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ADVANCED EMPIRICAL METHODS FOR ECONOMISTS
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ADVANCED EMPIRICAL METHODS FOR ECONOMISTS
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Academic year 2021/2022
- Course ID
- SEM0143
- Teaching staff
- Giovanni Mastrobuoni (Lecturer)
Ruben Durante (Lecturer) - Year
- 2nd year
- Teaching period
- Second semester
- Type
- Elective
- Credits/Recognition
- 6
- Course disciplinary sector (SSD)
- SECS-P/01 - economia politica
- Delivery
- Formal authority
- Language
- English
- Attendance
- Optional
- Type of examination
- Written
- Prerequisites
- A good understanding of statistics and econometrics
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Sommario del corso
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Course objectives
This course covers the basics for doing sound empirical work at the postgraduate level. It is particularly aimed at students and researchers who plan on doing applied work for their thesis. The identification issue of causal relationship when analyzing experimental and non-experimental data represents the unifying topic of the course. We will cover the most common approaches to identify causal relationships: conditional independence assumption, matching, instrumental variables, random assignment, regression discontinuity, synthetic control approach, and difference-in-differences, two-way fixed effects models. I will discuss graphical methods to present the main evidence.
Empirical papers will serve as examples, and should give a taste about how to perform a convincing empirical analysis. The ideal experimental setting is often going to serve as a benchmark case. Empirical exercises using STATA with real data are going to be part of the module. Some tasks might involve replicating empirical results of published papers.
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Results of learning outcomes
At the end of the course students will have a broad understanding on how to perform sound empirical analysis.
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Course delivery
The course delivery may in part take place in the computer lab.
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Learning assessment methods
During the course students are are going to be asked to present research papers. There are going to be problem sets, a midterm exam and a final exam.
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Program
We will cover the most common approaches to identify causal relationships: conditional independence assumption, matching, instrumental variables, random assignment, regression discontinuity, synthetic control approach, and difference-in-differences.
Extra topics may include non-parametric methods and text analysis.
Suggested readings and bibliography
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• Angrist and Pischke (2009) Mostly Harmless Econometrics
• Wooldridge (2002) Econometric Analysis of Cross Section and Panel Data
• Cameron and Trivedi (2005), Microeconometrics- Oggetto:
