Italian (but possibility to study and take the exam on English material)
Course Content
Notions of probability and statistical inference. Specification and estimation of an econometric model with one or several regressors. Hypothesis testing on single coefficients and on linear restrictions. Violation of ideal hypotheses. Advanced models (panel data, limited dependent variable).
Stock e Watson Introduction to econometrics (Chs 1 to 11) IV ed. Pearson 2016
Learning Objectives
The aim of the course is to introduce the students to the theory and the practice of econometric models in contexts in which the classical assumptions of the Ordinary Least Squares estimator do not hold. Among the issues that will be considered during the course there are: categorical dependent variables; non-homoskedastic errors; non-indepentent errors; non orthogonal errors; errors in variables. The arguments will be presented by means of an extensive use of monte-carlo studies, to achieve a better understanding of the concepts presented, and to introduce the students to the practice of computational-based estimation and validation techniques.
Prerequisites
An introductory course in Statistics (estimation and hypothesis testing)
Teaching Methods
Classroom lectures with practical examples shown in class
Further information
Additional material available on the Moodle platform
Type of Assessment
A written exam with theoretical and practical problems to be solved, similar to what appears at the end of the textbook's chapters. An oral discussion may follow to ascertain the actual knowledge/understanding of the material. Knowledge of the GRETL software is needed.
Course program
Part I. Introduction and Review
Chapter 1. Economic Questions and Data
Chapter 2. Review of Probability
Chapter 3. Review of Statistics
Part II. Fundamentals of Regression Analysis
Chapter 4. Linear Regression with One Regressor
Chapter 5. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
Chapter 6. Linear Regression with Multiple Regressors
Chapter 7. Hypothesis Tests and Confidence Intervals in Multiple Regression
Chapter 8. Nonlinear Regression Functions
Chapter 9. Assessing Studies Based on Multiple Regression
Part III. Further Topics in Regression Analysis
Chapter 10. Regression with Panel Data
Chapter 11. Regression with a Binary Dependent Variable