Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods
In recent years state space models, particularly the linear Gaussian version, have become the standard framework for analyzing macro-economic and financial data. However, many theoretically motivated models imply non-linear or non-Gaussian specifications or both. Existing methods for estimating such models are computationally intensive, and often cannot be applied to models with more than a few states. Building upon recent developments in precision-based algorithms, we propose a general approach to estimating high-dimensional non-linear non-Gaussian state space models. The baseline algorithm approximates the conditional distribution of the states by a multivariate Gaussian or t density, which is then used for posterior simulation. We further develop this baseline algorithm to construct more sophisticated samplers with attractive properties: one based on the accept-reject Metropolis-Hastings (ARMH) algorithm, and another adaptive collapsed sampler inspired by the cross-entropy method. To illustrate the proposed approach, we investigate the effect of the zero lower bound of interest rate on monetary transmission mechanism.
|Date of creation:||2012|
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- McCAUSLAND, William, 2008.
"The Hessian Method (Highly Efficient State Smoothing, In a Nutshell),"
Cahiers de recherche
2008-03, Universite de Montreal, Departement de sciences economiques.
- McCAUSLAND, William, 2008. "The Hessian Method (Highly Efficient State Smoothing, In a Nutshell)," Cahiers de recherche 03-2008, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.
- 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.
- Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(05), pages 933-956, October.
- Neil Shephard & Thomas Flury, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," Economics Series Working Papers 413, University of Oxford, Department of Economics.
- Chris M. Strickland & Catherine S. Forbes & Gael M. Martin, 2003.
"Bayesian Analysis of the Stochastic Conditional Duration Model,"
Monash Econometrics and Business Statistics Working Papers
14/03, Monash University, Department of Econometrics and Business Statistics.
- Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006. "Bayesian analysis of the stochastic conditional duration model," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2247-2267, May.
- Iwata, Shigeru & Wu, Shu, 2006. "Estimating monetary policy effects when interest rates are close to zero," Journal of Monetary Economics, Elsevier, vol. 53(7), pages 1395-1408, October.
- repec:rim:rimwps:24-08 is not listed on IDEAS
- Sylvia FrüHwirth-Schnatter & Helga Wagner, 2006. "Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling," Biometrika, Biometrika Trust, vol. 93(4), pages 827-841, December.
- Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392.
- Smith M. & Kohn R., 2002. "Parsimonious Covariance Matrix Estimation for Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1141-1153, December.
- Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
- Reifschneider, David & Willams, John C, 2000. "Three Lessons for Monetary Policy in a Low-Inflation Era," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(4), pages 936-66, November.
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
- Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2004.
"Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood,"
PIER Working Paper Archive
04-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Juan F. Rubio-Ramirez & Jesus Fernández-Villaverde, 2005. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 891-910.
- Jesús Fernández-Villaverde & Juan Francisco Rubio-Ramírez, 2004. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," FRB Atlanta Working Paper 2004-3, Federal Reserve Bank of Atlanta.
- David L. Reifschneider & John C. Williams, 1999.
"Three lessons for monetary policy in a low inflation era,"
Finance and Economics Discussion Series
1999-44, Board of Governors of the Federal Reserve System (U.S.).
- David L. Reifschneider & John C. Williams, 2000. "Three lessons for monetary policy in a low-inflation era," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, pages 936-978.
- J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
- Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342.
- Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
- Siddhartha Chib & Ivan Jeliazkov, 2005. "Accept-reject Metropolis-Hastings sampling and marginal likelihood estimation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(1), pages 30-44.
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