hall, or slide it under my door in Tyler Hall 252. Should you not give it to me in person and the work goes missing, you remain responsible for getting me your work on time to avoid late assignment penalties.

Grade Discrepancies and Grade Questions

I am happy to discuss questions you have about your grade on class assignments. Any questions you have regarding a potential grade change on an assignment must be cleared up within 1 week of receiving your work back from me. The only exception to this policy is if I made an arithmetic or data entry error in adding your score up and entering it into blackboard. I will not entertain grade questions at the beginning of or following a class. These need to be handled in my office.

Course Materials

All course materials are available on my website for this course at the links listed below. I will only be using blackboard for posting grades and problem set solutions.

Syllabus (this document)
Lecture Notes
Presentations and Course Google Drive Site
Data Found at


The required book for the class is Greene's Econometric Analysis. This book is expensive. Instead, you may want to pick up an older edition which can be found at less than half the price. There may be minor differences in chapter names and content, but the models we are studying this semester are well-established and the content of either will be sufficient for our needs. With the money saved, you can pick up this one by Wooldridge which is recommended but not required (older editions of this one are also available).

Computing, Computers, and the Class

We will make extensive use of Stata (or if you optionally decide to use it, R). For stata you may want to buy your own copy of the software (through the Grad Plan at and the cost is around $100. Alternatively, a College-owned version can be run remotely on a Unix machine ( from your laptop through X11 or other means. If you decide to go this route, know that the setup is time consuming and you will likely need assistance from IT. If you plan on using, please deal with these logistics during the first week of class. Additionally, there are computer labs around campus (e.g. Morton 240) where stata is accessible. R is free for you to install on as many machines as you like, is open-source, and is also available on lab computers.

We will make some use of the main computer in the classroom for much of what we do together in class. The data for course examples and problem sets will be available on the web for the duration of the course, but you should have archival copies and working backups of all of the programs you have written to take the data from raw to final form. All computer work you do in this class must self-generate the full analysis and write-up using markstat or Rmarkdown as described on the first day of class.

Acceptable Collaboration and Automatic Plagiarism Scanning

Finally, I want to define acceptable collaboration. In this course, I want you to think for yourself in applying these techniques. Using resources outside of class and properly citing it, is perfectly with-in bounds. Taking .do files from your fellow classmates or off of the web and treating them as your own work is not acceptable. Receiving assistance at every critical modeling step is also not acceptable. Asking a classmate about clarification of stata syntax (e.g.- I forgot how to ask for robust standard errors, could you help me with that?), is fine.

Be aware that all assignments submitted to blackboard are automatically scanned by Turnitin and are compared against the assignments for all current and past Cross Section classes as well against information from the web.

Class Schedule

Topic Approx. Duration Summary & Notes Book
Introduction 3 weeks Linear Algebra Intro/ Review Green Appendix
    Stata Intro/Review Supplemental Notes
    Review of Regression Greene 3,4
    Review of Endogeneity Green 12,13.5,13.5.5
Maximum Likelihood 1 week Intro to Maximum Likelihood Green 16
Truncation & Censoring 2 weeks Tobit, Heckman, Poisson Green 24
    and Negative Binomial Green 25
Discrete Dependent Variables 3 weeks Binomial Logit and Probit Green 23
    Multinomial Logit and Probit  
Panel Data 3 weeks Fixed Effects Green 9-9.7
    Random Effects Wooldridge 10
Simulation and Bootstrap 1 week Simulating standard errors TBA