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Stochastic model specification search for Gaussian and partial non-Gaussian state space models

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  • Frühwirth-Schnatter, Sylvia
  • Wagner, Helga
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    Abstract

    Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 154 (2010)
    Issue (Month): 1 (January)
    Pages: 85-100

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    Handle: RePEc:eee:econom:v:154:y:2010:i:1:p:85-100

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    Web page: http://www.elsevier.com/locate/jeconom

    Related research

    Keywords: Auxiliary mixture sampling Bayesian econometrics Non-centered parameterization Markov chain Monte Carlo Variable selection;

    References

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    1. Durbin, J. & Koopman, S.J.M., 1998. "Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives," Discussion Paper 1998-142, Tilburg University, Center for Economic Research.
    2. Sylvia Frühwirth-Schnatter, 2001. "Fully Bayesian Analysis of Switching Gaussian State Space Models," Annals of the Institute of Statistical Mathematics, Springer, vol. 53(1), pages 31-49, March.
    3. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
    4. Carmen Fernandez & E Ley & Mark F J Steel, 2004. "Benchmark priors for Bayesian models averaging," ESE Discussion Papers 66, Edinburgh School of Economics, University of Edinburgh.
    5. 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.
    6. Sylvia Fr�Hwirth-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.
    7. Smith, M. & Kohn, R., . "Nonparametric Regression using Bayesian Variable Selection," Statistics Working Paper _009, Australian Graduate School of Management.
    8. Claudia Czado & Peter Song, 2008. "State space mixed models for longitudinal observations with binary and binomial responses," Statistical Papers, Springer, vol. 49(4), pages 691-714, October.
    9. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
    10. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2008. "Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4608-4624, June.
    11. Shively, Thomas S. & Kohn, Robert, 1997. "A Bayesian approach to model selection in stochastic coefficient regression models and structural time series models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 39-52.
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    Cited by:
    1. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
    2. Miguel Belmonte & Gary Koop & Dimitris Korobilis, 2011. "Hierarchical Shrinkage in Time-Varying Parameter Models," Working Papers 1137, University of Strathclyde Business School, Department of Economics.
    3. Wolfgang Brunauer & Stefan Lang & Nikolaus Umlauf, 2010. "Modeling House Prices using Multilevel Structured Additive Regression," Working Papers 2010-19, Faculty of Economics and Statistics, University of Innsbruck.
    4. Stefano Grassi & Tommaso Proietti, 2011. "Characterizing economic trends by Bayesian stochastic model specification search," CREATES Research Papers 2011-16, School of Economics and Management, University of Aarhus.
    5. Korobilis, Dimitris, 2014. "Data-based priors for vector autoregressions with drifting coefficients," MPRA Paper 53772, University Library of Munich, Germany.
    6. Tommaso, Proietti & Stefano, Grassi, 2010. "Bayesian stochastic model specification search for seasonal and calendar effects," MPRA Paper 27305, University Library of Munich, Germany.
    7. Marcio Laurini, 2011. "Bayesian Factor Selection in Dynamic Term Structure Models," Economics Bulletin, AccessEcon, vol. 31(3), pages 2167-2176.
    8. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," CAMA Working Papers 2014-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.

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