ECON 407: Syllabus

Syllabus for Cross Section Econometrics

ECON407/ PUBP615

Fall 2017

Instructor: Rob Hicks
Office: Tyler Hall 252
Class Time: TR 3:30 - 4:50 pm
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 4:00-6:00 pm or by appointment
  • Email Policy : 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. Emails must contain concise questions no longer than what would be amenable to respond to email. If you use the email address listed at the top of this page, it will auto-tag the email.
  • 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 markdoc (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 mid-term is scheduled for October 18 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 30 First day of class
Oct 14 - 17 Fall Break
Oct 18 Mid-Term
Nov 22 - 26 Thanksgiving
Dec 6 Last day of this class
Dec 14 TH (9am - 12pm) Final Exam
  • Policy on Late Assignments : 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.
  • 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.
Item Link
Presentations and Other Materials Course Google Drive Site
Data (for stata and R) Found at
  • Book : 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 is 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 especially well suited to very large data sets 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 start-up costs are higher and you will likely need assistance from IT to get everything set up. 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.
  • 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 markdoc or Rmarkdown as described above.
  • The class: Ensure that you understand the material before we move past it. Ask questions. We will on many classroom occasions employ actual data and Stata, and you may want to follow along on your own computers. I encourage you to bring your computers to class.

Acceptable Collaboration

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.

Class Schedule

Topic Approx. Duration Summary & Notes Book$^1$
Introduction 3 weeks Linear Algebra Intro/ Review GA
Stata Intro/Review A
Review of Regression G3,4
Review of Endogeneity G12,13.5,13.5.5
Panel Data 3 weeks Fixed Effects G9-9.7
Random Effects W10
Maximum Likelihood 1 week Intro to Maximum Likelihood G16
Truncation & Censoring 2 weeks Tobit, Heckman, Poisson G24
and Negative Binomial G25
Discrete Dependent Variables 3 weeks Binomial Logit and Probit G23
Multinomial Logit and Probit
Simulation and Bootstrap 1 week Simulating standard errors TBA

$^1$ G denotes Greene, A is the online appendix, and W denotes Wooldridge.