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Data Augmentation And Dynamic Linear Models

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  • 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
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    Cited by:

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    3. C. Glocker & G. Sestieri & P. Towbin, 2017. "Time-varying fiscal spending multipliers in the UK," Working papers 643, Banque de France.
    4. 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.
    5. 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.
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    8. 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.
    9. 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.
    10. 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.
    11. 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.
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    14. 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.).
    15. Ataman, B.M., 2007. "Managing brands," Other publications TiSEM 462dcbba-2ac1-46d1-a61c-f, Tilburg University, School of Economics and Management.
    16. Wu, Jingtao, 2009. "Three Bayesian econometric studies on forecast evaluation," ISU General Staff Papers 200901010800002984, Iowa State University, Department of Economics.
    17. 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.
    18. Arthur Korteweg & Morten Sorensen, 2012. "Estimating Loan-to-Value and Foreclosure Behavior," NBER Working Papers 17882, National Bureau of Economic Research, Inc.

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