Time Series Analysis

Academic year: 2008-2009 Course code module FTEMAJ0133 Semester: 1st semester Credits: 6 Study load (hours) 168 Theory (hours): 45,00 Practice/Exercises(hours): 15,00 Other (hours): Part-time program: Instructor(s) Jozef Plasmans Language of instruction: English Semester exam information: exam in the 1st semester Contract restriction information: 1. Prerequisites *Algemene competenties A general introduction to econometrics is very welcome. *Sequentiality 2. Objectives (expected learning outcomes) To be able to perform yourself a time series analysis of (multiple) economic data (simultaneously). 3. Course content 1. A typology of dynamic models: autoregressive distributed lag models, partial adjustment models, error correction models, autoregressive error models. 2. ARIMA models: stationarity and the (partial) autocorrelation function, autoregressive (AR) processes, moving average (MA) processes, ARMA processes, testing for unit roots, ARIMA models and the Box-Jenkins approach; detecting and dealing with aberrant observations. 3. Cointegration and causality, including spurious regressions and cointegration and error-correction mechanisms. 4. Forecasting: AR processes, MA processes; ARMA and ARIMA processes; ad-hoc forecasting methods; evaluation of density forecasts. 5. Estimation, testing and forecasting Varying Parameter Models (VPMs) 5.1 Regime Switching Models (RSMs): Markov-Switching models, Threshold Autoregression models and Smooth Transition Autoregression models; one-market and multi-market disequilibrium models 5.2 Volatility models: univariate ARCH and generalized ARCH (GARCH) models, functional forms of GARCH models (integrated GARCH or IGARCH models, absolute value GARCH models, exponential GARCH or EGARCH models, GARCH-in-mean models), multivariate GARCH processes: VEC and BEKK models. 6. Multivariate time series analysis: Impulse response functions, V(AR)(MA) models, VARs: orthogonalization, variance decomposition, multivariate cointegration and Granger causality, forecasting with seasonally cointegrated processes. 4. Teaching method Direct contact: Lectures Exercise sessions Personal work: 5. Assessment method Exam: Oral, with written preparation Written assignment: With oral presentation 6. Compulsory reading – study material Verbeek, M., "A Guide to Modern Econometrics", Second Edition, Wiley, 2005.Plasmans, J., "Modern Linear and Nonlinear Econometrics", Part II (Time Series Analysis), Springer, 2006. 7. Recommended reading - study material Green, W., "Econometric Analysis", Fifth Edition, Prentice Hall, 2003. 8. Tutoring laatste aanpassing: last update: 22/12/2008 18:55 ilke.franquet