Quantitative Methods

 Quantitative Methods for Public Policy

Fall, 2017

Instructor: Dr. Tieshan Sun

Contact: tieshansun@hotmail.com

Class: Tuesday 8:30-11:30 in Leo KoGuan Building 506

 

COURSE DESCRIPTION

This course will focus on the use of quantitative methods, specifically regression analysis, applied to issues in public policy. The course will systematically cover the fundamental concepts and methods of applied regression analysis. Topics covered will include building, interpreting, evaluating, and validating regression models, with emphasis on practical aspects.

The orientation of the course is applied and the goal is to expand the knowledge of students about quantitative methods, and to train students to effectively apply econometric methods to policy analysis, and evaluate the claims of those who use quantitative research to promote specific policies.

This is a 3-credit course, which follow a seminar format and will include formal lectures, labs and group discussions. The course fulfills the quantitative methods requirement for graduates in public policy.

RECOMMENDED TEXTS (NOT REQUIRED)

Guiarati D.N., Porter D.C. Essentials of Econometrics (4th Edition). Beijing: China Machine Press, 2010.8.

Studenmund A.H. Using Econometrics: A Practical Guide (6th Edition). Beijing: Tsinghua University Press, 2011.1.

Kenneth J. Meier and Jeffrey L. Brudney (2002). Applied Statistics for Public Administration (fifth edition). Orlando, FL: Harcourt Inc.

SOFTWARE AND COMPUTER

The methods covered in this course require the use of statistical software. It will be illustrated in class how to use SPSS (Statistical Packages for the Social Sciences) (or some other software) to apply various methods covered in the course. Students are required to bring their own laptops into class.

 COURSE EVALUATION

The course grade will be based on three components: problem sets, the final examination, and an application.

1.  Problem Sets. Problem sets will account for 30% of the course grade.

2.  Examination. The final exam will account for 40% of the course grade, and will be given by the end of the semester.

3.  Analysis Project. Students are required to apply regression methods studied in the course to a policy problem. The project accounts for 30% of the course grade. Students are required to select a policy problem of interest and formulate the problem so that it can be addressed through regression analysis. Students will then collect data, perform the analysis, and write up the report based on their results.

 ANALYSIS PROJECT INFORMATION

The analytical project should be done independently. You are free to identify a project topic on your own but it needs fit in the framework of the class.

The final project report is due on 15 January, 2018. No matter what project you choose, I would like to see you demonstrate your ability in applying the material learned in class. You will be graded based on the technical content, originality/difficulty, and presentation (neatness) of the report.

 

COURSE OUTLINE AND CLASS SCHEDULE

WEEK

TOPICS

ASSIGNMENTS AND DUE DATES

#1

12 Sep.

Course   Introduction

 

#2

19 Sep.

Basics of Quantitative Research Design and Statistical Inference

 

#3

26 Sep.

Introduction to Linear Regression I

basics of regression analysis

simple linear regression model

estimation of coefficients

statistical assumptions of   linear regression

simple vs. multiple linear   regression model

 

#4

3 Oct.

Holiday   Break (National Day Festival)

 

#5

10 Oct.

Introduction   to Linear Regression II

hypothesis testing

analysis of variance

prediction

 

#6

17 Oct.

Lab 1

n  organizing and exploring data

n  linear regression analysis

Problem Set 1

#7

24 Oct.

1/Regression   Model Specification

model selection

functional form and   transformation of variables

2/Dummy variables and interaction effects in   regression

 

#8

31 Oct.

Regression   Diagnostics

residual analysis

multicollinearity

heteraskedasticity

serial correlation

Problem Set 1 DUE

#9

7 Nov.

Lab 2

model specification

regression diagnostics

Problem Set 2

#10

14 Nov.

Lab 2   (Continued)

 

#11

21 Nov.

Qualitative Dependent Variable Models

Problem Set 2 DUE

#12

28 Nov.

Time Series Models

 

#13

5 Dec.

Lab 3

logistic regression

time series application

Exam Sample Questions

#14

12 Dec.

Lab 3   (Continued)

Final   Review

Q&A

#15

19 Dec.

Final Exam

 

15 Jan., 2018

 

Analysis   Project Report DUE

 

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