STATISTICAL METHODS FOR ECONOMIC ANALYSIS

Single discipline educational activity

Course Sheet

Course code:

000175L

Academic Year of enrolment:

2020

Year of supply:

2021/2022

Type of Course:

Characterising

Degree:

Course of 2nd Cycle Degree in ECONOMICS AND COMMERCE

Disciplinary Sector:

Statistics for Economics

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

BENEDETTI ROBERTO

Cycle:

Second semester

Hours of classroom activity:

Individual study hours:

153

Prerequisites:

The knowledge of Basic Data Science Techniques is requested.

Objectives

The training aim for the student is to reach the following learning outcomes:

Knowledge and ability to understand

The course aims to provide methodological and application advances of specific data science methods. In particular, students will analyze some types of economic and business data through specific statistical techniques. We also want to encourage students to specialize in the use of the open source statistical package R.

Ability to apply knowledge and understanding

At the end of the teaching course, the student will also be able to analyze data bases, even large ones, with sophisticated statistical methods, with the help of case studies carried out with the statistical software R. The acquired knowledge will allow him to critically interpret economic and / or business relationships.

Contents

1.Network data Analysis
2. Advanced Regressive Methods
3. Cluster Analysis
4. Time Series
5. Factor Analysis and Structural Equation Models
6. Correspondences Analysis
7. Multidimensional Scaling

Extended Syllabus

1.Network data Analysis
1.1. Introduction
1.2. Grafh types: Direct and Indirect
1.3. Visualization and descriptive analysis of Network data
1.4. Graphic layout
1.5. Connections and Contiguity Matrixes
1.6. Metrics and taxonomy of Network data
1.7. Use of Network data in Classification and Forecasting
1.8. Network data collection with R
1.9. Applications
2. Advanced Regressive Methods
2.1. Local polynomial regression: non-parametric regression
2.2. Selection of variables based on the penalization of regression models
2.3. LASSO
2.4. Quantitative regression
2.5. Applications
3. Cluster Analysis
3.1. Introduction
3.2. Distances between Units and between Groups
3.3. Hierarchical clustering
3.4. Dendrogram: visualization of the aggregation process
3.5. Limits of Hierarchical Clustering
3.6. Non-Hierarchical Clustering The k-Means Algorithm
3.7. Applications
4. Time Series
4.1. Introduction
4.2. Collection Time Series data with R
4.3. Components of a time series
4.4. The ARIMA models: Identification and Estimation
4.5. Use of the ARIMA models for prediction
4.6. Other forecasting methods
4.7. Applications
5. Factor Analysis and Structural Equation Models
6. Correspondences Analysis
7. Multidimensional Scaling

Recommended Bibliography

Course slides.
James G, Witten D, Hastie T, Tibshirani R (2013). An Introduction to Statistical Learning
with Applications in R. Springer.

Further readings:
Giudici P, Figini S (2009). Applied Data Mining for Business and Industry. Wiley
Ledolter J. (2013). Data Mining and Business Analytics With R. Wiley
Shmueli G, Bruce PC, Yahav I, Patel NR, Lichtendahl KC, Jr. (2018). Data Mining for Business Analytics. Wiley

Methods of Provision

Conventional

Teaching Methods

Lectures.
R practice and exercises.

Evaluation methods

Verification of learning:

Knowledge and ability to understand

To verify the learning a test and an interview are scheduled. The test will consist of theoretical questions and empirical exercises on the whole program with particular attention to the use of the software R, simulating some statistical analysis on real case studies. The final evaluation, expressed in thirtieths, takes into account both the test and the interview.

Ability to apply knowledge and understanding

During the test and the interview, students' ability to apply the knowledge of advanced data science models is tested so as to be able to deal with specific case studies.

Contacts/More Information

E-mail: benedett@unich.it
In the first semester the Professor sees students only by appointment (benedett@unich.it). In the second semester, the office hours for students is scheduled for Wednesday from 4 pm to 6 pm, studio DEC 2 ° floor, Viale Pindaro 42.

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Aree

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