Lawrence J. Christiano, Mathias Trabandt, and Karl Walentin
CQER Working Paper 10-02
Monetary DSGE models are widely used because they fit the data well and can be used to address important monetary policy questions. We provide a selective review of these developments. Policy analysis with DSGE models requires using data to assign numerical values to model parameters. The paper describes and implements Bayesian moment matching and impulse response matching procedures for this purpose.
JEL classification: E2, E3, E5, J6
Key words: Taylor rule, labor supply, boom, output gap, unemployment, Bayesian inference, vector autoregression, posterior distribution
The authors are grateful for advice from Michael Woodford, for comments from Volker Wieland, and for assistance from Daisuke Ikeda and Matthias Kehrig. The views expressed here are the authors' and not necessarily those of the European Central Bank, Sveriges Riksbank, the Federal Reserve Bank of Atlanta, or the Federal Reserve System. Any remaining errors are the authors' responsibility.
Please address questions regarding content to Lawrence J. Christiano, Department of Economics, Northwestern University, 2001 Sheridan Road, Evanston, IL 60208 and NBER, 847-491-8231, l‑email@example.com; Mathias Trabandt, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany, and Sveriges Riksbank, 49-69-1344-6321, firstname.lastname@example.org; or Karl Walentin, Sveriges Riksbank, 103 37 Stockholm, Sweden, 46-8-787-0491, email@example.com.
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