ESTA ASIGNATURA SE IMPARTE EN INGLÉS. LA INFORMACIÓN CONTENIDA EN ESTA GUÍA ESTÁ EN INGLÉS.
This is an optional course on Time Series Analysis in the second year of the Master in Quantitative Economics.
This course builds on an earlier course in univariate time series and has the following objectives: 1.To introduce the students to the main developments in time series econometrics which have assumed considerable importance in the last two decades. 2. To provide the students with an understanding of the relevant concepts which are fundamental to an understanding of time series econometrics. 3. To examine, in detail, the statistical models that are in use and the techniques and methods that are used in their analysis and to note their strengths and limitations. 4. To consolidate the knowledge gained by solving simple problems 5. To provide the student with an ability to critically assess the many applications of time series eonometrics to problems in economics
1. UNIVARIATE TIME SERIES Brief review of ARMA models. Maximum likelihood estimation of ARMA Models. 2. MULTIVARIATE TIME SERIES Vector Autoregressions. Estimation. The Impulse Response Function. Weak exogeneity. Granger causality. Vector autoregressions and structural econometric models. 3. GENERALISED METHOD OF MOMENTS Estimation by GMM. Asymptotic distribution of GMM estimators. Instrumental variable estimation. 4. NON-STATIONARITY Properties of estimators and tests in non-stationary models. Non-stationary time series. Spurious regressions. Integrated processes. Trend and difference stationarity. Testing for unit roots. 5. COINTEGRATION AND ERROR CORRECTION MODELS Testing for cointegration. Estimating the cointegrating vector. Hypothesis Testing. Cointegration and its implications. 6. COINTEGRATION IN SYSTEMS OF EQUATIONS Estimating cointegrating vectors in systems. Inference about the cointegration space. Asymptotic distributions of estimators of cointegrating vectors. 7. TIME SERIES MODELS OF HETEROSCEDASTICITY ARCH and GARCH Models. Maximum likelihood estimation. Testing for ARCH and GARCH models. Further extensions. REFERENCES The most comprehensive textbook in the field is J.D Hamilton (1994). Time Series Analysis, Princeton University Press. Despite being over eleven years old this is still the best book for our course. It will also be important for the follow up course in Semester 2. Numerous references will be made to this text but the more technical material will be avoided at this stage.
Problem Sets: 50%. Final Exam: 50%. Those students with a final grade below 5 will have a second opportunity in the corresponding exam period, but the grade corresponding to problem sets will be kept.
ENTREGA DE PROBLEMAS