Spring 2020 - ECON 838 G100

Econometrics II (4)

Class Number: 4417

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 6 – Apr 9, 2020: Tue, 8:30–10:20 a.m.
    Burnaby

    Jan 6 – Apr 9, 2020: Thu, 8:30–9:20 a.m.
    Burnaby

  • Prerequisites:

    ECON 837.

Description

CALENDAR DESCRIPTION:

Develops the core tools of theoretical and applied econometrics including time series, cross sectional, and panel data methods. Topics may include limited dependent variable models, GMM, instrumental variables, ARMA models, unit roots and cointegration, fixed and random effects, incidental parameters, testing, program evaluation, nonlinear regression, semi- and nonparametric methods.

COURSE DETAILS:

Develops the core tools of theoretical and applied econometrics including limited dependent variable models, program evaluation, nonparametric and semiparametric estimation, panel data methods and big data/machine learning methods.

Topics: Covered topics will include:  

  1. Limited dependent variable models (probit, logit, tobit and selection models)
  2. Program evaluation (RCT, natural experiments, matching, instrumental variable models, regression discontinuity design, and control function methods)
  3. Nonparametric and semiparametric estimation (kernel density estimator, conditional mean function estimator, maximum score estimator and more)
  4. Panel data methods (static and dynamic panel models)
  5. Big data/machine learning (LASSO, regression trees and random forest)

Grading

  • Midterm exam 40%
  • Final exam 60%

Materials

REQUIRED READING:

Li and Racine, 2006, “Nonparametric Econometrics: Theory and Practice.” Princeton University Press.
ISBN: 978-0691121611

RECOMMENDED READING:

Angrist and Pischke, 2009, “Mostly Harmless Econometrics”. UCAL.
ISBN: 978-0691120355

James, Witten, Hastie and Tibshirani, 2017, “An Introduction to Statistical Learning”, Springer.
ISBN: 978-1461471370

Graduate Studies Notes:

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.

Registrar Notes:

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

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS