Programme

Part I: Introduction to data analysis and exploratory tecniques
- Data frames
- Cluster analysis
- Principal component analysis

Part 2: Normal linear regression and its generalizations
- Polynomial regression
- Multiple regression
- Logistic and multinomial regression
- Beta regression
- Poisson and negative binomial regression

Part 3: Panel data analysis
- Balanced and unbalanced panel, micro and macro panel
- Modeling the level of the dependent variable
- Modeling change of the dependent variable
- Fixed effects and random effects models for categorical variables and continuous variables

Core Documentation

Chatterjee, S. and Hadi, A.S. (2012), Regression Analysis by Example, 5th Edition, Wiley. Chapters: 1, 2, 3 (excluding 3.9), 4 (excluding 4.3, 4.9.2, 4.9.3, 4.10, 4.12, 4.13, 4.14), 5 (excluding 5.6 and 5.7), 6 (excluding 6.6 and 6.7), 9, 11, 12 (excluding 12.8.3 and 12.8.4), 13 (excluding 13.5, 13.6, 13.7).

Fox,J. and Weisberg, S. (2010), An R companion to applied regression, 2nd Edition, SAGE publications
Inc.

Andreb, H-J, Golsch, K., Schmidt, A.W. (2013), Applied panel data analysis for economic and social
surveys, Springer. Chapters: 1, 2, 3, 4

Type of delivery of the course

In the academic year 2018/2019 the students have been invited to attend the lectures for the course Statistical methods in economics and to contact the lecturer for any explanation. The teaching material on the web page of the course was in italian.

Attendance

In the academic year 2018/2019 the students have been invited to attend the lectures for the course Statistical methods in economics and to contact the lecturer for any explanation. The teaching material on the web page of the course was in italian.

Type of evaluation

The course assessment will be based on a written exam held in the computer lab, that will involve the analysis of different data sets using the methods and models studied during the course. Attending students will be allowed to develop and discuss a short dissertation before the end of the course, and will be exempt from a part of the written exam.

Programme

Part I: Introduction to data analysis and exploratory tecniques
- Data frames
- Cluster analysis
- Principal component analysis

Part 2: Normal linear regression and its generalizations
- Polynomial regression
- Multiple regression
- Logistic and multinomial regression
- Beta regression
- Poisson and negative binomial regression

Part 3: Panel data analysis
- Balanced and unbalanced panel, micro and macro panel
- Modeling the level of the dependent variable
- Modeling change of the dependent variable
- Fixed effects and random effects models for categorical variables and continuous variables

Core Documentation

Chatterjee, S. and Hadi, A.S. (2012), Regression Analysis by Example, 5th Edition, Wiley. Chapters: 1, 2, 3 (excluding 3.9), 4 (excluding 4.3, 4.9.2, 4.9.3, 4.10, 4.12, 4.13, 4.14), 5 (excluding 5.6 and 5.7), 6 (excluding 6.6 and 6.7), 9, 11, 12 (excluding 12.8.3 and 12.8.4), 13 (excluding 13.5, 13.6, 13.7).

Fox,J. and Weisberg, S. (2010), An R companion to applied regression, 2nd Edition, SAGE publications
Inc.

Andreb, H-J, Golsch, K., Schmidt, A.W. (2013), Applied panel data analysis for economic and social
surveys, Springer. Chapters: 1, 2, 3, 4

Type of delivery of the course

In the academic year 2018/2019 the students have been invited to attend the lectures for the course Statistical methods in economics and to contact the lecturer for any explanation. The teaching material on the web page of the course was in italian.

Attendance

In the academic year 2018/2019 the students have been invited to attend the lectures for the course Statistical methods in economics and to contact the lecturer for any explanation. The teaching material on the web page of the course was in italian.

Type of evaluation

The course assessment will be based on a written exam held in the computer lab, that will involve the analysis of different data sets using the methods and models studied during the course. Attending students will be allowed to develop and discuss a short dissertation before the end of the course, and will be exempt from a part of the written exam.

Programme

Part I: Introduction to data analysis and exploratory tecniques
- Data frames
- Cluster analysis
- Principal component analysis

Part 2: Normal linear regression and its generalizations
- Polynomial regression
- Multiple regression
- Logistic and multinomial regression
- Beta regression
- Poisson and negative binomial regression

Part 3: Panel data analysis
- Balanced and unbalanced panel, micro and macro panel
- Modeling the level of the dependent variable
- Modeling change of the dependent variable
- Fixed effects and random effects models for categorical variables and continuous variables

Core Documentation

Chatterjee, S. and Hadi, A.S. (2012), Regression Analysis by Example, 5th Edition, Wiley. Chapters: 1, 2, 3 (excluding 3.9), 4 (excluding 4.3, 4.9.2, 4.9.3, 4.10, 4.12, 4.13, 4.14), 5 (excluding 5.6 and 5.7), 6 (excluding 6.6 and 6.7), 9, 11, 12 (excluding 12.8.3 and 12.8.4), 13 (excluding 13.5, 13.6, 13.7).

Fox,J. and Weisberg, S. (2010), An R companion to applied regression, 2nd Edition, SAGE publications
Inc.

Andreb, H-J, Golsch, K., Schmidt, A.W. (2013), Applied panel data analysis for economic and social
surveys, Springer. Chapters: 1, 2, 3, 4

Type of delivery of the course

In the academic year 2018/2019 the students have been invited to attend the lectures for the course Statistical methods in economics and to contact the lecturer for any explanation. The teaching material on the web page of the course was in italian.

Attendance

In the academic year 2018/2019 the students have been invited to attend the lectures for the course Statistical methods in economics and to contact the lecturer for any explanation. The teaching material on the web page of the course was in italian.

Type of evaluation

The course assessment will be based on a written exam held in the computer lab, that will involve the analysis of different data sets using the methods and models studied during the course. Attending students will be allowed to develop and discuss a short dissertation before the end of the course, and will be exempt from a part of the written exam.