02054 - Econometrics

Learning outcomes

At the end of this course the student will know the linear multiple regression model for cross section data and the basic concepts of linear regression with time series data. More particularly, the student will be able to understand the application of the linear regression model in the recent empirical literature, to perform his own analysis using a specific econometric package, to present the results of his work.

Course contents

1. Economic questions and economic data
2. Simple linear regression. Ordinary least squares estimators. Inference. Goodness of fit. Hetroskedasticity and homoskedasticity. Examples.
   
3. Multiple linear regression. Ordinary least squares estimators. Inference. Goodness of fit. Omitted variable bias. Examples.
4. Non linear regression. Logarithms, polynomials, interactions of independent variables. Examples.
5. Linear regression with qualitative information. Dummy variables for binary and multiple categories, interactions. Examples.
6. Chow test for difference in regression function between groups. Examples.
7. Repeated cross section, Chow test for structural changes of regression functions. Examples.
8. Internal and external validity of multiple regression analysis.
9. Introduction toregression analysis with time series data. Examples.
10. Applications of the various methods to real data using the econometric software GRETL.

Readings/Bibliography

Stock, J.H., Watson, M.W. "Introduzione all'econometria", 2a edizione (2009), Pearson Education Italia.
See the course web page for further material: http://www2.dse.unibo.it/monfardi/indice_corsi.htm

Teaching methods

During the lectures the theoretical aspects are presented together with examples of their application. Students will learn how to use the basic tools to perform empirical analysis with real data. To this aim, the course involves some computer laboratory sessions using the econometric software GRETL.

Assessment methods

During the lectures the theoretical aspects are presented together with examples of their application. Students will learn how to use the basic tools to perform empirical analysis with real data. To this aim, the course involves some computer laboratory sessions using the econometric software GRETL.

Teaching tools

videoprojector, PC, computing lab

Links to further information

http://www2.dse.unibo.it/monfardi/indice_corsi.htm

Office hours

See the website of Chiara Monfardini