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 economic 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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