IDEAS home Printed from
   My bibliography  Save this paper

Bias-Corrected Estimation for Spatial Autocorrelation


  • Zhenlin Yang

    () (School of Economics, Singapore Management University)


The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler, and can easily be extended to other models of a similar structure. Extensive Monte Carlo results show that the new approach performs excellently in general.

Suggested Citation

  • Zhenlin Yang, 2010. "Bias-Corrected Estimation for Spatial Autocorrelation," Working Papers 12-2010, Singapore Management University, School of Economics.
  • Handle: RePEc:siu:wpaper:12-2010

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Findlay, Ronald, 1970. "Factor Proportions and Comparative Advantage in the Long Run," Journal of Political Economy, University of Chicago Press, vol. 78(1), pages 27-34, Jan.-Feb..
    2. Matsuyama, Kiminori, 1988. "Life-cycle saving and comparative advantage in the long run," Economics Letters, Elsevier, vol. 28(4), pages 375-379.
    3. Hian Hoon, 1996. "Payroll taxes and VAT in a labor-turnover model of the ‘natural rate’," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 3(3), pages 369-383, July.
    4. Richard B. Freeman & Ronald Schettkat, 2005. "Marketization of household production and the EU–US gap in work," Economic Policy, CEPR;CES;MSH, vol. 20(41), pages 6-50, January.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Yang, Zhenlin, 2015. "A general method for third-order bias and variance corrections on a nonlinear estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 178-200.

    More about this item


    Third-order bias; Third-order variance; Bootstrap; Concentrated estimating equation; Monte Carlo; Quasi-MLE; Spatial layout.;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:siu:wpaper:12-2010. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (QL THor). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.