Student guide Facoltà di Economia A.A. 2008/09

This course is designed to teach methods of data analysis to students whose primary interest is not in econometrics, statistics or mathematics. It purports to show students how to apply econometric techniques in the context of real-world empirical economic and financial problems. It covers most of the tools used in modern econometrics research, e.g. correlation, regression and extensions for time-series methods. During the course extensive use of real data examples is made and students are involved in hands-on computer work.

An overview of econometrics. Introduction to simple linear regression. Statistical aspects of regression. Multiple regression. Relaxation of classical assumptions: autocorrelation, heteroskedasticity, stochastic regressors. Introduction to time series analysis.

1. An overview of econometrics

2. A non-technical introduction to regression

3. The simple linear regression model under clasical assumptions: OLS and t-test

4. The multiple linear regression model under classical assumptions: OLS, F-test, dummy variables, forecasting

5. The multiple linear Regression model: relaxation of classical assumptions (autocorrelation, heteroskedasticity, stochastic regressors)

6. Introduction to Time series analysis (stationary vs non-stationary time series, autocorrelation function, volatility, unit roots, cointegration, ECM, VAR)

The assessment will be based on a written final examination.

Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons.