INTERMEDIATE ECONOMETRICS

Course objectives

General goals The aim of the course is to provide students with knowledge of advanced topics in econometrics. In the frontal lessons, the exposition of theoretical topics will be integrated with applications to real case studies and numerical estimates, in order to make consistent theory and applications. In the first part of the course alternative models and estimators to the classical regression model will be presented, in order to provide the students with appropriate tools to deal with problem of non-spherical errors, endogeneity, binary data, maximum likely estimations and panel data. In the second part of the course will focus on empirical applications of the topics taught so far in context of causality, probability models and/or time series Specific goals Knowledge and understanding: students will be able to understand the different estimation problems one comes across in time series, becoming able to identify in what context each method applies and how the results may be used to inform health policy and health care decision-making. Applying knowledge and understanding: students will be able to apply econometric methods for time series on modern econometric software. Making judgement: students will be able to critically evaluate strengths and weaknesses of time series analysis as a research and health policy tool. Communication skills: students will be able to effectively communicate the results of their own analysis in simple and effective ways. Learning skills: students will develop the necessary skills and abilities to independently identify and formulate empirical research questions.

Channel 1
MARCO VENTURA Lecturers' profile

Program - Frequency - Exams

Course program
elements of matrix algebra, the classical multiple linear regression model, the OLS estimator and its properties maximum likelihood estimation, discrete data models: binary, ordina, multinomial and count data Machine learning: penalized OLS, trees, trees ensamble, predictive inference, causality in high dimension Applications in R
Prerequisites
Knowledge of linear algebra, derivatives, descriptive statistics and inference
Books
Verbeek M. A Guide to Modern Econometrics. Wiley Custom, 5th Eds. Ch 7. Cerulli G. Fundamentals of Supervised Machine Learning With Applications in Python, R, and Stata. Spriinger 2023 ISSN 1431-8784 ISSN 2197-1706 (electronic) Additional tetching material will be delivered during the course
Frequency
Strongly suggested
Exam mode
written examination in classroom
Lesson mode
The course will be held in classroom by means of traditional frontal lessons
  • Lesson code10606556
  • Academic year2025/2026
  • CourseEconomics and Finance
  • CurriculumEconomia e finanza
  • Year3rd year
  • Semester1st semester
  • SSDSECS-P/05
  • CFU6