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Econometric Methods II PhD Course, Economics Department, Universitat Pompeu Fabra and Barcelona GSE, 2011 Fridays 9am-11am (20.173) and 5pm-7pm (20.031) Overview This course aims at equipping students with the tools needed to produce applied macro economic research. Most of the material can be found in "Time Series Analysis" by James D. Hamilton (Princeton University Press, 1994) and Cochrane's text, but reading articles will also be required. Lecture 1 Overview and introduction Lecture 2 Linear Difference Equations Notes and MatLab code for TA Session 1 (April 19) Lecture 2 Stationary ARMA models Lecture 4 Forecasting and prediction Homework 1 (due 24.00 Monday April 30) Lecture 5 Maximum Likelihood Estimation Lecture 6 Estimating Vector Autoregressions (VARs) Notes and MatLab code for TA Session 2 (May 3) Lecture 7 Structural VARs: Identification Lecture 8 Identification of Structural VARs II Lecture 9 Factor models Homework 2 (due 24.00 Sunday May 20) Lecture 10 - 11 Cointegration MatLab code for TA Session 3 (May 17) Lecture 12 State space models and the Kalman filter Lecture 13 Applications of the Kalman filter Lecture 14 ML Estimation of State Space Models Notes and MatLab code for TA Session 4 (May 31) Lecture 15 Conditional Heteroscedasticity Lecture 16 Introduction to Bayesian Statistics Homework 3 (due 24.00 Tuesday June 12) Lecture 17 The Metropolis-Hasting Algorithm Lecture 18 Bayesian Posterior Analysis and the Gibbs sampler MatLab code for TA Session 5 (June 14) Homework 4 (due 24.00 Sunday July 1) Lecture 19 - 20 Course Review |