Note on the role of natural condition of control in the estimation of DSGE models
AbstractThis paper is written by authors from technical and economic fields, motivated to find a common language and views on the problem of the optimal use of information in model estimation. The center of our interest is the natural condition of control -- a common assumption in the Bayesian estimation in technical sciences, which may be violated in economic applications. In estimating dynamic stochatic general equilibrium (DSGE) models, typically only a subset of endogenous variables are treated as measured even if additional data sets are available. The natural condition of control dictates the exploitation of all available information, which improves model adaptability and estimates efficiency. We illustrate our points on a basic RBC model.
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 Federal Reserve Bank of Kansas City in its series Research Working Paper with number RWP 11-03.
Date of creation: 2011
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-10-09 (All new papers)
- NEP-DGE-2011-10-09 (Dynamic General Equilibrium)
- NEP-ECM-2011-10-09 (Econometrics)
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.:
- Frank Schorfheide & Keith Sill & Maxym Kryshko, 2009.
"DSGE Model-Based Forecasting of Non-modelled Variables,"
NBER Working Papers
14872, National Bureau of Economic Research, Inc.
- Schorfheide, Frank & Sill, Keith & Kryshko, Maxym, 2010. "DSGE model-based forecasting of non-modelled variables," International Journal of Forecasting, Elsevier, vol. 26(2), pages 348-373, April.
- Frank Schorfheide & Keith Sill & Maxym Kryshko, 2008. "DSGE model-based forecasting of non-modelled variables," Working Papers 08-17, Federal Reserve Bank of Philadelphia.
- Pablo A. Guerron-Quintana, 2010.
"What you match does matter: the effects of data on DSGE estimation,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 25(5), pages 774-804.
- Pablo A. Guerron, 2007. "What You Match Does Matter: The Effects of Data on DSGE Estimation," Working Paper Series 012, North Carolina State University, Department of Economics.
- Boivin, J. & Giannoni, M., 2007.
"DSGE Models in a Data-Rich Environment,"
162, Banque de France.
- Jean Boivin & Marc Giannoni, 2006. "DSGE Models in a Data-Rich Environment," NBER Working Papers 12772, National Bureau of Economic Research, Inc.
- Jean Boivin & Marc Giannoni, 2006. "DSGE Models in a Data-Rich Environment," NBER Technical Working Papers 0332, National Bureau of Economic Research, Inc.
- Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
- King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lu Dayrit).
If references are entirely missing, you can add them using this form.