• Databases for bibliographic research (Cristina ZERBINI; Laura L'EPISCOPO - University of Parma)
The course covers the processes and methods of publishing research tools for analysis of academic literature through databases.
The first part of the course focuses on the importance of academic publications for the advancement of international knowledge and for an academic career. It examines the different types of publication, the structure of an academic paper, choosing a journal and the review and publication processes. Students are invited to share personal experience.
The second part of the course focuses on the literature review: the choice of keywords, the use of the databases available through the University of Parma (e.g. Ebsco Host, Emerald, Essper) as well as other online resources (e.g. Researchgate, Academia.edu, Scienceopen, Iris, sole24ore), and reference management software (Zotero and Mendeley). There are practical exercises
• Statistical methods for the analysis of economic and business data (Andrea CERIOLI; Fabrizio LAURINI - University of Parma)
The course consolidates knowledge of advanced statistical methodologies relevant to economics and business sciences and typically applicable to big data. The course consists of two modules.
The first module focuses on statistical models and algorithms for prediction and segmentation. It analyses the following methodologies: association measures and rules, and their applications to Market Basket Analysis; linear regression for classical inferential aspects and diagnosis of violations of basic assumptions; logistic regression for prediction of individual behaviour; classification trees, including overfitting and misclassification rates; Cluster Analysis and the non-hierarchical K-means segmentation algorithm. All methodologies are applied to economics or business examples using SPSS.
The second module focuses on theory and algorithms describing phenomena characterized by dynamics evolving over time. There are two aspects: the first extends multiple regression models to cases of seasonal data using ARIMA-type models. Basic assumptions are introduced, and inferential results and post-adaptation diagnostics are presented. The goal is to build models that can be used to provide reliable forecasts in the short and medium term. The regression model is subsequently extended to panel data. Practical exercises will verify student learning in the methodological sessions. There are two assignments at the end of the course.
• Qualitative research methodology (Caterina CAVICCHI; Emidia VAGNONI - University of Ferrara)
The module presents research design and offers in depth focus on qualitative research methodologies. Content:
- Definition of the research topic;
- Identifying research questions;
- Literature analysis and the systematic literature review;
- Relationship between theory and research – basics of epistemology and ontology;
- Qualitative - rational research methods, strengths and weaknesses;
- Approaches to qualitative research - ethnography, grounded research, action research, narrative research, case studies;
- Qualitative case studies - research design and data collection methods;
- Action research - research design and management methods;
- Data collection methods - focus groups and qualitative interviews;
- Data analysis methods - text analysis and content analysis.
• Quantitative research methodology (Beatrice LUCERI; Donata Tania VERGURA; Simone AIOLFI - University of Parma)
The course extends student knowledge on the collection and analysis of primary and secondary data through different quantitative research techniques.
The first part focuses on the use of quantitative research techniques for the collection of primary data. Two research techniques are studied. The structured questionnaire and the main methods of data analysis, with particular regard to validity and reliability measurement models are covered. The next focus is on the technique of experiments and quasi-experiments and construction process of the experimental protocol, the types of data collected and the basic analysis methodologies.
The second part delivers the theoretical knowledge and application tools to carry out quantitative systematic reviews of academic literature. The course focuses first on the use of SciMAT open source software for longitudinal bibliometric analyses for mapping the cognitive structure and thematic evolution over time of a line of research. The course also offers an overview of the theoretical and practical tools for systematically reviewing literature by means of meta-analyses and for critical reading meta-analyses.
The lectures will describe and discuss published academic papers, as well as data analysis software (mainly SPSS).
At the end of the course, students have about two months to design their research project on a topic of interest and to draft research questions and hypotheses, the methodology and the expected results. The projects are assessed individually, and strengths and weaknesses of the research designs are shared.
• Game theory (Chiara LODI - University of Urbino)
Game theory represents the strategic interaction between different agents and has become a key tool in economics studies. This course presents the basics of game theory and different real-world applications. The original forms of well-known games such as the Prisoner’s Dilemma, the Battle of the sexes and the Chicken game are presented in their original form and through examples of environmental economics. Mathematical tools and a basic introduction to sequential games and concepts of bounded rationality are provided.
Content:
- Basic concepts - rationality, payoff, classification and representation of games, strategies and Nash equilibrium, prisoner's dilemma (original game, from a monopoly to a duopoly; for example, common access to natural resources);
- Main games: Battle of the sexes (original game; mutually exclusive environmental project), the Chicken game (original game, cross-border species conservation policy), main-agent base model;
- Sequential games - Entrant-Incumbent game and Stackelberg model;
- Bounded rationality and adaptive dynamic models.
• Big databases and economic analysis. Application to patent data (Ugo RIZZO; Nicolò BARBIERI; Marianna GILLI - University of Ferrara)
Given the importance of big data for decision making and policy making, the ability to analyze large databases has become increasingly important to researchers. The module develops student ability to manage and analyze large databases using the OECD patent database.
The data management employed is STATA and the course presents basic use.
Contents:
- Opening datasets; importing datasets from other sources;
- Variables - types, commands for creation and replacement;
- Renaming and labelling variables; encoding variables;
- Operatiing if and looping commands (foreach, forvalues)
- Operatiing keep and drop commands; designing data subsets.
Use of STATA:
- Descriptive statistics;
- Construction of the main summary tables (Estout) and graphs (bidirectional command).
Introduction to patent databases, focusing on:
- OECD patent databases;
- Overview of the OECD REGPAT;
- Key aspects of patents (technological classes, citations, patent families, etc.);
- Constructing key variables with patent data;
- Using patent data for the study of innovation.
The use of patent information for economic purposes, focusing on:
- Analysis of the economy of innovation with patent data;
- Deducing indicators from patent data;
- Application of the patent based indicator;
- Answering research questions based on patent data analysis (Examples).
