INTRODUCTION TO ECONOMETRICS
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.