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A composite likelihood approach for dynamic structural models

Author

Listed:
  • Fabio Canova

  • Christian Matthes

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 and formally justi?es existing practices. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.

Suggested Citation

  • Fabio Canova & Christian Matthes, 2018. "A composite likelihood approach for dynamic structural models," Working Papers No 10/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    • Handle: RePEc:bny:wpaper:0068
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    References listed on IDEAS

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    Cited by:

    1. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    2. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.

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