Instrumental Variables - Online Course
A 3-Day Livestream Seminar Taught by
Felix Elwert10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This course offers an in-depth survey of modern instrumental variables (IV) analysis. IV analysis is an important quasi-experimental technique with numerous applications in economics, the social and biomedical sciences, business, marketing, and education.
IVs allow us to get unbiased estimates of causal effects even when there is selection bias, unobserved confounding, or imperfect compliance. The technique applies equally to randomized trials and observational studies. IV analysis is a very powerful tool—as long as the underlying assumptions are met.
Starting April 10, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
This seminar will take you from the basic Wald estimator up to powerful recent developments, including non-parametric tests of the exclusion assumption. You will get extensive hands-on experience by analyzing real world examples across the social sciences. We will carefully dissect key technical and substantive assumptions to empower you to recognize, understand, and empirically test these assumptions in practice.
This seminar puts a premium on a practical understanding. We will capitalize on three complementary perspectives: modern potential-outcomes notation, visually intuitive directed acyclic graphs (DAGs), and the traditional algebraic approach. This will enable you to recognize IVs in your own studies, understand assumptions thoroughly, and read the specialist literatures in different fields.
Topics include single instruments, multiple instruments, weak instruments, first-stage diagnostics, over-identification tests, exclusion tests, two-stage least squares (2SLS), natural experiments, encouragement trials, Mendelian randomization, compliance classes, local average treatment effects (LATE), and Balke-Pearl bounds.
We will use the latest commands in Stata and learn theoretical and practical insights that transfer across software packages.
This seminar will take you from the basic Wald estimator up to powerful recent developments, including non-parametric tests of the exclusion assumption. You will get extensive hands-on experience by analyzing real world examples across the social sciences. We will carefully dissect key technical and substantive assumptions to empower you to recognize, understand, and empirically test these assumptions in practice.
This seminar puts a premium on a practical understanding. We will capitalize on three complementary perspectives: modern potential-outcomes notation, visually intuitive directed acyclic graphs (DAGs), and the traditional algebraic approach. This will enable you to recognize IVs in your own studies, understand assumptions thoroughly, and read the specialist literatures in different fields.
Topics include single instruments, multiple instruments, weak instruments, first-stage diagnostics, over-identification tests, exclusion tests, two-stage least squares (2SLS), natural experiments, encouragement trials, Mendelian randomization, compliance classes, local average treatment effects (LATE), and Balke-Pearl bounds.
We will use the latest commands in Stata and learn theoretical and practical insights that transfer across software packages.
Computing
This is a hands-on course with instructor-led software demonstrations and guided exercises. You should use a computer with a recent version of Stata installed (release 13 or later). You should have basic knowledge of Stata (point-and-click graphical user interface or basic command line operation).
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
This is a hands-on course with instructor-led software demonstrations and guided exercises. You should use a computer with a recent version of Stata installed (release 13 or later). You should have basic knowledge of Stata (point-and-click graphical user interface or basic command line operation).
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
Who should register?
If you want to understand how and when to use instrumental variables analysis in practice, this course is for you. The material is equally applicable to experimental and non-experimental data. You should have a good working knowledge of multiple regression.
This course is appropriate for researchers (in industry or at universities, faculty, doctoral students, and advanced masters students) with a solid applied background in multiple regression analysis. This course does not require calculus. You will be given fully executable do-files, so no prior programming experience is necessary.
If you want to understand how and when to use instrumental variables analysis in practice, this course is for you. The material is equally applicable to experimental and non-experimental data. You should have a good working knowledge of multiple regression.
This course is appropriate for researchers (in industry or at universities, faculty, doctoral students, and advanced masters students) with a solid applied background in multiple regression analysis. This course does not require calculus. You will be given fully executable do-files, so no prior programming experience is necessary.
Seminar outline
Day 1
Motivation and outline
-
-
-
- Preliminaries: linear models
- Graphs and path algebra
Linear IV: Just-identified models
-
-
-
- IV: problem and solution
- Real Example: Angrist’s draft lottery
- Instrumental variables in Stata
Day 2
-
-
-
- Understanding exclusion violations
- Real Example: Angrist & Krueger’s quarter of birth IV
- Important little known facts
- How to find good instruments
Linear instrumental variables: over-identified models
-
-
-
- Overview of estimation approaches: IV, 2SLS, GMM, LIML
- IV and 2SLS
Day 3
-
-
-
- Finding and creating multiple IVs
- GMM: getting standard errors right
- Testing and correcting IV assumptions
- IV for measurement error
Nonlinear IV: effect heterogeneity and LATE
Day 1
Motivation and outline
-
-
-
- Preliminaries: linear models
- Graphs and path algebra
-
-
Linear IV: Just-identified models
-
-
-
- IV: problem and solution
- Real Example: Angrist’s draft lottery
- Instrumental variables in Stata
-
-
Day 2
-
-
-
- Understanding exclusion violations
- Real Example: Angrist & Krueger’s quarter of birth IV
- Important little known facts
- How to find good instruments
-
-
Linear instrumental variables: over-identified models
-
-
-
- Overview of estimation approaches: IV, 2SLS, GMM, LIML
- IV and 2SLS
-
-
Day 3
-
-
-
- Finding and creating multiple IVs
- GMM: getting standard errors right
- Testing and correcting IV assumptions
- IV for measurement error
-
-
Nonlinear IV: effect heterogeneity and LATE
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.