Instrumental Variables

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Date/Time
Date(s) - 07/28/2016 - 07/29/2016
All Day

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Start Date: 7/28/2016

End Date: 7/29/2016

Location:

Berlin Marriott Hotel

Inges-Beisheim-Platz 1

Berlin, Germany

Website: http://statisticalhorizons.com/seminars/public-seminars/iv-spring-2016-berlin

Contact: 610-642-1941

Taught by Dr. Felix Elwert, Ph.D.

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.

This seminar will take students from the basic Wald estimator up to powerful recent developments, including non-parametric tests of the exclusion assumption. Students 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 students 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 students to recognize IVs in their 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, continuous and categorical outcomes, 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.

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