Università degli Studi di Napoli "Parthenope"

Teaching schedule

Academic year: 
2019/2020
Belonging course: 
Course of Master's Degree Programme on QUANTITATIVE METHODS FOR ECONOMIC AND FINANCIAL EVALUATIONS
Disciplinary sector: 
ECONOMIC STATISTICS (SECS-S/03)
Language: 
Italian
Credits: 
9
Year of study: 
1
Teachers: 
Cycle: 
First Semester
Hours of front activity: 
72

Language

Italian

Course description

The course aims at developing expertise to:
- Be able to explore trends and relationships among economic and environmental data, financial and market data
- Know how to use the methodology and tools that statistics provides to support empirical applications, decision-making processes and in the guidelines for geolocation choices
- Be able to interpret results from statistical methods and digital cartography tailored to the economic, environmental and financial needs and to the problem solving.

Expected learning outcomes:
Knowledge and understanding:
The student should be able to understand the main theories and models for the analysis of economic phenomena in a spatial framework. Moreover, he/she should know the main statistical tools for the territorial analysis of socio-economic topics as well as the main statistical sources of spatial microdata and metadata.
Applying knowledge and understanding:
The student should be able to know the spatial statistical tools and to implement them in specific statistical software. The student should be able to build the matrix of territorial data using information from external sources and to manage it using statistical software. He/she should be able to interpret correctly the output of spatial models obtained through statistical software.
Making judgements:
The student should be able to use the acquired knowledge in practical circumstances. Moreover, he/she should have the maturity and the ability to “think” autonomously a scientific work project and carry on in all its phases, from the definition of the subject to the data searching and to the implementation of statistical tools in the specific software.
Communication:
The student should be able to communicate clearly and with an adequate technical language the (individual and/or group) work projects to teacher and to the other colleagues of the Course. The student should be able to answer clearly and in-depth the questions of the oral examination.
Lifelong learning skills:
The student should be able to show a good learning ability and autonomy in investigating in-depth the matters of the Course using the references provided by the teacher.

Prerequisites

Basic knowledge of Statistics and Economics that are usually acquired by the student during the first-level courses offered by the University Parthenope in the economic areas. For students coming from different first-level degree programs, an integration including a relevant bibliographic reference will be provided.

Syllabus

Space in the economic and quantitative perspectives. Economic theories and models. Spatial data. Geocoding and Georeferencing. Representing spatial data: maps, cartography, Sit-Gis technology. Digital maps. Web maps and databases. Territory and statistical information. Typologies and sources of spatial data. Territorial systems and classification. Territorial location and interaction of economic agents.
Territorial indicators. Methodology of composite indicators. Definition of the phenomenon. Selection of basic indicators. Imputation of missing data. Bivariate and multivariate association. Data transformation and normalisation. Weighting and aggregation systems. Sensitivity analysis. Study-cases on social, economic and environmental topics analysed using the methodology of composite indicators.
Spatial lag. Spatial weigh matrix. Spatial autocorrelation. Multiple regression and spatial regression. Spatio-temporal models. Spatial interaction models. Pattern analysis: methods and measures. Main typologies of patterns. Methods of spatial distances.

I module (24 hours): Territory and data sources
Space in the economic and quantitative perspectives. Economic theories and models. Spatial data. Geocoding and Georeferencing. Representing spatial data: maps, cartography, Sit-Gis technology. Digital maps. Web maps and databases. Territory and statistical information. Typologies and sources of spatial data. Territorial systems and classification. Territorial location and interaction of economic agents.

II module (24 hours): Composite indicators
Territorial indicators. Methodology of composite indicators. Definition of the phenomenon. Selection of basic indicators. Imputation of missing data. Bivariate and multivariate association. Data transformation and normalisation. Weighting and aggregation systems. Sensitivity analysis. Study-cases on social, economic and environmental topics analysed using the methodology of composite indicators.

III module (24 hours): Spatial regression models
Spatial lag. Spatial weigh matrix. Spatial autocorrelation. Multiple regression and spatial regression. Spatio-temporal models. Spatial interaction models. Pattern analysis: methods and measures. Main typologies of patterns. Methods of spatial distances.

Teaching Methods

Traditional lectures. Exercises and pc-lab (SPSS, R, GeoDa, GeoDaSpace) and datawarehouse. Individual and group work projects. Support materials and slides used at lesson are made available through the e-learning platform Moodle.

Textbooks

- Arbia G. (2014), A Primer for Spatial Econometrics (with applications in R), Palgrave Macmillan
- LeSage J., Kelley Pace R. (2009, Introduction to Spatial Econometrics, Taylor & Francis Group
- OECD (2008), Handbook on constructing composite indicators – Methodology and user guide
- Altro materiale di studio (articoli scientifici, slides) a cura del docente

Learning assessment

The learning assessment is on an ongoing basis during the Course. Students are continuously expected to take part into work projects using computer and statistical software in economic, financial and environmental fields with real data. In order to assess the students’ ability in applying their statistical skills in an interdisciplinary context, the discussion of the materials produced (R programs, elaborations with SPSS and Excel, GeoDa) and the presentation of their results to the teachers and to the other students are planned at the end of the Course using slides. This stage also includes an oral assessment of all the Course contents.

More information