PP5138
ECONOMETRICS FOR PUBLIC POLICY ANALYSIS (2014/2015, Semester 2) 

 MODULE OUTLINE Created: 29-Nov-2014, Updated: 29-Nov-2014
 
Module Code PP5138
Module Title ECONOMETRICS FOR PUBLIC POLICY ANALYSIS
Semester Semester 2, 2014/2015
Modular Credits 4
Faculty Lee Kuan Yew School of Public Policy
Department Lee Kuan Yew School Of Public Policy
Timetable Timetable/Teaching Staff
Module Facilitators
DR Khuong Minh Vu Lecturer
DR Kim Hye Won Lecturer
Weblinks
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Learning Outcomes | Syllabus


 LEARNING OUTCOMES Top
The purpose of this course is to prepare students for becoming both critical consumers and competent producers of quantitative evidence used in the public policy arena. This course provides students with a solid grounding on economic theory and statistical techniques used to analyze public policy. At the end of the course, students will be able to use advanced econometric tools on real world policy problems and draw policy implications. The major topics covered include: inference and hypothesis testing, simple regression analysis, multiple regression analysis, non-linear regression models, binary dependent variable models, program evaluation, panel data analysis, and time series analysis and forecasting.


 SYLLABUS Top
National University of Singapore 
Lee Kuan Yew School of Public Policy
 
PP5138. Econometrics for Public Policy Analysis
Semester 2 AY2014/2015
 
 
Lectures:         Tuesdays 2:00 – 4:00pm, SR3-1
Lab sessions:   Tuesdays 4:00 – 5:00pm, Computer Lab
 
Instructor 1:    KIM Hye-Won (Erin), Assistant Professor
Office:             LKS #02-04
Office hour:    By appointment (office)        
E-mail:                        sppkhw@nus.edu.sg
 
Instructor 2: VU Minh Khuong, Associate Professor
Office:             OTH, Wing A, 3rd floor         
Office hour:    By appointment (office)        
E-mail:                       sppkmv@nus.edu.sg
 
 
Course Objectives
 
The purpose of this course is to prepare students for becoming both critical consumers and competent producers of quantitative evidence used for policy analysis and policymaking.  By the end of this course, students should be able to:
  • analyze data using econometric techniques.
  • evaluate quantitative analyses and arguments for policy analysis.
  • make policy recommendations based on quantitative evidence.
 
The major topics covered are: probability, inference and hypothesis testing, simple regression analysis, multiple regression analysis, non-linear regression models, panel data analysis, binary dependent variable models, program evaluation, and time series analysis and forecasting.
 
 
Textbook
 
Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd edition, International edition, Pearson Education Limited.
 
This book is required and available for purchase at the NUS Co-op.  One copy will be also kept on RBR at C. J. Koh Law Library.  This book has its own website at http://wps.pearsoned.co.uk/ema_ge_stock_ie_3/193/49605/12699039.cw/index.html).  It contains useful resources for students, including datasets and replication files used in examples and exercises presented in the book.  You are encouraged to visit the website and make use of it. 

 
Prerequisites
 
  • PP5404 (Policy Analysis) or equivalent understanding of cross-sectional Ordinary Least Squares regression analysis
  • Experience of using a statistical package (e.g. Stata, SAS, SPSS)
 
 
Course Requirements
 
  • Problem Sets 
    • Problem set 1:  20% (Week 4)
    • Problem set 2:  20% (Week 10)  
 
Problem sets are individual assignments.Students should submit hard-copies of assignments at beginning of the class on the due date.Late submissions will not be accepted for credit.  
 
  • Mid-Term Exam (3 March in class):  20%
 
  • Research Project
    • Presentation:  10% (Week 13)
    • Paper:             20% (30 April, midnight)
 
A team of one or two students is required to write a research paper to analyze a policy issue of interests using regression analysis, and to present the paper in class.A detailed guideline for the project will be distributed in advance.
 
  • Class participation:  10%
You are encouraged to participate in class discussions to foster in-depth and vibrant discussions.I encourage you to raise questions to clarify any confusion that you have.
 
 
Lab Sessions and Software
 
Lab sessions will focus on empirical applications and review of materials covered in lectures.  Participation in lab sessions is mandatory.  This course will use the Stata statistical package.  Stata version 12 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.
           
In addition to the lab sessions and handouts available on IVLE, the following webpages at the University of California Los Angeles provide helpful resources to learn Stata:
http://www.ats.ucla.edu/stat/stata/modules/default.htm
http://www.ats.ucla.edu/stat/stata/default.htm
 
 
Academic Integrity
 
Students in this class should adhere to NUS Honour Code, which can be found athttp://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 cellular phones is prohibited during lectures and lab sessions.
 

Course Schedule
 
Date Topics Readings Problem Sets
Week 1 Shopping week  
Week 2 Review of statistics,
Introduction to multiple regression
Ch 1~5
Ch 6~7
Week 3 Multiple regression: Functional form
Multiple regression: Interaction term
Ch 8
Week 4 Multiple regression: Binary dependent variable
Multiple regression: Internal and external validity
Ch 11
Ch 9
PS1
Week 5 Multiple regression: Panel data
Experiment and quasi-experiment (I): Diff-in-diff design
Ch 10
Ch 13
Week 6 Quasi-experiment (II): Instrumental variables regression
Wrap-up
Ch 12
Recess week
Week 7 Mid-Term Exam  
Week 8 Regression analysis: Practice problems
Week 9 Time series econometrics: Basic concepts and models
(eLearning Exercise)
Ch 14
Week 10 Regression analysis with time series data and forecasting Ch 14-16
 
PS2
Week 11 Estimation of Dynamic Causal Effects Ch 15
Week 12 Additional topics in time series regression: VAR and VEC models Ch 16
Week 13 Research project: In-class presentation
Reading week
Exam week Research project: Paper due 30 April
 
 
 

 
Additional readings for Week 8 ~ 12
 
 
Week Topic Readings list
Week 8 Regression analysis: Practice problems
  • Krupp, C and Pollard P. (1996) Market Responses to Antidumping Laws: Some Evidence from the U.S. Chemical Industry. The Canadian Journal of Economics, 29(1): 199-227.
  • Fair, Ray C. (1996) Econometrics and Presidential Elections." Journal of Economic Perspectives, 10(3): 89- 102.
Week 9 Time series econometrics: Basic concepts and models
(eLearning Exercise)
  • *** Whittington, Leslie A., James Alm, and H. Elizabeth Peters, (1990) “Fertility and the Personal Exemption: Implicit Pronatalist Policy in the United States," American Economic Review, 1990, 80 (3), 545-556.
  • Crump, Richard, Gopi Shah Goda, and Kevin J. Mumford, “Fertility and the
Personal Exemption: Comment," American Economic Review, 2011, 101 (4), 1616-1628.
Week 10 Regression analysis with time series data and forecasting
  • *** “Time series forecasts of international travel demand for Australia”, Christine Lim and Michael McAleer, Tourism Management 23 (2002) 389–396
  • “ARIMA forecasting of primary energy demand by fuel in Turkey”, VŞ Ediger and S Akar, Energy Policy 35 (2007), 1701-1708.
Week 11 Estimation of Dynamic Causal Effects
  • *** Granger, C., B. Huang and C. Yang (2000) Bivariate Causality Between StockPrices and Exchange Rates in Asian Countries, The Quarterly Review of Economics and Finance, 40, 2000, 337-354.
  • Ashley. R., Granger, C.W.J. and R. Schmalensee, 1980, “Advertising and Aggregate Consumption: An Analysis of Causality,” Econometrica, 48, 1149-1168.
Week 12 Additional topics in time series regression: VAR and VEC models
  • *** Gaolu Zou, K.W. Chau (2006) “Short- and long-run effects between oil consumption and economic growth in China”, Energy Policy 34 (2006), 3644–3655.
  • “Human capital and economic growth: Time series evidence from Greece”, D. Asteriou, G.M. Agiomirgianakis, Journal of Policy Modeling 23 (2001) 481–489
 
 
Note: the articles marked with “***” are required readings.