Introduction to time series econometrics

Objective

- Generalities on univariate second-order stationary processes - Autocovariances, partial autocorrelations - Innovations - Wold theorem - Asymptotic properties of empirical moments.
- AR, MA, ARMA, SARIMA processes - Canonical representation - Identification, estimation, tests and forecasting - Model building - Nonstationary models, Unit root tests.
- Stationary vector processes - Multivariate AR models - Statistical Inference - Causality tests, impulse-response analysis.
- Non-stationary vector processes and definition of cointegration - Cointegrated VAR models and error-correction models (ECM) - Estimation of cointegrated VAR - Testing for Cointegration.

References

• Brockwell, P.J. and R.A. Davis (1991) Time Series: Theory and Methods. 2nd Edition, Sringer.
• Brockwell, P.J. and R.A. Davis (2002) Introduction to Time Series and Forecasting. Sringer.
• Gouriéroux, C.  and A. Monfort (1997) Time Series and Dynamic Models,  Cambridge University Press, Cambridge.
• Hamilton, J. D. (1994)  Time Series Analysis. Princeton University Press.