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.
|Date of creation:||2012|
|Contact details of provider:|| Postal: Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA|
Web page: http://www.EconomicDynamics.org/
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- Renate Meyer & David A. Fournier & Andreas Berg, 2003. "Stochastic volatility: Bayesian computation using automatic differentiation and the extended Kalman filter," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 408-420, December.
- Martin M. Andreasen, 2010. "Non-linear DSGE Models and The Optimized Particle Filter," CREATES Research Papers 2010-05, Department of Economics and Business Economics, Aarhus University.
- Flury, Thomas & Shephard, Neil, 2011.
"Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models,"
Cambridge University Press, vol. 27(05), pages 933-956, October.
- Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," OFRC Working Papers Series 2008fe32, Oxford Financial Research Centre.