A Composite Likelihood Approach for Dynamic Structural Models

Bibliographic Data

ID9719974
AuthorsFabio Canova (0000-0002-8782-4787, BI Norwegian Business School, CAMP & CEPR, Norway), Christian Matthes (Indiana University, corresponding author)
Year2021
Volume131
Issue638
Pages2447-2477
Publication date2021-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueThe Economic Journal (JOURNAL)
Journal identifiersISSN: 0013-0133 • E-ISSN: 1468-0297
PublisherOxford University Press (PUBLISHER • GB)
DOI10.1093/ej/ueab004
OpenAlexW3122481339
LanguageEN
Citations received2
References cited38

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

Algorithm · Construct (python library · Econometrics · Epistemology · Estimation theory · Estimator · Intuition · Likelihood function · Mathematical optimization · Maximum likelihood · Quasi-maximum likelihood · Statistics · Computer Science · Financial Risk and Volatility Modeling · Forecasting Techniques and Applications · Mathematics · Monetary Policy and Economic Impact

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