000107R - QUANTITAIVE METHODS FOR ECONOMICS

courses
ID:
000107R
Duration (hours):
72
CFU:
9
SSD:
STATISTICA ECONOMICA
Located in:
PESCARA
Url:
ECONOMICS AND FINANCE/GLOBAL ECONOMICS AND POLICY ANALYSIS Year: 1
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 training aims to reach the following learning outcomes: Knowledge and ability to understand The course aims at providing methodological and application for specific data science methods in the field of economics. In particular, students will analyse economic and business data through specific statistical techniques, from basic to more advanced. Students will be encouraged to apply the exposed methods on the software 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 and master some of the quantitative methods useful to interpret economic data.

Course Prerequisites

Basic knowledge of inference and R software.

Teaching Methods

Lectures. R practice and exercises.

Assessment Methods

Knowledge and ability to understand Assessment will be based on scheduled tests comprising theoretical questions and empirical exercises covering the entire course syllabus. The evaluation includes practical R-based simulations of statistical analyses on real case studies. The final grade, on a scale from 0 to 30, reflects both the theoretical test and R applications. Ability to apply knowledge and understanding Through the test and oral examination, students’ capacity to apply advanced data science methods to specific case studies will be evaluated.

Texts

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7 Wei, W. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods (2nd ed.). Pearson/Addison Wesley.

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) Fundamentals in time series analysis

Course Language


English

Degrees

Degrees

ECONOMICS AND FINANCE 
Master’s Degree
2 years
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