This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Time series analysis of non-gaussian observations based on state space models from both classical and bayesian perspectives

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Durbin, J.
Koopman, S.J. (Tilburg University, Center for Economic Research)

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

Abstract

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian disturbances for the state equation as well as for the observation equation are considered. Methods for estimating conditional and posterior means of functions of the state vector given the observations, and the mean square errors of their estimates, are developed. These methods are extended to cover the estimation of conditional and posterior densities and distribution functions. Choice of importance sampling densities and antithetic variables is discussed. The techniques work well in practice and are computationally e cient. Their use is illustrated by applying to a univariate discrete time series, a series with outliers and a volatility series.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://arno.uvt.nl/show.cgi?fid=3818
File Format: application/pdf
File Function:
Download Restriction: no
File URL: http://arno.uvt.nl/show.cgi?fid=3819
File Format: application/postscript
File Function:
Download Restriction: no

Publisher Info
Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 142.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 1998
Date of revision:
Handle: RePEc:dgr:kubcen:1998142

Contact details of provider:
Web page: http://center.uvt.nl

For technical questions regarding this item, or to correct its listing, contact: (Corry Stuyts).

Related research
Keywords:

Other versions of this item:

Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

This paper has been announced in the following NEP Reports:

Cited by:
(explanations, 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.)
  1. 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. [Downloadable!]
    Other versions:
  2. B.P.M. McCabe & G.M. Martin, 2003. "Coherent Predictions of Low Count Time Series," Monash Econometrics and Business Statistics Working Papers 8/03, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  3. KIANI, Khurshid M., 2007. "Determination Of Volatility And Mean Returns: An Evidence From An Emerging Stock Market," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 4(1), pages 103-118. [Downloadable!]
  4. J. Huston McCulloch & Prasad V. Bidarkota, 2003. "Signal Extraction can Generate Volatility Clusters," Computing in Economics and Finance 2003 59, Society for Computational Economics. [Downloadable!]
  5. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute. [Downloadable!]
    Other versions:
  6. Prasad Bidarkota & Khurshid M. Kiani, 2004. "No Predictable Components in G7 Stock Returns," Working Papers 0416, Florida International University, Department of Economics. [Downloadable!]
  7. J. Huston McCulloch & Prasad V. Bidarkota, 2002. "Signal Extraction Can Generate Volatility Clusters From IID Shocks," Working Papers 02-04, Ohio State University, Department of Economics. [Downloadable!]
  8. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  9. Eugenie Hol & Siem Jan Koopman, 2000. "Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility," Tinbergen Institute Discussion Papers 00-104/4, Tinbergen Institute. [Downloadable!]
  10. Jun Yu & Zhenlin Yang & Xibin Zhang, 2002. "A Class of Nonlinear Stochastic Volatility Models and Its Implications on Pricing Currency Options," Monash Econometrics and Business Statistics Working Papers 17/02, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
    Other versions:
  11. Junji Shimada & Yoshihiko Tsukuda, 2004. "Estimation of Stochastic Volatility Models : An Approximation to the Nonlinear State Space," Econometric Society 2004 Far Eastern Meetings 611, Econometric Society. [Downloadable!]
  12. Siem Jan Koopman & Kai Ming Lee, 2008. "Seasonality with Trend and Cycle Interactions in Unobserved Components Models," Tinbergen Institute Discussion Papers 08-028/4, Tinbergen Institute. [Downloadable!]
    Other versions:
  13. Harvey, A. & Koopman, S.J., 1999. "Signal extraction and the formulation of unobserved components models," Discussion Paper 44, Tilburg University, Center for Economic Research. [Downloadable!]
    Other versions:
  14. Roberto Casarin & Domenico sartore, 2008. "Matrix-State Particle Filter for Wishart Stochastic Volatility Processes," Working Papers 0816, University of Brescia, Department of Economics. [Downloadable!]
  15. Prasad Bidarkota & J. Huston McCulloch, 2003. "News or Noise? Signal Extraction Can Generate Volatility Clusters From IID Shocks," Working Papers 0304, Florida International University, Department of Economics. [Downloadable!]
  16. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge. [Downloadable!]
  17. Victor Guerrero, 2005. "Restricted estimation of an adjusted time series: application to Mexico's industrial production index," Journal of Applied Statistics, Taylor and Francis Journals, vol. 32(2), pages 157-177, March. [Downloadable!] (restricted)
  18. Siem Jan Koopman & Eugenie Hol Uspensky, 2000. "The Stochastic Volatility in Mean Model," Tinbergen Institute Discussion Papers 00-024/4, Tinbergen Institute. [Downloadable!]
  19. Prasad V. Bidarkota & Brice V. Dupoyet & J. Huston McCulloch, 2005. "Asset Pricing with Incomplete Information under Stable Shocks," Working Papers 0514, Florida International University, Department of Economics. [Downloadable!]
  20. Mikkelsen, Peter, 2001. "MCMC Based Estimation of Term Structure Models," Finance Working Papers 01-7, University of Aarhus, Aarhus School of Business, Department of Business Studies. [Downloadable!]
  21. Prasad Bidarkota, 2003. "Comparison of Two Alternative Approaches to Modeling Level Shifts in the Presence of Outliers," Working Papers 0307, Florida International University, Department of Economics. [Downloadable!]
Statistics
Access and download statistics

Did you know? It is the publishers that input data about their publications, as there is no staff at RePEc.

This page was last updated on 2009-11-25.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.