Econometrics I
ECON-7910 Econometrics I, Fall 2021
This class will begin with an exploration of the properties required to obtain causality in econometrics. We will focus largely on the theoretical properties of conditional expectations operators and basic asymptotic theory applied to ordinary least squares (OLS), two-stage least squares (2SLS), and nonlinear methods such as discrete response models. The class details can be referred to Professor Phillip Shaw's website.
Required Textbook
Wooldridge, J., 2010. Econometric Analysis of Cross Section and Panel Data, MIT Press, Edition 2.
Optional Textbook
Wooldridge, J., 2012, Introductory Econometrics: A Modern Approach (PDF).
Schedule
◾ Session 1 - Chapter 1 & 2
∘ Chapter 1: Causal Inference and Identification ( Handout 0)
‣ Whited, Toni, and Kahn, Jay, 2017, "Identification Is Not Causality, and Vice Versa", Review of Corporate Finance Studies
‣ Cinelli, Forney, and Pearl, 2021, Good Control VS Bad Controls
‣ Robert A. Miller also shared lecture notes on structural models
∘ Introduction to R (Notes, 401K data)
∘ Chapter 2: Conditional Expectations and Related Concepts in Econometrics ( Handout 1, HM1 Solution)
‣ Partially linear model (shown in HW2) contains parametric and nonparametric elements, is one type of semiparametric model.
◾ Session 2 - Chapter 3: Basic Asymptotic Theory ( Handout 2, HM2 Solution)
‣ Consistency and Unbiasedness in R
◾ Session 3 - Chapter 4: Single-Equation LM and OLS - Asymptotic ( HM3 Solution, HM3 Coding)
◾ Session 4 - Chapter 4: Single-Equation LM and OLS - Omitted Variables and Measurement Error ( Handout 3, HM4 Solution)
‣ Holzer, Block, Cheatham, and Knott, 1993, "Are Training Subsidies for Firms Effective? The Michigan Experience", Industrial and Labor Relations Review
‣ Blackburn and Neumark, 1992, "Unobserved Ability, Efficiency Wage, and Interindustry Wage Differentials", Quarterly Journal of Economics
‣ Cornwell and Trumball, 1994, "Estimating the Economic Model of Crime with Panel Data", The Review of Economics and Statistics
◾ Session 5 - Chapter 5: IV Estimation of Single-Equation Linear Models ( Handout 4, HM5 Solution Part 1, HM5 Solution Part 2)
◾ Session 6 - Chapter 6: Additional Single-Equation Topics ( Handout 5, HM6 Solution)
‣ Card and Krueger, 1994, "Minimum Wages and Employment", The American Economic Review (A CIC Replicate)
‣ Athey, S and Imbens, G (2006): Identification and Inference in Nonlinear Difference-in-Differences Models, Econometrica
◾ Session 7 - Chapter 7: System Estimation of Instrumental Variables and Simultaneous Equations Models
◾ Session 8 - Chapter 8: System Estimation by Instrumental Variables ( Handout 6)
◾ Session 9 - Lewbel Internal Instrumental Variable ( Handout 7 (By Raluca Gui), HM7 Solution)
‣ Lewbel, Arthur., 2012. "Using Heteroscedasticity to Indentify and Estimate Mismeasured and Endogenous Regressor Models", Journal of Business & Economic Statistics
‣ Lewbel, Arthur., 2016. "The Identification Zoo - Meanings of Identification in Econometrics", Journal of Economic Literature
‣ Lewbel, Arthur. ,and Christopher F. Baum, 2019. "Advice on using heteroscedasticity based identi cation".
‣ Courtemanche. Pinkston and Stewart, 2020. "Time Spent Excercising and Obesity: An Application of Lewbel's Instrumental Variables Method", NBER Working Paper.
◾ Session 10 - Nonparametric Models ( Handout 8, Handout 9)
Grading Policy
HW1: 2.1 (30 pts), 2.2 (30 pts), 2.4 (20 pts), 2.7 (20 pts)
HW2: 3.1 (10 pts), 3.3 (10 pts), 3.5 (25 pts), 3.7 (25 pts), 3.8 (30 pts)
HW3: 4.1 (30 pts), 4.2 (30 pts), 4.3 (20 pts), 4.5 (20 pts)
HW4: 4.11 (20 pts), 4.12 (20 pts), 4.13 (24 pts), 4.14 (36 pts)
HW5: 5.1 (20 pts), 5.3 (30 pts), 5.7 (30 pts), 5.9 (20 pts)
HW6: 6.1 (25 pts), 6.2 (25 pts), 6.3 (25 pts), 6.8 (25 pts)
HW7: a (10 pts), b (12 pts), c (12 pts), d (14 pts), e(12 pts), f(12 pts), g(14 pts), h(14 pts)
Chunyu Qu