Stochastic model specification search for Gaussian and partial non-Gaussian state space models
AbstractModel 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 154 (2010)
Issue (Month): 1 (January)
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconom
Auxiliary mixture sampling Bayesian econometrics Non-centered parameterization Markov chain Monte Carlo Variable selection;
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.:
- 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,"
1998-142, Tilburg University, Center for Economic Research.
- J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
- 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.
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- 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.
- 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.
- 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.
- Smith, M. & Kohn, R., .
"Nonparametric Regression using Bayesian Variable Selection,"
Statistics Working Paper
_009, Australian Graduate School of Management.
- Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
- 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.
- 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.
- 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.
- 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.
- 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.
- Miguel Belmonte & Gary Koop & Dimitris Korobilis, 2011.
"Hierarchical Shrinkage in Time-Varying Parameter Models,"
1137, University of Strathclyde Business School, Department of Economics.
- Miguel A.G. Belmonte & Gary Koop & Dimitris Korobilis, 2014. "Hierarchical Shrinkage in Time‐Varying Parameter Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 80-94, 01.
- Miguel, Belmonte & Gary, Koop & Dimitris, Korobilis, 2011. "Hierarchical shrinkage in time-varying parameter models," MPRA Paper 31827, University Library of Munich, Germany.
- Miguel A. G. Belmonte & Gary Koop & Dimitris Korobilis, 2011. "Hierarchical Shrinkage in Time-Varying Parameter Models," Working Paper Series 35_11, The Rimini Centre for Economic Analysis.
- BELMONTE, Miguel A.G. & KOOP, Gary & KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage in time-varying parameter models," CORE Discussion Papers 2011036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- 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.
- 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.
- Grassi, S. & Proietti, T., 2014. "Characterising economic trends by Bayesian stochastic model specification search," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 359-374.
- Grassi, Stefano & Proietti, Tommaso, 2010. "Characterizing economic trends by Bayesian stochastic model specifi cation search," MPRA Paper 22569, University Library of Munich, Germany.
- Stefano Grassi & Tommaso Proietti, 2010. "Characterizing economic trends by Bayesian stochastic model specification search," EERI Research Paper Series EERI_RP_2010_25, Economics and Econometrics Research Institute (EERI), Brussels.
- Korobilis, Dimitris, 2014.
"Data-based priors for vector autoregressions with drifting coefficients,"
53772, University Library of Munich, Germany.
- Dimitris Korobilis, 2014. "Data-based priors for vector autoregressions with drifting coefficients," Working Papers 2014_04, Business School - Economics, University of Glasgow.
- Tommaso, Proietti & Stefano, Grassi, 2010.
"Bayesian stochastic model specification search for seasonal and calendar effects,"
27305, University Library of Munich, Germany.
- Stefano Grassi & Tommaso Proietti, 2011. "Bayesian stochastic model specification search for seasonal and calendar effects," CREATES Research Papers 2011-08, School of Economics and Management, University of Aarhus.
- Marcio Laurini, 2011.
"Bayesian Factor Selection in Dynamic Term Structure Models,"
AccessEcon, vol. 31(3), pages 2167-2176.
- Márcio Laurini, 2011. "Bayesian Factor Selection in Dynamic Term Structure Models," IBMEC RJ Economics Discussion Papers 2011-02, Economics Research Group, IBMEC Business School - Rio de Janeiro.
- 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.
- Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.