and Econometrics is the first course in the sequence of graduate
courses in econometrics. The objective of the course is to familiarize students
with the basic methods of econometrics and its statistical foundations. The classes cover the usual
topics of a graduate econometrics course (multiple regression, asymptotic
theory, generalized least squares estimation and instrumental variables) and discusses applications.
The course also covers an introduction to time series.
Study guide and suggestions for exercises
Section leaders: Francesco Amodio, Gene Ambrocio and Dimitry Khametshin
Sections: Mondays from 9:00 to 10:30
Redings and other material:
we really know what makes us healthy?, The New York Times, Septembre
Comment: Live long? Die
young? Answer isn't just in genes.
Guesses and Hype Give Way to Data in
Study of Education, The New York Times, September 2, 2013
Athey et al (2007), What
does performance in Graduate School predict? Graduate economic education
and student outcomes, American Economic Review.
Fama and French (2004), The capital asset pricing model: theory and evidence, Journal of Economic Perspectives.
Ball (2001), Another look at the long run money demand, Journal of Monetary Economics.
Hill, R. (2011), Hedonic price indices for housing, OECD.
Blanchard and Leigh (2012), Are we underestimating short term fiscal multipliers? World Economic Outook October 2013, Box 1.1. Data.
ECB (2012), Comment on the IMF Box 1.1, Monthly Bulletin, december 2012.
Blanchard and Leigh (2013), Growth forecast errors and fiscal multipliers, American Economic Review Papers and Proceedings, May.
BBVA Research (2013), Comment on Box 1.1 (sorry, in Spanish)
Simulation program for LLN and CLT
Sample Exam: Fall 2012
Introduction and methodological issues
Chapter 5. Generalized
Chapter 6. Heteroscedasticity. Simulation to check the small sample behavior of alternative corrections of
standard errors under heteroscedasticity
Chapter 7. Autocorrelation
Chapter 8. Instrumental
Shortened versions of the slides: GLS, Heteroscedasticity, Autocorrelation and Instrumental variables.
Problem set 1. Data from
PISA to solve empirical question of the problem set. Due: Monday, September 30.
Problem set 2. Datasets: capm.xls and mrw.zip. Due: Monday, October 7.
Problem set 3 (final version with only four compulsory questions). Dataset: phousing.dta.
Due: Monday, October 14.
Problem set 4. Datasets: caltest.dta and wages. Appendix STAR. Due: Monday, October 21.
Problem set 5. Due: Monday, October 28.
Problem set 6. Dataset for question 1. Due: Monday November 4.
Problem set 7 (not compulsory). Due: Monday, November 11.