IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v64y2012i2p319-342.html
   My bibliography  Save this article

Bayesian estimation of a covariance matrix with flexible prior specification

Author

Listed:
  • Chih-Wen Hsu
  • Marick Sinay
  • John Hsu

Abstract

No abstract is available for this item.

Suggested Citation

  • Chih-Wen Hsu & Marick Sinay & John Hsu, 2012. "Bayesian estimation of a covariance matrix with flexible prior specification," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 319-342, April.
    • Handle: RePEc:spr:aistmt:v:64:y:2012:i:2:p:319-342
    DOI: 10.1007/s10463-010-0314-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10463-010-0314-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10463-010-0314-5?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shawn Ni & Dongchu Sun, 2005. "Bayesian Estimates for Vector Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 105-117, January.
    2. Tom Leonard & John Hsu & Kam-Wah Tsui & James Murray, 1994. "Bayesian and likelihood inference from equally weighted mixtures," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(2), pages 203-220, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hannart, Alexis & Naveau, Philippe, 2014. "Estimating high dimensional covariance matrices: A new look at the Gaussian conjugate framework," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 149-162.
    2. Oda, Hidemasa & Komaki, Fumiyasu, 2023. "Enriched standard conjugate priors and the right invariant prior for Wishart distributions," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    3. Soudeep Deb & Rishideep Roy & Shubhabrata Das, 2024. "Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1814-1834, September.
    4. Hein, Maren & Kurz, Peter & Steiner, Winfried J., 2019. "On the effect of HB covariance matrix prior settings: A simulation study," Journal of choice modelling, Elsevier, vol. 31(C), pages 51-72.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rangan Gupta & Moses M. Sichei, 2006. "A Bvar Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 74(3), pages 391-409, September.
    2. Karlsson, Sune & Mazur, Stepan, 2020. "Flexible Fat-tailed Vector Autoregression," Working Papers 2020:5, Örebro University, School of Business.
    3. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    4. Kociecki, Andrzej, 2012. "Orbital Priors for Time-Series Models," MPRA Paper 42804, University Library of Munich, Germany.
    5. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    6. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    7. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2014. "Bayesian analysis of dynamic factor models: An ex-post approach towards the rotation problem," Kiel Working Papers 1902, Kiel Institute for the World Economy (IfW Kiel).
    8. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    9. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    10. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pinter, Gabor, 2016. "VAR models with non-Gaussian shocks," LSE Research Online Documents on Economics 86238, London School of Economics and Political Science, LSE Library.
    11. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2016. "Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem," Journal of Econometrics, Elsevier, vol. 192(1), pages 190-206.
    12. T. Manouchehri & A. R. Nematollahi, 2019. "Periodic autoregressive models with closed skew-normal innovations," Computational Statistics, Springer, vol. 34(3), pages 1183-1213, September.
    13. Tamás Kiss & Hoang Nguyen & Pär Österholm, 2023. "Modelling Okun’s law: Does non-Gaussianity matter?," Empirical Economics, Springer, vol. 64(5), pages 2183-2213, May.
    14. Chen, Jing & Buckland, Roger & Williams, Julian, 2011. "Regulatory changes, market integration and spillover effects in the Chinese A, B and Hong Kong equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 19(4), pages 351-373, September.
    15. Lee, Namgil & Choi, Hyemi & Kim, Sung-Ho, 2016. "Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 250-276.
    16. Karling, Maicon J. & Lopes, Sílvia R.C. & de Souza, Roberto M., 2023. "Multivariate α-stable distributions: VAR(1) processes, measures of dependence and their estimations," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    17. Ossama Mikhail, 2005. "What Happens After A Technology Shock? A Bayesian Perspective," Macroeconomics 0510016, University Library of Munich, Germany.
    18. Jean-Baptiste Gossé & Cyriac Guillaumin, 2013. "Can External Shocks Explain the Asian Side of Global Imbalances? Lessons from a Structural VAR Model with Block Exogeneity," Review of International Economics, Wiley Blackwell, vol. 21(1), pages 85-102, February.
    19. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    20. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.

    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:spr:aistmt:v:64:y:2012:i:2:p:319-342. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc 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 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.