Summer 2020 - ECON 333 D100

Statistical Analysis of Economic Data (4)

Class Number: 2952

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo 8:30 AM – 10:20 AM
    REMOTE LEARNING, Burnaby

    We 8:30 AM – 9:20 AM
    REMOTE LEARNING, Burnaby

  • Exam Times + Location:

    Aug 12, 2020
    3:30 PM – 6:30 PM
    Location: TBA

  • Prerequisites:

    ECON 103 or 200; ECON 105 or 205; BUS (or BUEC) 232 or STAT 270; MATH 157; 60 units. Students with a minimum grade of A- in BUS (or BUEC) 232 or STAT 270 can take ECON 333 after 30 units. Students seeking permission to enroll based on their BUS (or BUEC) 232 or STAT 270 grade must contact the Undergraduate Advisor in Economics.

Description

CALENDAR DESCRIPTION:

An introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. Students with credit for BUEC 333 may not take this course for further credit. Quantitative.

COURSE DETAILS:

The focus of this course is on linear regression, by far the most common method for analyzing the relationship between variables, applied to economic data. Emphasis will be placed on both the use and interpretation of this technique, and dealing with common problems the econometricians face. Topics covered in this course include: ordinary least squares, the classical regression model, statistical inference, specification, multicollinearity, serial correlation and heteroskedasticity.  

Assignments will be given on a regular basis and will require the use of R, a programming language and free software environment for statistical computing and graphics. We will discuss the software in the beginning of the semester, and information will be posted on CANVAS to assist learning R.  

Course announcements, syllabus, additional notes, recommended practice problems, and assignments will be posted on CANVAS.  

Please note: this course is reserved for students declared in the ECON major only.

Grading

  • Assignments 50%
  • Midterm test 25%
  • Final exam 25%
  • These grading weights are subject to changes to be announced during the first week of classes.

Materials

REQUIRED READING:

A.H. Studenmund. Using Econometrics: A Practical Guide (7th ed.) Pearson, 2017. eBook.
ISBN: 978-0134182988

Department Undergraduate Notes:

Please note that, as per Policy T20.01, the course requirements (and grading scheme) outlined here are subject to change up until the end of the first week of classes.

Students requiring accommodations as a result of a disability must contact the Centre for Accessible Learning (CAL) at 778-782-3112 or caladmin@sfu.ca.

***NO TUTORIALS DURING THE FIRST WEEK OF CLASSES***

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN SUMMER 2020

Please note that all teaching at SFU in summer term 2020 will be conducted through remote methods. Enrollment in this course acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as soon as possible to ensure that they are eligible and that approved accommodations and services are implemented in a timely fashion.