Applied econometrics

Course objectives

Learning goals The primary learning goal of this course is that of exposing students to the body of econometric techniques that are customised to economics applications. The aim of the course is to review this body of techniques, to demonstrate their use in hands-on style, drawing on as wide a range of example as possible, and to interpret each set of results in ways that are most useful to read and represent economic phenomena. Knowledge and understanding. The course is supposed to broaden students' knowledge of the various econometric techniques that appear in the economics literature, their properties and the way these are applied to data in order to verify economic theory. Applying knowledge and understanding. Upon successful completion of the course, students will be able to carry out a wide range of tasks in empirical economics, such as recognising the most suitable approaches to analyse the data at hand in order to capture and model its regularities, and intelligibly convey its messages to both economists and broader audiences. Making judgements. The course develops in a way to spurs students on researching empirical evidence of competing economic theories by respecting the nature of convenient data. Communication skills. Through study and hands-on sessions, students will acquire the terminology characterising the discipline, which they are required to use in both written and oral dissemination. Learning skills. Students who complete the course successfully will be acquainted with a method of analysis enabling them to endeavour the main economic issues from an empirical point of view.

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ANDREA MERCATANTI Lecturers' profile

Program - Frequency - Exams

Course program
APPLIED ECONOMETRICS program: Introduction to the potential outcome approach. Neyman's mode of inference. Outcome regression. Propensity score. Propensity score weighting. Doubly robust estimation. Instrumental variables. Causal methods for panel data: difference-in-differences. Regression discontinuity designs.
Books
- Angrist, J. D. & Pischke, J.-S.: Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton Univ. Press, last edition). - P. Ding: A First Course in Causal Inference (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition, 2024.
Exam mode
A written exam is planned to assess both the acquisition of theoretical concepts and the ability to solve practical problems.
ANDREA MERCATANTI Lecturers' profile

Program - Frequency - Exams

Course program
APPLIED ECONOMETRICS program: Introduction to the potential outcome approach. Neyman's mode of inference. Outcome regression. Propensity score. Propensity score weighting. Doubly robust estimation. Instrumental variables. Causal methods for panel data: difference-in-differences. Regression discontinuity designs.
Books
- Angrist, J. D. & Pischke, J.-S.: Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton Univ. Press, last edition). - P. Ding: A First Course in Causal Inference (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition, 2024.
Exam mode
A written exam is planned to assess both the acquisition of theoretical concepts and the ability to solve practical problems.
  • Lesson code10611848
  • Academic year2024/2025
  • CourseStatistical Methods and Applications
  • CurriculumOfficial Statistics (percorso valido anche ai fini del conseguimento del doppio titolo italo-francese)
  • Year1st year
  • Semester2nd semester
  • SSDSECS-P/05
  • CFU6
  • Subject areaStatistico applicato