Unit ECONOMETRICS
- Course
- Finance and quantitative methods for economics
- Study-unit Code
- GP004243
- Location
- PERUGIA
- Curriculum
- Statistics for finance and economics
- Teacher
- Carlo Andrea Bollino
- Teachers
-
- Carlo Andrea Bollino
- Hours
- 42 ore - Carlo Andrea Bollino
- CFU
- 6
- Course Regulation
- Coorte 2020
- Offered
- 2020/21
- Learning activities
- Caratterizzante
- Area
- Economico
- Academic discipline
- SECS-P/05
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- The course provides the methods for the econometric analysis and verification of the hypotheses of the economic theory. The student learns the experimental verification method applied to economic theory and deepens the analysis of the main empirical relationships of the microeconomic and macroeconomic literature.
- Reference texts
- G. Amisano, Elementi di Econometria, Mondadori, 2004
(suggested reference: J.H. Stock M.W. Watson, Introduzione alla econometria, Pearson, 2012) - Educational objectives
- The Econometrics course provides the analytical tools and fundamental methods for the study of economic relations and for the quantification of structural parameters. The student acquires autonomous ability to estimate and model the magnitudes and relations of economic theory.
- Prerequisites
- knowledge of mathematics, statistics and economics
- Teaching methods
- Classroom lectures and practical work
- Learning verification modality
- written exam and group project work
- Extended program
- Introduction to econometrics: Frisch 1933, Matrix algebra - definitions and operations
Matrix algebra, Multivariate density functions and Multivariate normal function
Linear general model - the OLS estimator and properties
Laboratory: introduction to econometric software
The ML estimator and properties
Goodness of fit measures; proof of hypotheses; restrictions on coefficients
Laboratory: organization of data and estimates
Violation of classical hypotheses - the GLS estimator and properties
Heteroskedasticity and autocorrelation
Laboratory: tests on coefficients
The SUR model - estimation and special cases
Introduction to the dynamic specification integration and cointegration
Laboratory: heteroskedasticity test and autocorrelation
Dynamic ECM specification
Dynamic specification VAR
Laboratory: Dickey Fuller and Engle Granger tests
Binary choice models: logit and probit models
The electricity market and market power
Laboratory: ARMA and cointegration
Nonlinear estimates of demand systems
Estimation of the costs of not participating in the environmental objectives
Estimation of the consumption function with error correction
Estimation of the convergence sigma beta gamma