Christopher A. Sims will give the Tinbergen Econometrics Lectures in 2015. These lectures are a joint event with the Econometric Institute of the Erasmus School of Economics.
Chris Sims is the John F. Sherrerd ’52 University Professor of Economics at Princeton University, United States. Together with Thomas Sargent, Sims won the Nobelprize in economics in 2011.
These lectures start with a discussion of standard structural Vector AutoRegressive (VAR) Models. The usual identification strategies where use is made of information on contemporaneous coefficients, on long-run restrictions, and on “sign restrictions” is covered. Further, identification through heteroskedasticity and Markov switching is treated. Panel VAR’s are covered with special attention on identification using heteroskedasticity and hierarchical modeling.
The topics are summarized and given as: setting priors, handling initial conditions, identification, and testing restrictions.
- Domenico Giannone & Michèle Lenza & Giorgio E. Primiceri, 2015. “Prior Selection for Vector Autoregressions,” Review of Economics and Statistics, (forthcoming). Also available as Working Papers ECARES 2012-002, ULB — Universite Libre de Bruxelles.
- Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. “Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,” Review of Economic Studies, vol. 77 (2), pages 665-696.
- Marco Del Negro & Frank Schorfheide, 2004. “Priors from General Equilibrium Models for VARS,” International Economic Review, vol. 45 (2), pages 643-673.
- Del Negro, Marco & Schorfheide, Frank & Smets, Frank & Wouters, Rafael, 2007. “On the Fit and Forecasting Performance of New Keynesian Models,” Invited Journal of Business and Economic Statistics Lecture, vol. 2 (2), pages 123-143. Also available as ECB Working Paper, no. 491, June 2005.
- Sims, Christopher A., 2007, “Comment (on Del Negro, Schorfheide, Smets and Wouters),” Journal of Business and Economic Statistics (invited paper), vol. 25 (2), pages 152-154.
Comment on a paper presented at the August 2006 Joint Statistical Meetings in Seattle and published in the Journal of Business and Economic Statistics. The paper extends the Del Negro and Schorfheide approach to connecting beliefs about parameters of a behavioral DSGE to a prior on coefficients of a structural VAR. The comments argue that and explain why this is a good idea, then suggest some directions in which the particular methods used in the paper might be improved.
- Christiane Baumeister & James D. Hamilton, 2014. “Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information,” NBER Working Papers, 20741, National Bureau of Economic Research, Inc. Program(s): EFGME
Introductory Lectures on Markov Chain Monte Carlo and Kalman Filters on June 15 and 16
Participants are assumed to have a working knowledge of Bayesian Econometrics and Markov chain Monte Carlo methods and Kalman Filtering techniques. These topics are covered in the two day introductory lectures, including afternoon lab session, presented by prof. Herman van Dijk, preceding the lectures by prof. Sims.