IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v15y1994i2p183-202.html
   My bibliography  Save this article

Data Augmentation And Dynamic Linear Models

Author

Listed:
  • Sylvia Frühwirth‐Schnatter

Abstract

. We define a subclass of dynamic linear models with unknown hyperpara‐meter called d‐inverse‐gamma models. We then approximate the marginal probability density functions of the hyperparameter and the state vector by the data augmentation algorithm of Tanner and Wong. We prove that the regularity conditions for convergence hold. For practical implementation a forward‐filtering‐backward‐sampling algorithm is suggested, and the relation to Gibbs sampling is discussed in detail.

Suggested Citation

  • Sylvia Frühwirth‐Schnatter, 1994. "Data Augmentation And Dynamic Linear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(2), pages 183-202, March.
    • Handle: RePEc:bla:jtsera:v:15:y:1994:i:2:p:183-202
    DOI: 10.1111/j.1467-9892.1994.tb00184.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9892.1994.tb00184.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9892.1994.tb00184.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ritschl, Albrecht & Sarferaz, Samad & Uebele, Martin, 2008. "The U.S. business cycle, 1867-1995: dynamic factor analysis vs. reconstructed national accounts," Economic History Working Papers 22305, London School of Economics and Political Science, Department of Economic History.
    2. Belongia, Michael T. & Ireland, Peter N., 2016. "The evolution of U.S. monetary policy: 2000–2007," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 78-93.
    3. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2016. "Efficient Gibbs sampling for Markov switching GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 37-57.
    4. Martin Feldkircher & Florian Huber, 2018. "Unconventional U.S. Monetary Policy: New Tools, Same Channels?," JRFM, MDPI, vol. 11(4), pages 1-31, October.
    5. Ľuboš Pástor & Robert F. Stambaugh, 2009. "Predictive Systems: Living with Imperfect Predictors," Journal of Finance, American Finance Association, vol. 64(4), pages 1583-1628, August.
    6. Mauro Bernardi & Ghislaine Gayraud & Lea Petrella, 2013. "Bayesian inference for CoVaR," Papers 1306.2834, arXiv.org, revised Nov 2013.
    7. Ataman, B.M., 2007. "Managing brands," Other publications TiSEM 462dcbba-2ac1-46d1-a61c-f, Tilburg University, School of Economics and Management.
    8. Arthur Korteweg & Morten Sorensen, 2012. "Estimating Loan-to-Value and Foreclosure Behavior," NBER Working Papers 17882, National Bureau of Economic Research, Inc.
    9. C. Glocker & G. Sestieri & P. Towbin, 2017. "Time-varying fiscal spending multipliers in the UK," Working papers 643, Banque de France.
    10. Dueker, Michael, 2006. "Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models," Economics Letters, Elsevier, vol. 93(1), pages 58-62, October.
    11. van Heerde, H.J. & Helsen, K. & Dekimpe, M.G., 2005. "Managing Product-Harm Crises," ERIM Report Series Research in Management ERS-2005-044-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    12. Altug, Sumru & Çakmaklı, Cem & Demircan, Hamza, 2018. "Modeling of Economic and Financial Conditions for Nowcasting and Forecasting Recessions: A Unified Approach," CEPR Discussion Papers 13171, C.E.P.R. Discussion Papers.
    13. Michael B. Gordy & Pawel J. Szerszen, 2015. "Bayesian Estimation of Time-Changed Default Intensity Models," Finance and Economics Discussion Series 2015-2, Board of Governors of the Federal Reserve System (U.S.).
    14. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    15. Wu, Jingtao, 2009. "Three Bayesian econometric studies on forecast evaluation," ISU General Staff Papers 200901010800002984, Iowa State University, Department of Economics.
    16. Matthias Held & Marcel Omachel, 2014. "An Efficient Parallel Simulation Method for Posterior Inference on Paths of Markov Processes," FEMM Working Papers 140010, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    17. Michael Ho & Jack Xin, 2016. "Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps," Papers 1602.02185, arXiv.org, revised Apr 2016.
    18. Massimiliano De Santis, 2005. "Movements in the Equity Premium: Evidence from a Bayesian Time-Varying VAR," Money Macro and Finance (MMF) Research Group Conference 2005 62, Money Macro and Finance Research Group.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jtsera:v:15:y:1994:i:2:p:183-202. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.