Estimating Time-Varying DSGE Models Using Minimum Distance Methods

Liudas Giraitis, George Kapetanios, Konstantinos Theodoridis and Tony Yates
Additional contact information
Liudas Giraitis: Queen Mary University of London
Tony Yates: University of Bristol and Centre for Macroeconomics

No 768, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: Following Giraitis, Kapetanios, and Yates (2014b), this paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the data set constructed by Smets and Wouters (2007). We apply an indirect inference method to map from this TV VAR to time variation in implied Dynamic Stochastic General Equilibrium (DSGE) parameters. We find that many parameters change substantially, particularly those defining nominal rigidities, habits and investment adjustment costs. In contrast to the 'Great Moderation' literature our monetary policy parameter estimates suggest that authorities tried to deliver a low and stable inflation from 1975 onwards, however, the severe adverse supply shocks in the 70s could have caused these policies to fail.

Keywords: DSGE; Structural change; Kernel estimation; Time-varying VAR; Monetary policy shocks (search for similar items in EconPapers)
JEL-codes: C14 C18 E52 E61 E66 (search for similar items in EconPapers)
Date: 2015-12-20
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.qmul.ac.uk/sef/media/econ/research/wor ... 2015/items/wp768.pdf (application/pdf)

Related works:
Working Paper: Estimating time-varying DSGE models using minimum distance methods (2014)
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:qmw:qmwecw:768

Access Statistics for this paper

More papers in Working Papers from Queen Mary University of London, School of Economics and Finance Contact information at EDIRC.
Bibliographic data for series maintained by Nicholas Owen ( this e-mail address is bad, please contact ).