Syllabus

Welcome to the 2019 Fall Microeconometrics course of Johns Hopkins Applied Economics program.

The objective of the course is to review standard methods for cross-sectional analysis including linear regression and instrumental variables, and introduce modern approaches including difference-in-difference and stochastic control estimation. This will be a hands-on course. Students will learn how to analyze standard micro-data using the statistical package R and R Studio.

The course will use Cameron and Triverdi's Microeconometrics: methods and applications.

Applied Econometrics with R by Kleiber and Zeileis is an excellent companion.

In addition, draft sections of Learning Microeconometrics with R are linked below.

I also highly recommend Judea Pearl and Dana McKenzie, The Book of Why

Also:

• How to look up for R: https://smach.github.io/R4JournalismBook/HowDoI.html
• A guide to writing and presentation: https://sites.google.com/site/amandayagan/writingadvic

Syllabus version placed on JHU website

Grading

1. Final - 25%
2. Paper Draft - 10%
3. Paper Final - 20%
4. Homework - 4 x 10% = 40%
5. Participation - 5%

Introduction

9/5

• Theory: Introductions. Causality, treatment effects and uncertainty.
  • Text: Ch 2, 25
• Application: Should we increase the minimum wage to $15/hour?
  • Khan Academy analysis of the minimum wage
  • CBO Report
• Practical: Introduction to R
  • Download Rstudio and R
  • R for Researchers (UW-Madison Social Science Computing Cooperative)
  • Data (from Google Drive)

Causality, Experiments and Identification

9/12

• Theory: Ordinary Least Squares
  • Adams: Ch 1
  • Text: Ch 4.1-4.4
• Application: Returns to Schooling
  • Card (1993), Using Geographic Variation
• Practical: Card's Returns to Schooling data
  • Data (from Google Drive)
  • R for Researchers: OLS (UW-Madison SSCC)

9/19

HW1 Due 6.30pm

• Theory: Ordinary Least Squares
  • Adams: Ch 1
  • Text: Ch 4.1-4.4
  • Kahn Academy: Matrix Algebra
  • Kahn Academy: Solving Linear Systems
• Application: Bias in Lending
  • CFPB Fair Lending
  • Munnell et al (1996), Mortgage Lending in Boston
• Practical: Boston HMDA data
  • HMDA data (Google Sheets)
  • Code Book (pdf)

9/26

• Theory: Instrumental Variables
  • Adams: Ch 2
  • Text: Ch 4.9, 25.7
• Application: Returns to Schooling
  • Card (1993), Using Geographic Variation
• Practical: Estimating IV on Card data
  • Data (from Google Drive)

10/3

HW2 Due 6.30pm

• Theory: Instrumental Variables
  • Adams: Ch 2
  • Text: Ch 4.9, 25.7
  • Imbens and Wooldridge (2007), What's New in Econometrics: LATE
• Application: Returns to Schooling
  • Card (1993), Using Geographic Variation
• Practical: Estimating IV on Card data
  • Data

10/10

• Theory: Identification and Bounds
  • Adams: Ch 3
  • Text: Ch 2, 25
  • Rubin (1974), Estimating Causal Effects
  • Imbens and Wooldridge (2007), What's New In Econometrics: Bounds
  • Manski (1990), Nonparametric Bounds on Treatment Effects
  • Angrist, Treatment Effects
• Application: More Guns, Less Crime
  • Manski and Pepper (2000), Monotone Instrumental Variables
• Practical: Crime Data

Causality, Structure and Identification

10/17

HW 3 Due 6.30pm

• Theory: Choice Model
  • Adams: Ch 4
  • Text: Ch 14
  • Imbens and Wooldridge (2007), What's New in Econometrics: Discrete Choice
  • Berry (1994), Estimating Discrete Choice
• Application: Demand for BART
  • History of BART
  • McFadden (1974), Measurement of Urban Travel
• Practical: Demand for Trains
  • 
    

10/24

• Discuss Paper
  • bring ideas.
  • bring data sets.
    • Bill Greene's Microeconometric Course
    • Bill Greene's Applied Econometrics
    • Stock and Watson Data Archive
    • Cameron and Trivedi Data Archive
    • Replicated Studies
    • Studies Lacking Replication
    • Harvard Poverty Lab
    • Harvard Dataverse
• Practical:
  • HMDA Data (Note that you should use the other version under 9/20 for the homework)

10/31

HW4 Due 6.30pm

• Theory: Selection
  • Adams: Ch 5
  • Text: Ch 16
• Application: Measuring Income by Gender
  • Text: Ch 16.7.1
• Practical: Measuring Income by Gender
  • Data
  • NLSY97_gender (Google Sheets)
    • Variable names (pdf)
  • NLS Investigator website

11/7

Paper Due Draft 6.30pm

• Theory: Auctions
  • Adams: Ch 6
  • Dutch flower auction
  • American cattle auction
  • Vickery (1961), Counterspeculation, Auctions and Competitive Sealed Traders
• Application: Estimating Auction Models
  • Timber Auction Data
  • Phil Haile's Timber Auction Data

Repeated Measurement

11/14

• Theory: Panel Data
  • Adams: Ch 8
  • Text: Ch 21, 22.6
  • Card and Krueger (1994), Minimum Wage and Employment
  • Currie and Fallick (1996), Minimum Wage and Employment of Youth
  • Doudchenko and Imbens (2016), Balancing, Regression, Difference-in-Difference
• Application: Minimum Wage
  • Senate Testimony by Alan Krueger in 1995 (starts about 3.18.00)
• Practical: Minimum Wage
  • Data (Google sheet)
  • Column Names (Google Sheet)

11/21

• Theory: Panel Data
  • Adams: Ch 8
  • Text: Ch 21, 22.6
  • Card and Krueger (1994), Minimum Wage and Employment
  • Currie and Fallick (1996), Minimum Wage and Employment of Youth
  • Doudchenko and Imbens (2016), Balancing, Regression, Difference-in-Difference
  • Adams (2018), Measuring Treatment Effects with Big N Panels
• Practical: Minimum Wage

11/28 Thanksgiving (No Class)

12/5

Paper Due 6.30pm

Practice Problems (Not Due)

• Review
• Machine Learning

12/12

Final