A New Approach to Drawing States in State Space Models
AbstractWe introduce a new method for drawing state variables in Gaussian state space models from their conditional distribution given parameters and observations. Unlike standard methods, our method does not involve Kalman filtering. We show that for some important cases, our method is computationally more efficient than standard methods in the literature. We consider two applications of our method.
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Bibliographic InfoPaper provided by Centre interuniversitaire de recherche en économie quantitative, CIREQ in its series Cahiers de recherche with number 07-2007.
Length: 26 pages
Date of creation: 2007
Date of revision:
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More information through EDIRC
State space models; Stochastic volatility; Count data;
Other versions of this item:
- William J. McCausland & Shirley Miller & Denis Pelletier, 2007. "A New Approach to Drawing States in State Space Models," Working Paper Series 014, North Carolina State University, Department of Economics, revised Aug 2007.
- McCAUSLAND, William J. & MILLER, Shirley & PELLETIER, Denis, 2007. "A New Approach to Drawing States in State Space Models," Cahiers de recherche 2007-06, Universite de Montreal, Departement de sciences economiques.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996.
"Stochastic Volatility: Likelihood Inference And Comparison With Arch Models,"
- Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 361-93, July.
- Sangjoon Kim & Neil Shephard, 1994. "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers 3., Economics Group, Nuffield College, University of Oxford.
- Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
- 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.
- 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, 2008. "The Hessian Method (Highly Efficient State Smoothing, In a Nutshell)," Cahiers de recherche 2008-03, Universite de Montreal, Departement de sciences economiques.
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