66056 - Laboratory of Economic Statistics and Market Analysis

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Industry, innovation and infrastructure

Academic Year 2021/2022

Learning outcomes

This course will present multivariate statistical methods in several ways:

1) It offers an overview of the main multivariate methods in economics and business.

2) It helps in choosing the best statistical method for different economic data sets.

3) A brief explanation of some advanced methods, like spatial classification and clusterization, are also included.

Course contents

1. Data and Introduction to Multivariate Statistics

1.1 Data and main problems

1.2 Aims of Multivariate Statistics

2. Multivariate Normal Distriutions

2.1 Inference on Means

2.2 Inference on Covariances

3. Multivariate Regression Analysis

3.1 Univariate Regressions: a reference

3.2 Residual Analysis and Specification Tests

3.3 Multivariate Analysis and Principal Component Analysis on residuals

4. Discrimination and Classification

4.1 Nonlinear Models

4.2 Multinomial Logistic Regression

4.3 Spatial Probit Model

5. Clustering

5.1 Regression Trees

5.2 Hierarchical Clustering

5.3 K-means Clustering

5.4 Spatial Clustering

6. Time series

6.1 Time series components

6.2 Time series decomposition

6.3 Estimation of time series components

6.4 Simple forecasting methods





The following references are recommended:

Daniel Zelterman (2014), Applied Multivariate Statistics with
R, Springer.

Marno Verbeek (2005), Econometria, I edizione, Zanichelli

William Greene (2019), Econometric Analysis, Pearson. Eighth
Edition (Global Edition).


Teaching methods

Lessons are carried out considering both methodological and empirical aspects in economics, with the help of the statistical software R.

The used economic datasets are all available in R.

Assessment methods

Oral examination.

The examination consists into the evaluation of a work group.

The students are divided into groups and they will prepare a short thesis explaining the type of dataset used, the statistical method/s and the results with the use of R.

Each group will also prepare a short presentation/seminar during which some questions are made.

Teaching tools

Pc; videoprojector; computer laboratory

Office hours

See the website of Anna Gloria Billè