A Composite Likelihood Approach for Dynamic Structural Models

41 Pages Posted: 20 Aug 2018 Last revised: 21 Feb 2019

See all articles by Fabio Canova

Fabio Canova

Universitat Pompeu Fabra - Department of Economics and Business (DEB); European University Institute - Robert Schuman Centre for Advanced Studies (RSCAS); University of Southampton - Division of Economics; Centre for Economic Policy Research (CEPR)

Christian Matthes

Federal Reserve Banks - Federal Reserve Bank of Richmond

Multiple version iconThere are 2 versions of this paper

Date Written: 2018-07-23

Abstract

We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.

Keywords: dynamic structural models, composite likelihood, identification, singularity, large scale models, panel data

JEL Classification: C10, E27, E32

Suggested Citation

Canova, Fabio and Matthes, Christian, A Composite Likelihood Approach for Dynamic Structural Models (2018-07-23). FRB Richmond Working Paper No. 18-12. Available at SSRN: https://ssrn.com/abstract=3234198

Fabio Canova (Contact Author)

Universitat Pompeu Fabra - Department of Economics and Business (DEB) ( email )

Barcelona, 08005
Spain

European University Institute - Robert Schuman Centre for Advanced Studies (RSCAS) ( email )

Villa La Fonte, via delle Fontanelle 18
50016 San Domenico di Fiesole
Florence, Florence 50014
Italy

University of Southampton - Division of Economics

Southampton, SO17 1BJ
United Kingdom

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Christian Matthes

Federal Reserve Banks - Federal Reserve Bank of Richmond ( email )

P.O. Box 27622
Richmond, VA 23261
United States

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