# Fall 2020 - ECON 835 G100

## Overview

• #### Course Times + Location:

Mo 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby

We 3:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby

• #### Instructor:

Kevin Schnepel
kschnepe@sfu.ca
1 778 782-3795
• #### Prerequisites:

ECON 435 and ECON 798.

## Description

#### CALENDAR DESCRIPTION:

An introduction to econometric theory. Applications of econometric methods to both time series and cross-section data. Offered once a year.

#### COURSE DETAILS:

Prerequisites: This course assumes that students enter with a basic knowledge of calculus, matrix algebra, probability and statistics.  Although part of the course is devoted to teaching students how to use statistical software, students are expected to be proficient at operating a PC.  If you feel you may be deficient in any of these areas, you should contact me as soon as possible.

Description:  This is an introductory graduate level course in econometrics.  This course is designed to introduce students to the fundamental tools of econometrics. The primary goal of the course is to provide students an in-depth understanding of the classical linear regression model (CLRM) and the key identification assumptions.  Upon completing the course, successful students will be able to formulate econometric models, manage data and estimate regressions, and interpret results (sign, significance, and magnitude).  Successful students will understand finite and asymptotic properties of commonly used estimators, hypotheses testing, identification, estimation, accurate inference, linear, instrumental variables, logit/probit, and maximum likelihood regressions.

Topics (not necessarily in order presented in class):

1. The Classical Linear Regression Model
1. Classical Assumptions
2. Finite Sample Properties
3. Hypothesis Testing
4. Generalized Least Squares
5. Maximum Likelihood
6. Large Sample Properties
7. Hypothesis Testing – Large Sample Results
1. Violations of the Classical Assumptions
1. Heteroskedasticity / Serial Correlation
2. Omitted Variable Bias
3. Instrumental Variables
4. Logit and Probit

• Assignments 70%
• Lecture participation 5%
• Final exam 25%

#### NOTES:

Grades will be available via Canvas. Assignments will be due approximately every other week. Late assignments will not be accepted. Attendance at lecture is mandatory.

## Materials

We will rely on selected chapters from Bruce Hansen’s econometrics notes available for free online: https://www.ssc.wisc.edu/~bhansen/econometrics/.  I will post weekly lecture notes as well as other resources relevant to each lecture.

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.