- Docente: Anna Gloria Billè
- Credits: 8
- SSD: STAT-02/A
- Language: English
- Moduli: Silvia Emili (Modulo 1) Anna Gloria Billè (Modulo 2)
- Teaching Mode: In-person learning (entirely or partially) In-person learning (entirely or partially) (Modulo 1); In-person learning (entirely or partially) (Modulo 2)
- Campus: Rimini
- Corso: First cycle degree programme (L) in Economics of Tourism and Cities (cod. 6645)
Learning outcomes
By the end of the course, students will gain basic knowledge about the role of statistical methods for the analysis of economic problems and phenomena, mainly from a spatial perspective. They will learn how to use a statistical software to acquire, manage, and analyze spatial data for various applications, from both a descriptive and modelling point of view. They will develop practical skills to apply in the search of a solution of real-world problems in diverse fields (e.g. urban planning, or environmental science), and will become able to deal with big data and data mining techniques to identify spatial patterns and make informed decisions.
Course contents
MODULE 1
Introduction to basic concepts of spatial phenomena and statistics. The spatial weighting matrix. Global measures of spatial association. Local Indicators of Spatial Association (LISA). Hypothesis testing for global and local spatial association. Linear regression and spatial data.
MODULE 2
Introduction to linear regression models. Specification and estimation of linear spatial models for cross-sectional data. The concept of spillover effects and marginal effects. Economic definition of the weighting matrix. Spatial models with regimes. Applications in R.
Readings/Bibliography
- Bivand R.S., Pebesma E., Gómez-Rubio V. (2013) Applied Spatial Data Analysis with R. Springer.
- LeSage J. and H.K. Pace, Introduction to Spatial Econometrics, 2009, CRC Press.
- Elhorst, J.P., Spatial Econometrics: from Crossectional Data to Spatial Panels, 2014, Springer.
- Anselin L., Spatial Regression Analysis in R A Workbook, 2007
Teaching methods
Lectures and tutorials with the R software
Assessment methods
FOR ATTENDING & NOT ATTENDING STUDENTS: Written exam including multiple choices, RStudio coding and output interpretation, questions on all the topics included in the syllabus (Max grade: 33)
The grading system is as follows:
< 18: not sufficient (exam failed)
18-21: sufficient
22-24: satisfactory
25-27: good
28-30: very good
31-33 (30 cum laude): excellent
The examination can be taken in one of two ways:
- by passing two midterm exams, which must be taken at the end of the first module and at the end of the second module, respectively; or
- by taking a single comprehensive final exam covering the entire course.
In the first case, the final grade is the average of the grades obtained in the two midterm exams. In the second case, the final grade is the score obtained in the comprehensive final exam.
Teaching tools
Software: RStudio
Office hours
See the website of Anna Gloria Billè
See the website of Silvia Emili