W. Enders, 2014, Applied Econometric Time Series, 4th Edition, Wiley.
Learning Objectives
Upon completion of the course the student should be able to analyse economic time series and estimate univariate and multivariate models. He/she should also be alble to read most of the empirical papers in applied macro.
Prerequisites
Introductory econometrics. Statistcal inference. Calculus and linear algebra.
Teaching Methods
Traditional lectures
Further information
Additional material provided by the instructor
Type of Assessment
Three partial exams during the course or alternatively a final exam (firs option is recommended)
Course program
Time-Series Models, Difference Equations and Their Solutions, Lag Operators. Stochastic Difference Equation Models, ARMA Models, Stationarity, Stationarity Restrictions for an ARMA (p, q) Model , The Autocorrelation Function, The Partial Autocorrelation Function, Sample Autocorrelations of Stationary Series, Box–Jenkins Model Selection, Properties of Forecasts, Seasonality, Structural Change, Combining Forecasts. Deterministic and Stochastic Trends, Removing the Trend, Unit Roots and Regression Residuals, The Monte Carlo Method, Dickey–Fuller Tests and extensions, Power and the Deterministic Regressors, Panel Unit Root Tests, Trends and Univariate Decompositions, Intervention Analysis, ADLs and Transfer Functions, Limits to Structural Multivariate Estimation, Introduction to VAR Analysis, Estimation and Identifcation, The Impulse Response Function, Structural VARs, Examples of Structural Decompositions, Overidentifed Systems, The Blanchard–Quah Decomposition. Linear Combinations of Integrated Variables, Cointegration and Common Trends, Cointegration and Error Correction, Testing for Cointegration: The Engle–Granger Methodology, Cointegration and Purchasing Power Parity, Characteristic Roots, Rank, and Cointegration,