Methods of Economic Investigation (Ec402)

This part of the course will build on what you learned in the first term but with a heavier emphasis on the empirical applications of the techniques. The goal is to build a strong link between the theoretical foundations and practical use of many of the techniques that are commonly used by economists. In addition, the course is structured to allow you to become critical consumers of empirical research by investigating the practical limitations of many techniques. The course is set up with 3 1-hour lectures each week, on Monday, Tuesday, and Thursday. On Monday and Tuesday, we will cover the theoretic foundations of particular techniques. Each Thursday we will explore a specific application that has policy or economic significance.

Lectures Slides & Exercises Below are the slides from the lectures for each week. There are 3 per week: two theory lectures and one application. Notes and handouts for class will be posted the night before lectures.   Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10   Exercises will be posted towards the end of each week. If there are additional handouts for lectures, the lectures will be marked with a (*) and handouts are available on the handout page.   Week 1: Causal Inference Data Structures Application: Impact of Earnings on Health Exercise 1 Exercise 1 Solutions   Week 2: Experimental Evaluations Non-Experimental Evaluations Application 2: The Effect of Arrest on Domestic Violence Exercise 2 (Data) Exercise 2 Solutions   Week 3: Omitted Variable Bias Error Component Models Application 3: Estimating the Returns to Schooling Exercise 3 Exercise 3 Solutions Week 4: Difference-in-Differences Methods Matching Methods Matching Methods 2 Exercise 4 (data) Exercise 4 Solutions Week 5: Instrumental variables: Introduction Instrumental variables: Heterogeneous Treatment Effects Exercise 5 Exercise 5 Solutions Week 6: Instrumental variables: Weak Instruments Review of Causal Effects   Week 7: Maximum Likelihood Estimation Week 8: Univariate Time Series Processes Stationary Time Series and Model Selection Exercise 6 (data) Exercise 6 Solutions Week 9: Nonstationary Time Series & Forecasting Regression Discontinuity /Event Studies Exercise 7 Exercise 7 Solutions Week 10: Multivariate Time Series Vector Auto-Regressions Exercise 8 Exercise 8 Solutions ^