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Bayesian Estimation of A Distance Functional Weight Matrix Model

Author

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  • Kazuhiko Kakamu

    (Department of Economics & Finance, Institute for Advanced Studies)

Abstract

This paper considers the distance functional weight matrix in spatial autoregressive and spatial error models from a Bayesian point of view. We considered the Markov chain Monte Carlo methods to estimate the parameters of the models. Our approach is illustrated with simulated data set.

Suggested Citation

  • Kazuhiko Kakamu, 2005. "Bayesian Estimation of A Distance Functional Weight Matrix Model," Economics Bulletin, AccessEcon, vol. 3(57), pages 1-6.
    • Handle: RePEc:ebl:ecbull:eb-05c10018
    as

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    File URL: http://www.accessecon.com/pubs/EB/2005/Volume3/EB-05C10018A.pdf
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    References listed on IDEAS

    as
    1. LeSage, James P. & Kelley Pace, R., 2007. "A matrix exponential spatial specification," Journal of Econometrics, Elsevier, vol. 140(1), pages 190-214, September.
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    Cited by:

    1. Wang, Yiyi & Kockelman, Kara M. & Xiaokun (Cara) Wang, Xiaokun (Cara) Wang, 2013. "The impact of weight matrices on parameter estimation and inference: A case study of binary response using land-use data," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(3), pages 75-85.
    2. Paul Elhorst & Solmaria Halleck Vega, 2013. "On spatial econometric models, spillover effects, and W," ERSA conference papers ersa13p222, European Regional Science Association.

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    More about this item

    Keywords

    Distance functional Weight matrix;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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