Excerpt from course description

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

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.