Time Series Econometrics
The aim of the course is to give the students a formal understanding of time series econometrics at a level expected among Ph.D students in economics, finance and related disciplines.
I. Univariate stationary time series
- Stationary AR and MA processes
- Spectral analysis
II. Models of non-stationary time series
- Deterministic and stochastic trends, unit root tests, structural change
- Trend/cycle decompositions (linear filters)
- Analysis of business cycles in the frequency domain, spurious cycles
III. Vector autoregression (VAR) methodology
- Granger causality, cointegration.
- Structural VARs impulse responses, forecast error variance decomposition
- Identification: Cholesky, long-run restrictions, sign restrictions, external instruments.
IV. Methods of Estimation
- Instrumental variables (IV) estimation
- Maximum likelihood estimation
- Generalized method of moments (GMM) estimation
V. State space models and the Kalman filter
- Kalman filter
- Factor models
VI. Bayesian estimation
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