QUANTITATIVE METHODS III
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES:
The course provides an introduction to the tools used in Applied Microeconometrics to study causal inference. It has a practical flavor, emphasis is not on proofs but on intuitions
and on applications. The course covers linear regression models, identification based on observables, randomized control trials, difference-in-differences, instrumental variables and (time permitting) regression discontinuity design. We will cover the conceptual basis of the methods in an intuitive way, and you will learn how to apply and code such methods using the software Stata. Coding in Stata will be an important component of the module. The aim is to provide students with the skills to perform data preparation and econometric analyses.
KNOWLEDGE AND UNDERSTANDING:
Knowledge of data types and fundamentals of coding on Stata, linear regression models, causal inference techniques.
APPLYING KNOWLEDGE AND UNDERSTANDING:
Ability in selecting appropriate data analysis methods, and in analysing causal relationships among variables in economics.
MAKING JUDGMENTS:
This course empowers students with the analytical skills to rigorously analyse and interpret data, enabling them to discern between correlation and causation. By applying these skills to real-world scenarios, students are equipped to critically assess causal claims in diverse contexts, enhancing their ability to make informed, independent judgments.
COMMUNICATION SKILLS:
Ability to spot and present the most suitable empirical framework for the analysis based on the nature of the data at hand and effective communication of data analysis results, also by means of graphs and tables.
LEARNING SKILLS:
Ability to learn autonomously further data analysis techniques, in professional activities or subsequent studies, achieved through the analysis of econometric methods applied in
economics, finance and management.
The course provides an introduction to the tools used in Applied Microeconometrics to study causal inference. It has a practical flavor, emphasis is not on proofs but on intuitions
and on applications. The course covers linear regression models, identification based on observables, randomized control trials, difference-in-differences, instrumental variables and (time permitting) regression discontinuity design. We will cover the conceptual basis of the methods in an intuitive way, and you will learn how to apply and code such methods using the software Stata. Coding in Stata will be an important component of the module. The aim is to provide students with the skills to perform data preparation and econometric analyses.
KNOWLEDGE AND UNDERSTANDING:
Knowledge of data types and fundamentals of coding on Stata, linear regression models, causal inference techniques.
APPLYING KNOWLEDGE AND UNDERSTANDING:
Ability in selecting appropriate data analysis methods, and in analysing causal relationships among variables in economics.
MAKING JUDGMENTS:
This course empowers students with the analytical skills to rigorously analyse and interpret data, enabling them to discern between correlation and causation. By applying these skills to real-world scenarios, students are equipped to critically assess causal claims in diverse contexts, enhancing their ability to make informed, independent judgments.
COMMUNICATION SKILLS:
Ability to spot and present the most suitable empirical framework for the analysis based on the nature of the data at hand and effective communication of data analysis results, also by means of graphs and tables.
LEARNING SKILLS:
Ability to learn autonomously further data analysis techniques, in professional activities or subsequent studies, achieved through the analysis of econometric methods applied in
economics, finance and management.
ELISA FACCHETTI
Prerequisites
Attending students must have passed Quantitative Methods I and Quantitative Methods II from the B.D. in Business Administration and Economics, or similar courses. A good understanding of basic statistics, probability, statistical inference and the multiple linear regression models is mandatory.
Program
Linear regression model: bivariate and multivariate regressions
Omitted variable bias, partial-out interpretation of the coefficients
Heteroskedasticity and autocorrelation
Hypothesis testing
Fixed effects model
Causal inference and potential outcome framework
Causal inference techniques: Randomized Control Trials (RCT), Difference-in-differences
(DiD), instrumental variable (IV), Regression Discontinuity Design (RDD)
Omitted variable bias, partial-out interpretation of the coefficients
Heteroskedasticity and autocorrelation
Hypothesis testing
Fixed effects model
Causal inference and potential outcome framework
Causal inference techniques: Randomized Control Trials (RCT), Difference-in-differences
(DiD), instrumental variable (IV), Regression Discontinuity Design (RDD)
Books
The module material (slides, datasets, exercises) will be uploaded on the course webpage.
We will not follow closely any particular textbook, but the following books are highly recommended since they provide an introductory exposition of the topics we will cover:
- Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
- Cunningham, S. (2021) Causal Inference: The Mixtape, Yale University Press. The entire book is available online at https://mixtape.scunning.com/index.html.
- Wooldridge, J. (2019) Introductory Econometrics A Modern Approach, 7th ed. South-Western College Publishing
We will not follow closely any particular textbook, but the following books are highly recommended since they provide an introductory exposition of the topics we will cover:
- Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
- Cunningham, S. (2021) Causal Inference: The Mixtape, Yale University Press. The entire book is available online at https://mixtape.scunning.com/index.html.
- Wooldridge, J. (2019) Introductory Econometrics A Modern Approach, 7th ed. South-Western College Publishing
Teaching methods
During the whole duration of the course (6 weeks), there will be 3 weekly classes of 2 hours each. In all appointments, an active participation to the class will be strongly encouraged.
Exam Rules
The evaluation for this course comprises a final written examination along with two compulsory Stata problem sets, which, if successfully completed, constitute 25% of the overall mark.
Only submissions that meet the pass criteria will count towards the grade, ensuring that students actively participate and engage with the material. Failure to pass these sets results in the final exam counting for 100% of the entire grade.
The final exam, conducted without access to course materials, assesses both theoretical knowledge and empirical skills through a variety of question types, including open-ended, multiple-choice, and data interpretation tasks involving graphs and outputs. Grades span from 18 to 30, with any score below 18 will be recorded a Fail.
Only submissions that meet the pass criteria will count towards the grade, ensuring that students actively participate and engage with the material. Failure to pass these sets results in the final exam counting for 100% of the entire grade.
The final exam, conducted without access to course materials, assesses both theoretical knowledge and empirical skills through a variety of question types, including open-ended, multiple-choice, and data interpretation tasks involving graphs and outputs. Grades span from 18 to 30, with any score below 18 will be recorded a Fail.