  Area Riservata

accesso rapido

Statistical methods in economics (ex metodi statistici applicati all'economia)

lauree magistrali

The main objective of the course is to provide the fundamental tools for the application of statistical methods to the analysis of economic data. The theoretical part will be supported by an applied part devoted to the analysis of real data sets by means of the software R. One lecture per week will be held in the computer lab. A student that has completed the course should be practiced in the application of advanced statistical methods, should be able to interpret the results of a statistical analysis, and should be aware of limitations and possible sources of errors in the analysis.

Final assessment: 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.

Il corso si svolge nel I semestre (dal 16/9/2019 al 14/12/2019)

Docenti: Caterina Conigliani (docente titolare)

Crediti: 9

Numero moduli: 1

Date di esame

Programma

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

[ ultimo aggiornamento 6/9/2017 ]

Testi

Teaching material will be available to students in a shared Dropbox folder.

Text books:
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

Vai alla versione stampabile del corso

oppure crea PDF

Materiale del corso

(cliccare con il tasto destro del mouse e selezionare "Salva oggetto...")

• 1a) Statistical inference: a quick review
• 1b) Lab 1: an introduction to R - part 1
• 1c) Lab 2: an introduction to R - part 2
• 2a) Cluster analysis
• 2b) Principal components analysis
• 2c) Lab 3: Cluster analysis with R
• 2d) Lab 4: PCA with R
• 3a) Simple linear regression
• 3b) Lab 5: Simple linear regression with R
• 3c) Multiple regression
• 3d) Non linear regression
• 3e) Lab 6: multiple and nonlinear regression with R
• 3f) Lab 7: about outliers and influential observations
• 3g) Generalizing normal regression models
• 3h) Lab 8: PCA, normal and logistic regression
• 4) Supervised classification
• 5) The likelihood function
• 6a) Panel data: models for a continuous response
• 6b) Panel data: models for a dichotomous and a discrete response
• 6c) Lab 9: Models for panel data
• 7) Sample exam paper

I materiali non cliccabili sono riservati ai soli utenti accreditati.
Per ulteriori informazioni clicca qui.

© Scuola di Economia, Via Silvio D'Amico 77, 00145 Rome, Italy