Università degli Studi di Napoli "Parthenope"

Teaching schedule

Academic year: 
2021/2022
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 insurance 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 of spatial analysis and digital cartography tailored to the economic, environmental, financial and insurance 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.
Pattern analysis. Spatial lag. Spatial weigh matrix. Spatial autocorrelation. Spatial Auto-Regressive (SAR) model. Spatial Error Model (SEM). Spatial regression model diagnostics. Endogeneity and exogeneity. Instrumental variables. Spatial Durbin Error Model (SDEM). Spatial Autoregressive Confused (SAC). Spatial Durbin Model (SDM). Spatial Lag X (SLX). Spatial heterogeneity. Geographically Weighted Regression (GWR). Spatial regression models for panel data. Spatial models with fixed effects and random effects. Specification tests.
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.

Part I (24 h):
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.

Part II (24 h):
Pattern analysis. Spatial lag. Spatial weigh matrix. Spatial autocorrelation. Spatial Auto-Regressive (SAR) model. Spatial Error Model (SEM). Spatial regression model diagnostics. Endogeneity and exogeneity. Instrumental variables. Spatial Durbin Error Model (SDEM). Spatial Autoregressive Confused (SAC). Spatial Durbin Model (SDM). Spatial Lag X (SLX). Spatial heterogeneity. Geographically Weighted Regression (GWR). Spatial regression models for panel data. Spatial models with fixed effects and random effects. Specification tests.

Part III (24 h):
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.

Teaching Methods

Traditional lectures. Exercises and pc-lab using Excel and add-in (PHStat) and other software (R, GeoDa, GeoDaSpace) and datawarehouse. Individual and group work projects. Support materials and slides used at lesson are also 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
- Insee – Eurostat (2018), Handbook of Spatial Analyses – Theory and Application with R
- OECD (2008), Handbook on constructing composite indicators – Methodology and user guide
- Other study material (scientific articles, slides) by the teacher

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, environmental, financial and insurance fields with real data.
The discussion of these projects towards the middle of the semester of lessons can be proposed as a mid-term examination, which, in any case, is integrated with the administration of written questions on the program carried out up to that point. The written part of the exam includes five open questions, each of which is assessed on a scale from 0 to 6 points. The time available to complete the written test is 1 hour and 30 minutes.
In order to assess the students’ ability in applying their statistical skills in an interdisciplinary context, the final examination includes the presentation and discussion of the materials produced (R programs, elaborations with Excel, GeoDa) and results obtained to the teachers and other students using slides. This stage also includes an oral assessment of all Course contents. The oral examination is evaluated on a scale from 0 to 30 and is averaged with the grade obtained at the discussion of the project. In the presence of the mid-term examination, the vote so obtained is averaged with the grade of the mid-term examination. The laude can be assigned if the student shows an excellent ability to reason, link and deepen the various topics addressed during the course.

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