Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models

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Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models

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Chris M Strickland ()
Gael Martin ()
Catherine S Forbes ()

Additional information is available for the following registered author(s):

Gael Margaret Martin

Abstract

The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for two non-Gaussian state space models is examined. Specifically, focus is given to particular forms of the stochastic conditional duration (SCD) model and the stochastic volatility (SV) model, with four alternative parameterisations of each model considered. A controlled experiment using simulated data reveals that relationships exist between the simulation efficiency of the MCMC sampler, the magnitudes of the population parameters and the particular parameterisation of the state space model. Results of an empirical analysis of two separate transaction data sets for the SCD model, as well as equity and exchange rate data sets for the SV model, are also reported. Both the simulation and empirical results reveal that substantial gains in simulation efficiency can be obtained from simple reparameterisations of both types of non-Gaussian state space models.

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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 22/06.

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Length: 31 pages
Date of creation: Dec 2006
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Handle: RePEc:msh:ebswps:2006-22

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Article
- Strickland, Chris M. & Martin, Gael M. & Forbes, Catherine S., 2008. "Parameterisation and efficient MCMC estimation of non-Gaussian state space models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2911-2930, February. [Downloadable!] (restricted)

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
G1 - Financial Economics - - General Financial Markets

This paper has been announced in the following NEP Reports:

NEP-ALL-2006-12-22 (All new papers)
NEP-ECM-2006-12-22 (Econometrics)
NEP-ETS-2006-12-22 (Econometric Time Series)
NEP-MST-2006-12-22 (Market Microstructure)

References listed on IDEAS
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.:

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Other versions:
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Other versions:
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repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
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Other versions:
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Other versions:
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