> Teaching > Econometrics
Econometrics I (fall 2018)
This is the support page for Econometrics I (Applied Econometrics with R, fall 2018). You can find materials (slides, sample programs, assignments) and other information for the course.
As a preparation for this course, you need to install R and related programs in your own laptop computer and bring it to the classroom. Here is a brief guide of how to install the programs. We are going to use it from the second lecture.
Course Information
Time and classroom: Tue 19:00  21:20, A410
Textbook:
《计量经济学：第三版》英文版，斯托克、沃森著，格致出版社，2013. ISBN: 9787543222274.
Amazon: https://www.amazon.cn/dp/B00R7EEEUY/
当当网: http://product.dangdang.com/23621137.html
京东: https://item.jd.com/11583008.html
Instructor: Dr. JiaPing HUANG
Email: huangjp #at# szu . edu . cn
Office hours: Mon/Tue 13:00  14:00
Grading: assignments (3 × 10%) + reading report (20%) + final exam (50%)
Schedule and Materials

Lecture 1 (Sep 18, Week 3):
 Introduction
[Slides]
 Introduction

Lecture 2 (Sep 25, Week 4):
 R installation
 R basics (1): calculation, vector, matrix
[Slides]

Lecture 3 (Oct 9, Week 6):
 R basics (2): matrix, script file and project, data frame, graphics
[Slides]
 R basics (2): matrix, script file and project, data frame, graphics

Lecture 4 (Oct 16, Week 7):
 R programing : function, condition
[Slides]
 R programing : function, condition

Lecture 5 (Oct 23, Week 8):
 R programing : loop
[Slides]
 R programing : loop

Lecture 6 (Oct 30, Week 9):
 R programing : applications
[Slides]
 R programing : applications

Lecture 7 (Nov 6, Week 10):
[Supplement material: review of probability]
[Supplement material: review of statistics] Linear regression (1): model fitting
[Slides]
[caschool data] [Description]
 Linear regression (1): model fitting

Lecture 8 (Nov 13, Week 11):
 Linear regression (2): multiple linear regression, hypothesis testing
[Slides]
 Linear regression (2): multiple linear regression, hypothesis testing

Lecture 9 (Nov 20, Week 12):
 Nonlinear regression
[Slides]
 Nonlinear regression

Lecture 10 (Nov 27, Week 13):
 Regression with panel data
[Slides]
[fatality data] [Description]
 Regression with panel data

Lecture 11 (Dec 4, Week 14):
 Binary dependent variables
[Slides]
 Binary dependent variables

Dec 11, Week 15:
 No class

Lecture 12 (Dec 18, Week 16):
 Instrumental variables
[Slides]
 Instrumental variables

Review (Dec 25, Week 17)

Final Exam
 Dec 29, Saturday, 8:30  11:30, A410
 Only the textbook is allowed to be used during the exam.
Assignments
Assignment 1: R programming
 Learn the insertion sort algorithm from Wikipedia (or other websites): https://en.wikipedia.org/wiki/Insertion_sort
 Write a program that sorts a given sequence in descending order which meets the following conditions:
 use insertion sort algorithm to write a function named
inssort()
;  you should not use
while
loop;  your function should print all partially sorted sequences, one in a line, e.g.,
> inssort(c(3,5,1,4,2))
⏎
[1] 3 5 1 4 2
[1] 5 3 1 4 2
[1] 5 3 1 4 2
[1] 5 4 3 1 2
[1] 5 4 3 2 1
 generate a sequence with 10 positive integers and use the above function to sort it, and save your sorted sequence in a new variable.
 use insertion sort algorithm to write a function named
 Save your program in an .R script file and submit it by email before 20181106 19:00.
Assignment 2: Multiple linear regression
Use the California Test Score dataset (caschool.xlsx) to explain test scores (testscr) by other variables. Take the single regression on studentteach ratio (str) as the base specification. You can include other variables from the dataset to build alternative specifications.
Perform multiple linear regression analysis for the base specification and three alternative specifications that are not given in Table 7.1 (page 280). Answer the following questions.
 Q1: Write down your regression models and corresponding OLS regression results in equation form.
 Q2: Summarize your regression results in a table.
 Q3: Discuss your results (such as economic and statistical interpretation of coefficients, multicollinearity, goodness of fit, etc.).
Write a report with MSWord and submit it by email before 20181120 19:00.
Assignment 3: Using econometrics in research
Suppose you want to investigate the effect of export on the economic development of a country using econometric methods. You may need to think the following questions:
 What variables are you going to use as independent and dependent variables?
 Which kind of data as well as regression models you are going to use?
 Which kind of estimation biases you need to consider? How can you get rid of them?
Answer the above questions (either in Chinese or in English) within one page. Submit your report in a PDF file by email before 20181211 19:00.
Reading Report and Journal List
 Each student should select an applied econometrics article that is published on 2010 or later in a journal from the journal list below, read it intensively, and write a report in English.
 The report must contain a summary of the article, your comments describing the pros and cons, and optionally your research idea inspired by the article.
 Keep your report no longer than two pages.
Journal list (not a ranking)
 American Economic Review
 Econometrica
 Quarterly Journal of Economics
 Journal of Political Economy
 Review of Economic Studies
 Review of Economics and Statistics
 Economic Journal
 American Economic Journal: Applied Economics
 RAND Journal of Economics
 Journal of Applied Econometrics
 Journal of Business and Economic Statistics
 International Economic Review
 Journal of Econometrics
 Scandinavian Journal of Economics
 Journal of Labor Economics
 Labor Economics
 Journal of Public Economics
 Journal of Economic Growth
 Journal of Health Economics
 Health Economics
 Journal of the European Economic Association
 European Economic Review
 Oxford Bulletin of Economics and Statistics
Useful References
 Studenmund, A. H., Using Econometrics: A Practical Guide, 6th Edition, Pearson, 2011.
 Kleiber, C. and Zeileis, A., Applied Econometrics with R, Springer, 2008.
 Heiss, F., Using R for Introductory Econometrics, 2016.
 Dennis, B., 《R语言初学指南》，译者：高敬雅、刘波，人民邮电出版社，2016.
 Kabacoff, R. I., 《R语言实战》第二版，译者：王小宁等，人民邮电出版社，2016.
 QuickR, http://www.statmethods.net/
 Econometrics with R, https://www.econometricswithr.org/
Further Reading
Introductory level
 Gujarati, D. & Porter, D., Basic Econometrics, 5th edition, McGrawHill Education, 2008.
 Stock, J. & Watson, M., Introduction to Econometrics, 3rd edition, Pearson, 2015.
 Wooldridge, J., Introductory Econometrics: A Modern Approach, 6th edition, Cengage Learning, 2015.
 Kennedy, P., A Guide to Econometrics, 6th edition, WileyBlackwell, 2008.
Intermediary level
 Greene, W., Econometric Analysis, 8th edition, Pearson, 2017.
 Cameron, A. & Trivedi, P., Microeconometrics: Methods and Applications, Cambridge University Press, 2005.
Advanced level
 Wooldridge, J., Econometric Analysis of Cross Section and Panel Data, 2nd edition, MIT Press, 2010.
 Hayashi, F., Econometrics, Princeton University Press, 2000.
Modern monographs
 Angrist, J. D. and Pischke, J.S., Mastering ‘Metrics: The Path from Cause to Effect, Princeton University Press, 2015.
 Angrist, J. D. and Pischke, J.S., Mostly Harmless Econometrics: An Empiricist’s Companion, Princeton University Press, 2008.