Peking University HSBC Business School
Fall 2012 Module 1
Applied Econometrics
Instructor:
Qian Chen, Email: qianchen@phbs.pku.edu.cn
Meeting time/Venue: Tue & Fri 10:10am-12:00pm, TBA
Credits: 3
TA: TBA
Course objectives:
Welcome to the Applied Econometrics, where you will learn to use econometric methods as useful tools to address issues in the area of business and economics. There will be emphasis on the econometric methods and models illustrated by examples and scenarios using real-world data. At the end of the course, students should have a deeper theoretical understanding of econometrics, as well as statistical properties of econometric models and methods. Empirically, students should be able to construct appropriate models from real-world data and provide sound interpretations from the obtained results.
Supporting textbook:
Econometric methods with applications in business and economics, Heij C, de Boer P, Franses PH, Kloedk T, Van Dijk HK
Lecture notes, data sets and other learning materials will be provided before class.
Software
The course involves a considerable amount of computing, and students are encouraged to use Excel, Eviews and/or Matlab to solve some tasks in their assignments.
Assessment:
Grade of Mathematic course 30%
Problem sets 10%
Short individual written assignment 10%
Group assignment report 15%
Final exam 35%
Total 100%
There will be four problem sets, one per fortnight. Students are expected to work individually and submit their work within one week after each problem set is distributed.
The short individual written assignment will be due by the end of the fifth week. A hard copy is required, soft copy is optional.
The group assignment report will be due by the end of the last week of module I. A hard copy is required.
Late submission of the work and both assignments will not be accepted and considered failure of the tasks.
The final exam will cover all the topics in class.
Academic honesty:
Academic dishonesty will not be tolerated in this class. Students are expected to abide by the code of academic honesty of PHBS. Failure to abide the code will be prosecuted through the University’s judiciary system. Ignorance of the code is not a defense against a charge of dishonesty.
Course outlines:
A. Introduction and review of Statistics (Ch. 1)
---Data, sample, random variable, parameter estimation, hypothesis tests
B. Simple linear regression (Ch. 2)
---OLS estimation, inference, prediction
C. Multiple linear regression (Ch. 3)
---OLS estimation, variable selection, inference
D. Maximum likelihood and generalized method of moments (Ch. 4.3, 4.4)
E. Further topics in regression analysis (Ch. 5)
---Functional forms, dummy variables, Heteroskedasticity, serial correlation, disturbance distribution, autocorrelation