000175L - STATISTICAL METHODS FOR ECONOMIC ANALYSIS

courses
ID:
000175L
Duration (hours):
72
CFU:
9
SSD:
STATISTICA ECONOMICA
Located in:
PESCARA
Url:
ECONOMICS AND COMMERCE/ECONOMIA E STATISTICA Year: 2
Year:
2025
Course Catalogue:
https://unich.coursecatalogue.cineca.it/af/2025?co...

Overview

Date/time interval

Secondo Semestre (12/02/2026 - 12/05/2026)

Syllabus

Course Objectives

The educational objective for the student is to achieve the following learning outcomes: Knowledge and understanding The course aims to provide methodological and application advancements of specific multivariate analysis methods. In particular, students will analyze certain types of economic and business data through specific statistical techniques. Additionally, the course encourages students to specialize in the use of the open-source statistical package R. Ability to apply knowledge and understanding At the end of the course, with the help of practical case studies carried out using the statistical software R, the student will be able to analyze multivariate data using sophisticated statistical methods. The knowledge acquired will enable them to critically interpret economic and/or business relationships.

Course Prerequisites

Inference and basic knowledge of R/R Studio.

Teaching Methods

Knowledge and ability to understand To assess learning, a written and an oral exam are planned. The written exam will consist of theoretical questions and exercises covering the entire syllabus, with particular attention to the use of R software, simulating some statistical analyses on real cases. The final grade, expressed on a 30-point scale, takes into account both the written and the oral exam. Ability to apply knowledge and understanding During both the written and oral exams, students’ ability to apply knowledge of advanced multivariate analysis models will be tested to ensure they can handle specific case studies.

Assessment Methods

Test + interview with R applications.

Texts

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. New York: Springer.

Contents

The lectures will face the following topics: 1) Linear methods for regression 2) Unsupervised learning I - Cluster Analysis 3) Unsupervised learning II - Principal Component Analysis 4) Time series analysis and cointegration for policy analysis

Course Language

Italian

Degrees

Degrees

ECONOMICS AND COMMERCE 
Master’s Degree
2 years
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