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.

Planning

Références

  • 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.