Insegnamento mutuato da: B026789 - STATISTICA PER L'ECONOMIA E L'IMPRESA Corso di Laurea in STATISTICA
The course is held by prof. Laura GRASSINI (6 ECTS) and (3 ECTS).
PART 1. Laura Grassini (6 ECTS). Introduction to national accounts. Theory and practice of index numbers (prices and quantities). Productivity and efficiency analysis. Analysis of balance sheet data. Distress analysis.
PART 3 (3 ECTS) New data sources for business: introduction to data mining and machine learning.
Learning material will be available on the Moodle platform.
Biggeri L., Bini M., Coli A., Grassini L., Maltagliati M. (2012) Statistica per le decisioni aziendali. Ed. Pearson, Milano.
Piercarlo Frigero, Introduzione alla contabilità nazionale, Giappichelli, 2016.
Giovannini E. (2015), Le statistiche economiche, Il Mulino.
Istituto nazionale di statistica (2018), Rapporto annuale 2018 – La situazione del Paese. https://www.istat.it/it/archivio/214230
Istituto nazionale di statistica (2012), Linee guida per la qualità dei processi statistici. http://www.istat.it/it/strumenti/qualità-dei-dati/linee-guida
Predetti A. (2002), I numeri indici. Teoria e pratica dei confronti temporali e spaziali, Giuffrè.
KNOWLEDGE. Significant measures and indicators for business: benchmarking analysis, efficiency, productivity, evaluation of firm's performance through financial statement analysis (profitability, financial distress, etc.).
Multivariate analysis with unsupervisioned and supervisioned methods..
Principles of national account. The economic aggregates.
Index numbers (price and quantities). Representation at constant prices of monetary aggregates (deflation).SKILLS. Ability for evaluating firm's performance: efficiency, productivity, profitability measures for monitoring firm's performance and for benchmarking activities. Application of multivariate methods on new data sources like: Twitter data, logfiles, scanner data. Ability of understanding statistical packages outputs.
Recognizing which variables must be observed to represent the functioning of an economic system, and how they are inter-related. Measuring the change over time of economic aggregates with particular attention to the measurement of price and inflation changes
Traditional lectures, computer lab, final project
Learning material. Moodle platform:
Type of Assessment
Oral exam. Weight: Part 1: 2/3, part 2: 1/3.
the total length of the oral exam will be (approximately) 45 minutes.
The course aims at providing knowledge and skills for the collection and processing of data for the construction of significant measures and indicators for business and economic analysis.
Outline of the course.
Introduction to national account. General structure of the national account system (SNA) and the European system of integrated economic accounts (SEC). Evolution of national accounts; measurement of economic phenomena; methods for quantifying the economic aggregates; stock and flow variables.Representation of the economic relations in closed and open economies. The classification of production units in branches. The classification of operators, operations and aggregates. Rules of registration of operations and flows. The income circuit and its analytical representation through the national accounts: the accounts of production, distribution, redistribution, utilization and accumulation. The accounts with the rest of the world. Financial accounts. Index numbers of prices and quantities. Paasche, Laspeyres, Fischer formulas of synthetic index numbers. Economic aggregates at market prices and at constant prices (deflation).
Efficiency and productivity analysis. Technical efficiency: parametric and non-parametric methods. Partial and total-factor productivity.
Business Performance Indicators from financial statement data. Financial ratios. Distress analysis (introduction).
Data mining and machine learning. Supervised methods (classification trees and CART, k-NN, naive Bayes) and non-supervised (associative rules, clustering).
Special issues and topics: text-mining of Twitter data, analysis of logfiles
Software used: R (http://www.rdatamining.com/)