Estimating Nonlinear Economic Models Using Surrogate Transitions
We propose a novel combination of algorithms for jointly estimating parameters and unobservable states in a nonlinear state space system. We exploit an approximation to the marginal likelihood to guide a Particle Marginal Metropolis-Hastings algorithm. While this algorithm seemingly targets reduced dimension marginal distributions, it draws from a joint distribution of much higher dimension. The algorithm is demonstrated on a stochastic volatility model and a Real Business Cycle model with robust preferences.
Year of publication: |
2012
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Authors: | Smith, Matthew |
Institutions: | Society for Economic Dynamics - SED |
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