IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v39y2002i4p387-400.html
   My bibliography  Save this article

Bayesian analysis of regression models with spatially correlated errors and missing observations

Author

Listed:
  • Oh, Man-Suk
  • Shin, Dong Wan
  • Kim, Han Joon

Abstract

No abstract is available for this item.

Suggested Citation

  • Oh, Man-Suk & Shin, Dong Wan & Kim, Han Joon, 2002. "Bayesian analysis of regression models with spatially correlated errors and missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 387-400, June.
    • Handle: RePEc:eee:csdana:v:39:y:2002:i:4:p:387-400
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(01)00084-6
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Robert E. McCulloch & Ruey S. Tsay, 1994. "Bayesian Analysis Of Autoregressive Time Series Via The Gibbs Sampler," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(2), pages 235-250, March.
    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. Omid Karimi & Mohsen Mohammadzadeh, 2012. "Bayesian spatial regression models with closed skew normal correlated errors and missing observations," Statistical Papers, Springer, vol. 53(1), pages 205-218, February.
    2. Smirnov, Oleg A. & Anselin, Luc E., 2009. "An O(N) parallel method of computing the Log-Jacobian of the variable transformation for models with spatial interaction on a lattice," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2980-2988, June.

    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. Myroslav Pidkuyko, 2014. "Dynamics of Consumption and Dividends over the Business Cycle," CERGE-EI Working Papers wp522, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    2. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    3. Barnett, Glen & Kohn, Robert & Sheather, Simon, 1996. "Bayesian estimation of an autoregressive model using Markov chain Monte Carlo," Journal of Econometrics, Elsevier, vol. 74(2), pages 237-254, October.
    4. Francesco Battaglia & Lia Orfei, 2005. "Outlier Detection And Estimation In NonLinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 107-121, January.
    5. Héctor Zárate & Norberto Rodríguez & Margarita Marín, 2013. "El tamano de las empresas y la transmisión de la política monetaria en Colombia: una aplicación con la encuesta mensual de expectativas económicas," Revista de Economía del Rosario, Universidad del Rosario, June.
    6. McCoy, E. J. & Stephens, D. A., 2004. "Bayesian time series analysis of periodic behaviour and spectral structure," International Journal of Forecasting, Elsevier, vol. 20(4), pages 713-730.
    7. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    8. Pfann, Gerard A. & Schotman, Peter C. & Tschernig, Rolf, 1996. "Nonlinear interest rate dynamics and implications for the term structure," Journal of Econometrics, Elsevier, vol. 74(1), pages 149-176, September.
    9. Sánchez, María Jesús, 1997. "The identification of multiple outliers in arima models," DES - Working Papers. Statistics and Econometrics. WS 6220, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. N. K. Unnikrishnan, 2004. "Bayesian Subset Model Selection for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 671-690, September.
    11. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    12. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.
    13. Tsay, Ruey S. & Pankratz, Alan E., 1998. "Outliers in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS 6285, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Chatzivgeri, Eleni & Mumtaz, Haroon & Tavasci, Daniela & Ventimiglia, Luigi, 2019. "Common and country specific factors in the distribution of real wages," Economics Letters, Elsevier, vol. 184(C).
    15. Chen, Cathy W. S., 1997. "Detection of additive outliers in bilinear time series," Computational Statistics & Data Analysis, Elsevier, vol. 24(3), pages 283-294, May.
    16. Greyserman, Alex & Jones, Douglas H. & Strawderman, William E., 2006. "Portfolio selection using hierarchical Bayesian analysis and MCMC methods," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 669-678, February.
    17. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    18. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    19. Byers, J.W. & Popova, I. & Simkins, B.J., 2021. "Robust estimation of conditional risk measures using machine learning algorithm for commodity futures prices in the presence of outliers," Journal of Commodity Markets, Elsevier, vol. 24(C).
    20. Galeano, Pedro & Tsay, Ruey S., 2004. "Outlier detection in multivariate time series via projection pursuit," DES - Working Papers. Statistics and Econometrics. WS ws044211, Universidad Carlos III de Madrid. Departamento de Estadística.

    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:eee:csdana:v:39:y:2002:i:4:p:387-400. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.