Minimum Distance Estimation and Testing of DSGE Models from Structural VARs*

Patrick Fève, Julien Matheron and Jean-Guillaume Sahuc

Oxford Bulletin of Economics and Statistics, 2009, vol. 71, issue 6, 883-894

Abstract: The aim of this paper is to complement the minimum distance estimation–structural vector autoregression approach when the weighting matrix is not optimal. In empirical studies, this choice is motivated by stochastic singularity or collinearity problems associated with the covariance matrix of impulse response functions. Consequently, the asymptotic distribution cannot be used to test the economic model's fit. To circumvent this difficulty, we propose a simple simulation method to construct critical values for the test statistics. An empirical application with US data illustrates the proposed method.

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://doi.org/10.1111/j.1468-0084.2009.00562.x

Related works:
Working Paper: Minimum Distance Estimation and Testing of DSGE Models from Structural VARs (2009)
Working Paper: Minimum Distance Estimation and Testing of DSGE Models from Structural VARs (2009)
Working Paper: Minimum Distance Estimation and Testing of DSGE Models from Structural VARs (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:obuest:v:71:y:2009:i:6:p:883-894

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0305-9049

Access Statistics for this article

Oxford Bulletin of Economics and Statistics is currently edited by Christopher Adam, Anindya Banerjee, Christopher Bowdler, David Hendry, Adriaan Kalwij, John Knight and Jonathan Temple

More articles in Oxford Bulletin of Economics and Statistics from Department of Economics, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().