A Composite Likelihood Approach for Dynamic Structural Models

Fabio Canova and Christian Matthes

The Economic Journal, 2021, vol. 131, issue 638, 2447-2477

Abstract: We explain how to use the composite likelihood function to ameliorate estimation, computational and inferential problems in dynamic stochastic general equilibrium models. We combine the information present in different models or data sets to estimate the parameters common across models. We provide intuition for why the methodology works and alternative interpretations of the estimators we construct and of the statistics we employ. We present a number of situations where the methodology has the potential to resolve well-known problems and to provide a justification for existing practices that pool different estimates. In each case, we provide an example to illustrate how the approach works and its properties in practice.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1093/ej/ueab004 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
Working Paper: A composite likelihood approach for dynamic structural models (2018)
Working Paper: A composite likelihood approach for dynamic structural models (2018)
Working Paper: A Composite Likelihood Approach for Dynamic Structural Models (2018)
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:oup:econjl:v:131:y:2021:i:638:p:2447-2477.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

The Economic Journal is currently edited by Francesco Lippi

More articles in The Economic Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press () and ().