Time Series Econometrics is aimed at students who wish to gain a working knowledge of time series and forecasting methods as applied in economics, social science, and finance. The course introduces the theory and practice of time series analysis, with an emphasis on practical skills. Students will gain an appreciation for the role of dependence in statistical modeling.
The course name says it all: introduction to econometrics taught in italian.
L’obiettivo del corso è di introdurre gli studenti all’econometria usando un approccio prevalentemente applicato. Sebbene siano trattati sia gli aspetti teorici che quelli pratici, l’accento è posto sulla comprensione intuitiva con i concetti chiave delle tecniche econometriche illustrati con applicazioni empiriche aventi ad oggetto questioni economiche rilevanti nel dibattito di policy.
This course gives a thorough coverage of classical econometric theory with an emphasis on the estimation of causal relations between economic variables. Applications in the areas of microeconomics and macroeconomics will be considered.
This is a course on the econometric techniques used in the estimation of dynamic macroeconomic models (DSGE models). The aim of the course is mostly theoretical, but applications are also presented using Julia.
The course main objective is to provide students with a solid grasp of the probabilistic and statistical underpinnings of the most common econometric techniques. The topics studied will be of practical use to any student who plans on confronting data in their thesis or wishes to read and precisely understand the econometrics typically used in empirical research published in academic journals. The recommended software package is Julia.
The aim of this course is to provide an introduction to the practice of econometrics. While both theoretical and practical aspects will be covered, emphasis will be on intuitive understanding. Concepts will be illustrated with real world applications on real world data.
Econometric Review Course is a 20 hours long course. I am teaching half of the course; the other half is taught by Marco Lippi.
In my part I will focus on the specification and estimation of the linear regression model. The course departs from the standard Gauss-Markov assumptions to include heteroskedasticity, serial correlation, and errors in variables. Advanced topics include generalized least squares, instrumental variables, generalized method of moments, limited dependent variable models, and panel data.
This course gives an overview of many techniques, and algorithms in machine learning, beginning with topics such as linear regression and classification and ending up with more recent topics such as boosting, support vector machines, random forests and and unsupervised learning techniques. The course will give the student the ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The module will use primarily the R programming language.
Some of the classes I have offered in recent years, including pointers to upcoming courses and other resources, when they are available. Classes are listed once, but are --- or have been --- typically taught more frequently than that. Not all courses offered at the Luiss Business School are listed here.
To receive updates from this site, you can subscribe to the RSS feed of all updates to the site in an RSS feed reader