Bayesian Estimation of DSGE Models: Is the Workhorse Model Identified?
AbstractKoop, Pesaran and Smith (2011) suggest a simple diagnostic indicator for the Bayesian estimation of the parameters of a DSGE model. They show that, if a parameter is well identified, the precision of the posterior should improve as the (artificial) data size T increases, and the indicator checks the speed at which precision improves. It does not require any additional programming; a researcher just needs to generate artificial data and estimate the model with different T. Applying this to Smets and Wouters'(2007) medium size US model, we find that while exogenous shock processes are well identified, most of the parameters in the structural equations are not.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Koc University-TUSIAD Economic Research Forum in its series Koç University-TUSIAD Economic Research Forum Working Papers with number 1205.
Length: 24 pages
Date of creation: Feb 2012
Date of revision:
Bayesian Estimation; Dynamic stochastic general equilibrium models; Identification.;
Other versions of this item:
- Evren Caglar & Jagjit S. Chadha & Katsuyuki Shibayama, 2011. "Bayesian Estimation of DSGE models: Is the Workhorse Model Identified?," Studies in Economics 1125, Department of Economics, University of Kent.
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-02-20 (All new papers)
- NEP-CBA-2012-02-20 (Central Banking)
- NEP-DGE-2012-02-20 (Dynamic General Equilibrium)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Agostino Consolo & Carlo A. Favero & Alessia Paccagnini, 2007.
"On the Statistical Identification of DSGE Models,"
324, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Iskrev, Nikolay, 2008. "Evaluating the information matrix in linearized DSGE models," Economics Letters, Elsevier, vol. 99(3), pages 607-610, June.
- Nikolay Iskrev, 2009.
"Local Identification in DSGE Models,"
w200907, Banco de Portugal, Economics and Research Department.
- Marco Ratto, 2008. "Analysing DSGE Models with Global Sensitivity Analysis," Computational Economics, Society for Computational Economics, vol. 31(2), pages 115-139, March.
- Nikolay Iskrev, 2010. "Parameter identification in Dynamic Economic models," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
- Marco Ratto & Werner Roeger, 2005. "An estimated open-economy model for the EURO area," Computing in Economics and Finance 2005 84, Society for Computational Economics.
- Andrle, Michal, 2010. "A note on identification patterns in DSGE models," Working Paper Series 1235, European Central Bank.
- Sims, Christopher A, 2002.
"Solving Linear Rational Expectations Models,"
Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
- Christopher Sims, 2001. "Matlab Code for Solving Linear Rational Expectations Models," QM&RBC Codes 11, Quantitative Macroeconomics & Real Business Cycles.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sumru Oz).
If references are entirely missing, you can add them using this form.