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A Composite Likelihood Approach for Dynamic Structural Models
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.
Cite this item
Fabio Canova & Christian Matthes, A Composite Likelihood Approach for Dynamic Structural Models, Federal Reserve Bank of Richmond, Working Paper 18-12, 23 Jul 2018.
- 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
Keywords: dynamic structural models; composite likelihood; identification; singularity; large scale models; panel data
This item with handle RePEc:fip:fedrwp:18-12
is also listed on EconPapers
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