ECONOMETRIC PERSPECTIVES ON CIRCULAR ECONOMY
(objectives)
The course aims at introducing students to the main econometric tools for the analysis and advanced processing of economic data, specifically connected to the study of an economic system oriented towards circularity. The examination of the different statistical inference tools and instruments, linear and non-linear regression models will allow students to acquire useful knowledge to acquire critical ability and autonomy of judgment in choosing and implementing the most appropriate statistical and econometric tools for processing of statistical data of an economic nature. The applications and case studies drawn from scientific literature will allow students to become aware of and know the state of the art and research carried out in the circular economy framework, with specific reference to the quantitative aspects, tools and methods, the specifications adopted for the implementation of business and economic models of circular economy and the related study of the relationships and associations between variables.
With specific reference to the Dublin descriptors, the course aims to achieve the following learning objectives:
Knowledge and ability to understand : at the end of the course, students will acquire specific knowledge on statistical methodologies for data analysis related to some main topics of interest for the degree course. With reference to econometric methodologies, students will develop methodological knowledge and the ability to use methods and tools for: a) descriptive data analysis; b) the study of relationships between variables both from a descriptive and inferential point of view; c) multivariate data analysis. Students will acquire skills on the characteristics of the different structures of databases (cross-section, time series, longitudinal data) and on their management and processing through statistical software.
Ability to apply knowledge and understanding: at the end of the course, students will have acquired methodological knowledge and analytical skills and will be able to independently interpret analyses and empirical research on the most relevant areas of intervention in the international arena with specific reference to circular economy strategies and the Sustainable Development Goals (SDGs). Students will be able to: i) evaluate the results of empirical analyses; consider the appropriateness of the econometric methodologies used; identify any limitations of the statistical and econometric analyses carried out and consider the use of alternative approaches; ii) develop case studies relevant to public decision makers, outlining the topic of interest, selecting the databases, identifying the econometric methodologies of empirical analysis, and communicating the main findings in the form of presentations and/or reports.
Autonomy of judgement: the course is designed to foster a critical approach to the use of different methods of data analysis for the interpretation of national and international phenomena of interest and relevant to the degree course. Students will: i) develop critical skills on the use of various methods in relation to the analysis objectives of the international phenomenon under study; ii) be able to assess the contribution of a specific data analysis methodology to the study of complex phenomena; iii) develop the ability to coherently integrate the contribution provided by quantitative analysis methods with the student's interdisciplinary skills. These objectives are pursued by providing practical activities of work and data processing, functional to activate critical thinking processes of the individual student's skills. Communication skills: students will have developed specific skills to communicate in an unambiguous and clear manner the data analysis scheme adopted for the empirical study, with particular reference to the structure of the databases, the statistical and econometric methods used, the results obtained. The ability to effectively communicate data analysis and the acquisition of an appropriate technical language will be achieved through written tests, presentation and discussion of research results on empirical data, scientific articles and reports on issues related to the circular economy and economic sustainability.
Learning skills: the teaching methodologies used during the course, which will include the development of case studies and participation in seminars, and the use of methods of verification of learning through the implementation of practical projects will help to strengthen the capacity for independent judgment and the development of self-learning skills by students. This competence will be achieved through the application of econometric and statistical methods in areas strictly relevant to the degree course.
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Teacher
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SECONDI Luca
(syllabus)
Basic elements of probability and statistical inference. Point estimate, interval estimation and hypothesis testing. The simple linear regression model. The classical hypotheses underlying the linear regression model. Ordinary Least Squares estimator. Goodness of fit measures, hypothesis testing and confidence intervals in the linear regression model. Introduction and use of dichotomous variables in the regression model. Linear regression with multiple regressors: distortion from omitted variables, the OLS estimator of multiple regression, measures of goodness of fit, least squares and collinearity assumptions, inference in the multiple linear regression model. Nonlinear regression functions: nonlinear functions of a single independent variable, interactions between independent variables. Regression with binary dependent variable: binary dependent variables and linear probability model. Probit and logit regressions. Estimate and inference in logit and probit models. Applications. Introduction to regression with panel data. Regression with fixed effects, regression with temporal effects. The statistical and econometric approach to the study of the circular economy: data collection, existing data sources at national and international level, methodological analysis, examples of circularity processes' measurement in the micro/macro economic field and empirical applications.
(reference books)
J. H. Stock and M. W. Watson, Introduction to Econometrics, most recent available edition
Lecture notes and teaching materials made available by the teacher during the class
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Dates of beginning and end of teaching activities
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From to |
Delivery mode
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Traditional
At a distance
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Attendance
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not mandatory
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Evaluation methods
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Written test
Oral exam
A project evaluation
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