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PP5171 

ADVANCED APPLIED ECONOMETRICS FOR POLICY ANALYSIS
   2015/2016, Semester 2
   Lee Kuan Yew School of Public Policy (Lee Kuan Yew School Of Public Policy)
Modular Credits: 4
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Learning Outcomes

TopThe purpose of this course is to provide students with a ‘toolbox’ and working knowledge of advanced crosssectional and panel data econometric techniques frequently used in applied microeconomic policy analysis and research. This course will cover major extensions to the standard OLS regression model and provide students with an introduction to the ‘cutting edge’ techniques used today to evaluate microeconomic theories and policies, including instrumental variables, difference-in-differences, matching estimators, regression discontinuity and quantile regressions. The emphasis of the course will be on estimating causal relationships that can then be used to make predictions about the consequences of changing a policy.

Prerequisites

TopNA

Syllabus

TopPP5171 – Advanced Applied Econometrics for Policy Analysis
Lee Kuan Yew School of Public Policy, National University of Singapore
Spring 2016
 
Instructor:                  Ron Shadbegian: spprjs@nus.edu.sg
Time:                           Wednesday, 6:30 pm – 9:30 pm
Classroom:                 SR 3-5
Course Description: Regression analysis plays an extremely important role in the empirical analysis of public policies and research and as such will be the foundation of this course. The purpose of this course is to provide students with a ‘toolbox’ and working knowledge of advanced cross-sectional and panel data econometric techniques frequently used in applied microeconomic policy analysis and research. This course will cover major extensions to the standard OLS regression model and provide students with an introduction to the ‘cutting edge’ techniques used today to evaluate microeconomic theories and policies, including instrumental variables, difference-in-differences, matching estimators, regression discontinuity and quantile regressions. The emphasis of the course will be on estimating causal relationships (e.g. whether our estimates will deliver answers to questions like: “Does health insurance make us healthier?”) that can then be used to make predictions about the consequences of changing a policy. Given time constraints the course cannot go into too much depth in regard to any particular technique, but will instead endeavor to provide you with enough knowledge about each one so that you will understand what various methods do, how and when to use them, and when not to use them rather than on the study of the mechanical and mathematical features of estimators. None the less to do this we will have to learn the mechanics of some of the estimators. The level of mathematics in this course will not be too complex, but we will need to use tools including expectation operators, multivariate calculus and simple matrix algebra at times, and these tools will reviewed in class as they become necessary.
 
Learning Objectives: By the end of the semester each student should be able to:
 
 
  • 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.
 
Office hours:  TBD.
Required Texts: Mostly Harmless Econometrics: An Empiricists Companion by Angrist and Pischke
                          Introduction to Econometrics by Stock and Watson 3rd edition        
Supplementary Texts: 
Introductory Econometrics: A Modern Approach (4th or 5th) edition by Jeff Wooldridge
Basic Econometrics (3rd or 4th) edition by by Damodar N. Gujarati
 
Grading: Your grade for the class will be divided between:
 
  • Homework (20%)
  • Midterm Exam (20%)
  • Non-Cumulative Final Exam (30%)
  • Research paper and class presentation (30%)
The midterm will take place on TBD.  The final exam will occur on TBD.
 
Lab Sessions
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.
 
Software
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.

Academic Integrity
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.
Course Outline
 
Part I              Introduction: Random Variables, Linear Regression Model, and Causality
 
 
  • 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
 
Part II             Matrix Algebra of OLS
 
                        A. Introduction to Matrix Algebra
 
 
  • What is a matrix?
  • Matrix Operations: Addition, Subtraction, Multiplication and Differentiation
  • Determinants
  • Matrix Rank
  • Matrix Inversion
 
Reading:          S&W ch.18 pp 737-740 or Gujarati Appendix B
 
                        B. The Matrix Approach to OLS
 
 
  • The OLS Model in Matrix Notation
  • Assumptions of the CLRM in Matrix Notation
  • OLS Estimation using Matrix Algebra: β = (X'X)-1X'Y
  • Variance-Co-Variance of β using Matrix Algebra: VAR-COVAR = σ2(X'X)-1
  • BLUE Property of the OLS Estimator
  • Monte Carlo Simulations
 
Reading:          S&W ch.18 pp 698-702 & 712-713 or Gujarati Appendix C
 
Part III           Multivariate Regression
 
 
  • 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
 

Part IV           Instrumental Variables/Two-Stage Least Squares
 
  • 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
 
Part V             Models for Panel Data: Combining Cross-Sectional Data and Time Series Data
 
  • 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
Part VI           Experiments and Quasi-Experiment
 
  • 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
Part VII          Matching Estimators and Selection Models
 
  • 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
Part VIII        Two Additional Extensions
 
  1. Regression Discontinuity Designs
  • Sharp RD vs. Fuzzy RD
  • Estimating a RD
  • Internal vs. External Validity
 
Reading: MHE ch. 6
 
 
  1. 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
 
 

Preclusions

TopNA

Workload

Top3-0-0-2-5

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week

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