The aim of the course is to provide students with a coverage of a set of methods used in the analysis of economic data to answer a variety of specific, quantitative questions and with the computational tools to be used in the empirical applications. The program includes further topics on the classical regression model, time series analysis and some recent proposal to deal with applications having many observations and/or many predictors relative to the number of observations.
The course applies the widely used freeware programming environment for statistical analysis, known as R (through the RStudio interface).
The course applies the widely used freeware programming environment for statistical analysis, known as R (through the RStudio interface).
teacher profile teaching materials
Introduction to time series analysis and forecasting. Stationarity and unit roots.
Models for univariate time series: ARMA, ARIMA, ARCH, and GARCH.
Regression models for time-series data.
Multivariate time series: cointegration and error correction models; VAR models.
Panel data: fixed-effects and random-effects models; dynamic panel data models.
Use of R and the RStudio environment to estimate and apply the models to real-world data.
The detailed syllabus and additional teaching materials will be made available on the course Moodle page.
Lecture notes prepared by the instructor will be made available on the course Moodle page.
Mutuazione: 21210418 Advanced topics in Statistical learning in Economia dell'ambiente, lavoro e sviluppo sostenibile LM-56 R BARBIERI MARIA MADDALENA
Programme
A review of the multiple linear regression model.Introduction to time series analysis and forecasting. Stationarity and unit roots.
Models for univariate time series: ARMA, ARIMA, ARCH, and GARCH.
Regression models for time-series data.
Multivariate time series: cointegration and error correction models; VAR models.
Panel data: fixed-effects and random-effects models; dynamic panel data models.
Use of R and the RStudio environment to estimate and apply the models to real-world data.
The detailed syllabus and additional teaching materials will be made available on the course Moodle page.
Core Documentation
Verbeek M., A guide to modern econometrics, 2004, Wiley.Lecture notes prepared by the instructor will be made available on the course Moodle page.
Type of delivery of the course
Whole class teaching.Attendance
Attendance is recommended but not compulsory.Type of evaluation
The final grade is determined on the basis of the following assessment components: • assignments submitted via Moodle during the teaching period: 20% of the final grade; • written examination: 80% of the final grade. The final grade is expressed on a 30-point scale. A grade of at least 18/30 is required to pass the examination. Students who do not submit the assignments during the teaching period may replace this assessment component with a project involving the analysis of a dataset of their own choice and an oral examination. The project and the oral examination assess the same knowledge and skills as the assignments and jointly account for 20% of the final grade. The project will be discussed and assessed during the oral examination. The assignments may include multiple-choice questions, open-ended questions, and data-analysis exercises to be completed using R. The written examination lasts no more than two hours. Students are not permitted to consult books or notes during the examination. It consists of open-ended questions covering the entire course syllabus and may also require students to interpret and discuss output produced using R. Two midterm examinations, each covering a different part of the syllabus, will be held during the teaching period. Taking both midterm examinations replaces the written examination. The grade for this assessment component is calculated by assigning a weight of 30% to the first midterm examination and 70% to the second. Students who do not take both midterm examinations must sit the written examination covering the entire course syllabus. The assessment procedures, deadlines, and grading criteria will be explained at the beginning of the semester and published in a dedicated document available among the course materials on Moodle.