Econometrics (2017/2018)

Course code
Name of lecturer
Laura Magazzini
Laura Magazzini
Number of ECTS credits allocated
Academic sector
Language of instruction
I sem. dal Oct 2, 2017 al Jan 31, 2018.

Lesson timetable

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Learning outcomes

Statistic tools and economic theory will be applied in order to provide students with capabilities to understand and perform empirical analysis of economic phenomena. Empirical problems and applications will be discussed during the course to provide students with the tools needed for the analysis of economic data.


1. Introduction
- Economic questions and data
- Review of probability
- Review of statistics

2. The regression analysis
- Linear regression model: single regressor and multiple regressors
- Ordinary least squares estimation of model coefficients
- Least squares assumptions
- Properties of OLS estimators
- Hypothesis testing and confidence intervals
- Goodness of fit
- Heteroschedasticity and homoschedasticity
- Omitted variable bias
- Generalized least squares
- Nonlinear regression functions
- Assessment of studies based on multiple regression

3. Regression analysis of time series data
- Forecasting
- Autoregression
- Non-stationarity: trend and structural break
- Stationarity in the AR(1) model
- Estimation of dynamic casual effects

Reference books
Author Title Publisher Year ISBN Note
James H. Stock, Mark W. Watson Introduzione all'econometria (Edizione 4) Pearson Education Italia 2016 978-8-891-90124-8

Assessment methods and criteria

During the exam the students will sit in the computer lab. It will last two hours and will cover the full program of the course. The exam aims at assessing the capabilities of the student for the econometric analysis of cross sectional and time series data. Students will be asked to perform an econometric analysis on a dataset with the software Gretl. Questions on econometric theory will also be assigned.