Brief review of Statistics and Probability. Model specification and estimation: univariate and multivariate. Hypothesis testing on single coefficients and linear restrictions.
Stock e Watson Introduzione all'econometria (cap. da 1 a 11) IV ed. Pearson 2016
Learning Objectives
Module objective is to introduce the student to the concepts and practice of an advanced econometric analysis. Students will learn to apply classic linear regression analysis to a wide array of relevant cases. Amongst the most important examples of violations of estimator's assumptions the emphasis will be on categorical dependent variables, heteroskedastic shocks and measurement errors.
Module topics will be presented through an extensive use of Monte Carlo simulations for a better understanding of the underlying mechanisms.
Prerequisites
Statistics (estimation and hypothesis testing)
Teaching Methods
Standard lectures with examples of problem solving and use of econometric software.
Further information
Additional material available on Moodle.
Type of Assessment
A written examination similar to the theoretical and practical problems at the end of the textbook chapters and an optional oral examination to further verify the student's knowledge and understanding as well as the ability to apply knowledge and understanding. It is necessary to be proficient in GRETL.
Course program
Brief review of probability and statistics.
Specification of an econometric model.
Estimation and hypothesis testing in statistics.
The Classic Linear Model: specification, estimation, hypothesis testing, non-linear specifications.
Panel Data Regressions: Pooled OLS, Fixed Effects, Random Effects, Hausman Test.
Binary Data Regressions.