Time Series Econometrics

Introduction

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.

Course content

I. Univariate stationary time series

• Stationary AR and MA processes
• Forecasting
• 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