This course is a field course in the PhD Program in Economics (PIE), which is jointly offered by the Universities of Innsbruck and Linz. In winter term 2022/23,  Bernhard Schmidpeter and I off this course. The course schedule and the reading list can be found in the syllabus.

Objectives

This course covers microeconometrics concepts and methods used in most recent papers to estimate causal effects in the presence of potentially unobserved confounding factors. Both theoretical and applied work will be discussed. The emphasis is, however, on application rather than on method. Methods will be illustrated by papers drawn from the most recent literature. By the end of the course, students should have a firm grasp of the most recent research designs that allow a convincing estimation of causal effects and be ready to apply methods to their own research questions and data.

Topics

• Recap of causality, Rubin Causal Model (RCM), and established methods (IV, DiD, RDD)

• Introduction

• Statistical power

• Marginal treatment effects (MTE) framework

• DiD estimation with variation in treatment timing (DiD^{vtt}, link)

• Synthetic control method (SCM)

• Regression kink designs (RKD) 

• Bunching (B)

Prerequisites

This course is for graduate students with prior training in microeconometrics, who are ideally working on an empirical dissertation. The methods discussed are also useful in an experimental setting. Students should be familiar with estimators such as IV, DiD and RDD (see Identification in Applied Microeconometrics).

Readings

Reading material is listed in the syllabus. Required readings are indicated by *.

Meetings

The course consists of 12 to 15 meetings (90 minutes each); broken down by two types:

• Lecture: In the lecture-meetings, I will introduce the respective estimation method. The focus will be (as much as possible) on the intuition behind the method rather than on the algebra. Before class, students are expected to read assigned readings from the respective theory-section from the reading list indicated by *.

• Presentations: In the presentation-meetings, we will discuss applied papers drawn from the most recent literature. Each student will pick one paper (either one from the reading list, which is indicated by ¶, or on of their choice) and prepare a presentation of about 30 minutes. The focus of the discussion should be on the identification strategy. Each presentation is followed by a group discussion..

Material

• Recap of causality and Rubin Causal Model: see course of Identification in Applied Microeconometrics

• Introduction: slides

• Statistical power: slides

• Marginal treatment effects framework: slides

• DiD estimation with variation in treatment timing: slides

• Synthetic control method: slides 

• Regression kind design: slides

• Bunching estimators: slides

Course Grading

Students are expected to read assigned readings and attend all classes. Grades for the course will be based on:

• a classroom presentation (50%);

• and a take-home exam (50%).

Further details will be announced later.