A composite likelihood approach for dynamic structural models

In the new CAMP working paper 10/2018, Canova and Matthes describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. They propose a method that helps to solve these difficulties and provides parameter estimates and policy analyses that formally combine the information present in different models using a shrinkage-type procedure.

Canova and Matthes present examples indicating that a composite likelihood constructed using the information present in distinct models helps

  1. To ameliorate population and sample identification problems,
  2. To solve singularity problems,
  3. To produce more stable estimates of the parameters of large scale structural models
  4. To robustly the estimation of parameters appearing in multiple models and to rank models with different observables, and
  5. To combine information coming from different sources, frequencies, and levels of aggregation.

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