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.
Prerequisites
Data Analysis and Descriptive Statistics, Probability and Inference. ANOVA
Program
Linear Regression Model, both simple and with multiple regressors (10 hours)
Assumptions and Diagnosis (8 hours)
Inference (4 hours)
Internal and External Validity (2 hours)
Regression Analysis of Economic Time Series Data (8 hours)
Dynamic Causal Effects (4 hours)
Assumptions and Diagnosis (8 hours)
Inference (4 hours)
Internal and External Validity (2 hours)
Regression Analysis of Economic Time Series Data (8 hours)
Dynamic Causal Effects (4 hours)
Books
James H. Stock and Mark W. Watson (fifth edition), Introduction to Econometrics
Bibliography
James H. Stock and Mark W. Watson (fifth edition), Introduction to Econometrics
Teaching methods
During the whole duration of the course (6 weeks), there will be 3 weekly classes of 2 hours each, and 1 practice of 2 hours.
In all appointments, an active participation to the class will be strongly encouraged.
In all appointments, an active participation to the class will be strongly encouraged.
Exam Rules
The final written exam is a closed-book exam, consisting of both theoretical and empirical questions covering the entire program of the course. Questions can be both open and multiple choice and can feature graphs and estimation output, with the aim to evaluate the ability of the student to interpret the final results of a rea-world dataset.
Final evaluation ranges between 18 and 30. Scores lower than 18 will be recorded as Fail.
Students must book through the DELPHI website to take the written exam. Students who are not registered will not be admitted.
Final evaluation ranges between 18 and 30. Scores lower than 18 will be recorded as Fail.
Students must book through the DELPHI website to take the written exam. Students who are not registered will not be admitted.