Econometric Methods II

PhD Course, Economics Department, Universitat Pompeu Fabra and Barcelona GSE, 2011

Mondays and Tuesdays 11am-1pm (20.049)
Office Hours: Thursdays 10.30am-12.00am, Room 23.408
Exam date: June 27 10.00am - 12.00am , 20.061TBA


Overview

This course aims at equipping students with some of the tools needed to produce applied macro economic research. Most of the material can be found in the etxt book New Introduction to Multiple Time Series Analysis by Helmut Lutkepohl (Berlin: Springer-Verlag, 2005) or in Cochrane's text but reading articles may also be required.


Syllabus

Homework 1 (due 24.00 Friday April 29)

Homework 2 (due 24.00 Wednesday May 18)

Homework 3 (due 9.00 Monday June 6)

Homework 4 (due 13.00 Thursday June 16)

Exercise Questions for exam

2009 midterm

Readings

Lecture 1 Overview and introduction
Lutkepohl Ch 1, 2.1

Lecture 2 Linear Stochastic Difference Equations
Lutkepohl Ch 2.2, 2.3

Lecture 3 Estimating VARs using orthogonal projections
Lutkepohl Ch 3.1 - 3.3
Notes on the Projection Theorem

Lecture 4 Specification of VARs
Lutkepohl Ch 4.1 - 4.3, 4.6
Slides

Lecture 5 Identification of Structural VARs
Lutkepohl Ch 9.1, Cochrane Ch 7.1 Leeper, Sims and Zha (1996)
Slides

Lecture 6 Identification of Structural VARs II
Lutkepohl Ch 9.1, Cochrane Ch 7.1
Blanchard and Quah (1989)
Rudebusch (1998)
Sims(1996!) response to Rudebusch (1998)
Chari, Kehoe and McGrattan (2008) Ellen McGrattan in multimedia format
Slides

Lecture 7 Cointegration
Lutkepohl Ch 6.3 and Cochrane Ch 11

Lecture 8 Cointegration II
Lutkepohl Ch 6.3 and Cochrane Ch 11

Lecture 9 Factor models
Stock and Watson (2010) and Bernanke, Boivin and Eliasz (2005)
Slides

Lecture 10 Presentations of Homework II

Lecture 11 Forecasting
Stock and Watson (2006)
Slides

Lecture 12 State space models and the Kalman filter
Lecture notes on the Kalman Filter

Lecture 13 Kalman filter applications
Slides

Lecture 14 Numerical maximization of the likelihood function
Hamilton Ch 5
Goffe et al (1994)
Slides and MatLab Code from class

Lecture 15 Presentations of Homework III

Lecture 16 Introduction to Bayesian estimation
Eddy (2004) An and Schorfheide (2007)
Slides

Lecture 17 Bayesian estimation of DSGE models
Slides and MatLab Code used for slides

Lecture 18 Baysian model averaging and model comparison
Wright (2009) and Hoeting et al (1999)
Slides

Lecture 19 Presentations of Homework IV