Fall 2022 - ECON 333 D100

Statistical Analysis of Economic Data (4)

Class Number: 3864

Delivery Method: In Person

Overview

  • Course Times + Location:

    We 3:30 PM – 4:20 PM
    SSCC 9002, Burnaby

    Fr 2:30 PM – 4:20 PM
    AQ 3182, Burnaby

  • Prerequisites:

    ECON 103 with a minimum grade of C- or ECON 113 with a minimum grade of A-; ECON 105 with a minimum grade of C- or ECON 115 with a minimum grade of A-; ECON 233 or BUS (or BUEC) 232 or STAT 270, MATH 157, all with a minimum grade of C-; 60 units. Students with a minimum grade of A- in ECON 233, BUS (or BUEC) 232 or STAT 270 can take ECON 333 after 30 units. Students seeking permission to enroll based on their ECON 233, 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:

An introduction to the use of statistical methods in the context of economic applications using typical economic data.

Topics: This course will introduce you to the statistical analysis of economic data: econometrics. We will focus on linear regression, which is by far the most common method for analyzing the relationship between two or more variables. The main goal of this course is to learn how to apply regression methods to economic data and how to interpret the results of econometric analysis.

There will be regular graded assignments consisting of both exam-type questions and computer exercises. You are expected to work independently on the assignments. Cheating of any kind will result in at least a failing grade in the course.
The computer exercises will get you doing some real econometric analysis using the R software package. R is installed on the lab computers; details on lab hours and procedures will be announced in the first week of class. R is open source, so you can freely download a copy for your own use here: https://www.r-project.org.
I encourage you familiarize yourself with R before the start of the semester. You’ll find a good introduction here: https://www.r-econometrics.com/rbasicsintro/ There will be a
course website on canvas. It is your responsibility to check it regularly. This is where assignments, readings, etc. will be posted. I encourage you to prepare for ECON333 by reviewing material from your introductory statistics course. Concentrate your review on probability distributions (especially sampling distributions) and hypothesis testing.
Course Schedule:
Week 1: Intro and basic statistics
Week 2: Probability Theory 1
Week 3: Probability Theory 2
Week 4: Randomized controlled trials (RCTs)
Week 5: Linear regression with single regressor (SLR)
Week 6: Inference on SLR
Week 7: Midterm exam (no lecture)
Week 8: Multiple linear regression (MLR)
Week 9: Inference on MLR
Week 10: Nonlinear regression
Week 11: Validity of regression analysis
Week 12: Instrumental Variable regression
Week 13: Binary dependent variable

Grading

  • Participation 5%
  • Assignment 20%
  • Midterm 30%
  • Final Exam 45%

Materials

REQUIRED READING:

Stock and Watson, “Introduction to Econometrics”, 4rd edition

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.

Final exam schedules will be released during the second month of classes. If your course has a final exam, please ensure that you are available during the entire final exam period until you receive confirmation of your exam dates. 

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