2015/2016, Semester 2

Lee Kuan Yew School of Public Policy (Lee Kuan Yew School Of Public Policy)

Modular Credits: 4

- develop appropriate sophisticated econometric models to estimate the causal relationships for a range of interesting policy issues (e.g. “Does health insurance make us healthier?”) that can then be used to make predictions about the consequences of changing a policy and validate the selected model using an array of statistical tests
- use Stata to estimate these models with cross-sectional and/or panel data and perform the necessary statistical tests to validate the use of the empirical model and be able to interpret the economic meaning of the results.
- conduct an independent empirical research project, on a topic of their choosing, which will help integrate their understanding of the applied econometric techniques covered during the semester

The applied econometric skills students learn in this class, which I use in my own research and have used in my policy work at the U.S. Environmental Protection Agency, will prepare them for work in both the private sector (e.g. consulting firms, think tanks, and banks) and public sector (e.g. government agencies and NGOs), as well as, for future graduate work in economics or a related field.

Introduction to Econometrics by Stock and Watson 3

Introductory Econometrics: A Modern Approach (4

Basic Econometrics (3

- Homework (20%)
- Midterm Exam (20%)
- Non-Cumulative Final Exam (30%)
- Research paper and class presentation (30%)

This course will not include any formal lab sessions. However, from time to time, as necessary, I will conduct informal, optional lab sessions to supplement the course with hands-on experience of data analysis using STATA. The computer labs are already equipped with Stata and students can practice and do the exercises in the lab.

This course will use the Stata statistical package. Stata version 13 is available in the computer lab in LKYSPP.

In case you want your own copy of Stata, plans for students at discounted prices are at http://www.stata.com/order/new/edu/gradplans (Do not buy “Small Stata,” which cannot handle the datasets used in this class.). You can find the contact information of official Stata distributors in Singapore at http://www.stata.com/worldwide/?country=Singapore.

Students in this class should adhere to NUS Honour Code, which can be found at http://www.nus.edu.sg/registrar/adminpolicy/acceptance.html#NUSHonourCode. In addition, the LKY School’s Code of Conduct (http://www.spp.nus.edu.sg/Code_of_Conduct.aspx) lists academic integrity as one of its six important values. Violations of these codes in any form, including cheating in exams and plagiarism, will not be tolerated and will immediately lead to the student getting zero marks and follow-up action.

Plagiarism includes copying all or any part of your classmate’s assignments. To avoid giving the impression that you are passing off other people’s work as your own, you will need to acknowledge conscientiously the sources of information, ideas, and arguments used in your paper. For this purpose, you will use the ‘footnote style’ according to the Chicago Manual of Style, the guidelines for which can be found online at http://www.dianahacker.com/resdoc/p04_c10_s2.html in the companion website for Diana Hacker’s A Writer’s Reference. Please also refer to the handout that was given to you at the Workshop on Plagiarism conducted during the Orientation period.

Use of laptops is discouraged during lectures unless its purpose is related to this course. Instructor reserves the right to ban laptops if their use distracts students. Using cell/smart phones is prohibited during lectures and exams.

- The Experimental Ideal
- Conditional Expectations Function (CEF)
- Linear Regression Model and the CEF
- Causality and the Selection Problem

Reading: MHE ch.s 1-2, sections 3.1.1 & 3.1.2; S&W ch.s 1-2, 4

- What is a matrix?
- Matrix Operations: Addition, Subtraction, Multiplication and Differentiation
- Determinants
- Matrix Rank
- Matrix Inversion

Reading: S&W ch.18 pp 737-740

- The OLS Model in Matrix Notation
- Assumptions of the CLRM in Matrix Notation
- OLS Estimation using Matrix Algebra: β = (X'X)
^{-1}X'Y - Variance-Co-Variance of β using Matrix Algebra: VAR-COVAR = σ
^{2}(X'X)^{-1} - BLUE Property of the OLS Estimator
- Monte Carlo Simulations

Reading: S&W ch.18 pp 698-702 & 712-713

- Saturated Models – Binary (Dummy) Variables & Interaction Terms
- Regression and Causality
- Conditional Independence (Selection on Observables) Assumption
- Short vs Long Regression
- Omitted Variables Bias
- Regression Anatomy Formula
- Bad Control

Reading: MHE sections: 3.1.4 & 3.2.1 - 3.2.3

- Cov(X,U) ≠ 0
- Failure of OLS: Biased and Inconsistent
- IV: Biased, but Consistent
- Omitted Variable Bias
- IV and Causality
- Checking Instrument Validity
- Problems of Identification
- Problem of Weak IVs
- Hausman Test of Simultaneity
- Lagrange-Multiplier Test: Test for Overidentifying Restrictions
- 2SLS – The case of Simultaneous Equations

Reading: MHE sections 4.1, 4.6.1, & 4.6.4; S&W ch. 12

- Pooled OLS Regression
- Fixed Effects and Random Effects
- Within Group Estimator and the Between Group Estimator
- Breush-Pagan Test for Random Effects
- Hausman Test: Fixed or Random Effects
- Clustering SEs

Reading: MHE section ch. 5.1; S&W ch. 10

- The Randomized Control Trial Ideal
- Potential Outcomes, Causal Effects and Idealized Experiments
- Internal vs. External Validity
- Quasi Experiments and the Differences-in-Differences Estimator

Reading: MHE section ch. 5.1; S&W ch. 13

- Sample Selection Bias
- Creating Counterfactuals with Observational Data
- Approximating Random Control Trials – Balancing Covariates
- Estimating DifferenctTreatment Effects
- Conditional Independence (Selection on Observables) Assumption (CIA)
- Stable Unit Treatment Value Assumption (SUTVA)
- Common Support
- Estimating Various Matching Models: Nearest Neighbor; Mahalanobis; and Kernel
- Combining Matching with Regression Analysis
- Heckit Model

Reading: MHE sections 3.3.1 – 3.3.3

**Regression Discontinuity Designs**

- Sharp RD vs. Fuzzy RD
- Estimating a RD
- Internal vs. External Validity

Reading: MHE ch. 6

**Quantile Regression**

- Least Absolute Deviations Regression
- Estimating Causal Effects on the Distribution of Y
- Estimating Conditional Median Function

Reading: MHE sections 7.1.1 & 7.1.3