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A Composite Likelihood Approach for Dynamic Structural Models

Author

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  • 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. 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 Paper 18-12, Federal Reserve Bank of Richmond.
  • Handle: RePEc:fip:fedrwp:18-12
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    References listed on IDEAS

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    More about this item

    Keywords

    dynamic structural models; composite likelihood; identification; singularity; large scale models; panel data;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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