Advanced Search
MyIDEAS: Login to save this article or follow this journal

Statistical inference on regression with spatial dependence

Contents:

Author Info

  • Robinson, Peter M.
  • Thawornkaiwong, Supachoke
Registered author(s):

    Abstract

    Central limit theorems are developed for instrumental variables estimates of linear and semiparametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss estimation of the variance matrix, including estimates that are robust to disturbance heteroscedasticity and/or dependence. A Monte Carlo study of finite-sample performance is included. In an empirical example, the estimates and robust and non-robust standard errors are computed from Indian regional data, following tests for spatial correlation in disturbances, and nonparametric regression fitting. Some final comments discuss modifications and extensions.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.sciencedirect.com/science/article/pii/S0304407611002144
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 167 (2012)
    Issue (Month): 2 ()
    Pages: 521-542

    as in new window
    Handle: RePEc:eee:econom:v:167:y:2012:i:2:p:521-542

    Contact details of provider:
    Web page: http://www.elsevier.com/locate/jeconom

    Related research

    Keywords: Linear regression; Partly linear regression; Nonparametric regression; Spatial data; Instrumental variables; Asymptotic normality; Variance estimation;

    Find related papers by JEL classification:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. Abhijit Banerjee & Lakshmi Iyer, 2010. "History Institutions and Economic Performance: The Legacy of Colonial Land Tenure Systems in India," Working Papers id:2811, eSocialSciences.
    2. Robinson, P.M., 2011. "Asymptotic theory for nonparametric regression with spatial data," Journal of Econometrics, Elsevier, Elsevier, vol. 165(1), pages 5-19.
    3. Jenish, Nazgul & Prucha, Ingmar R., 2009. "Central limit theorems and uniform laws of large numbers for arrays of random fields," Journal of Econometrics, Elsevier, Elsevier, vol. 150(1), pages 86-98, May.
    4. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    5. Y. Nishiyama & P. M. Robinson, 2000. "Edgeworth Expansions for Semiparametric Averaged Derivatives," Econometrica, Econometric Society, Econometric Society, vol. 68(4), pages 931-980, July.
    6. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, Elsevier, vol. 92(1), pages 1-45, September.
    7. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, Econometric Society, vol. 56(4), pages 931-54, July.
    8. Hidalgo, Javier, 1992. "Adaptive Estimation in Time Serise Regression Models With Heteroskedasticity of Unknown Form," Econometric Theory, Cambridge University Press, vol. 8(02), pages 161-187, June.
    9. Peter Robinson, 2011. "Asymptotic theory for nonparametric regression with spatial data," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP11/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, Elsevier, vol. 140(1), pages 131-154, September.
    11. Castellana, J. V. & Leadbetter, M. R., 1986. "On smoothed probability density estimation for stationary processes," Stochastic Processes and their Applications, Elsevier, Elsevier, vol. 21(2), pages 179-193, February.
    12. Robinson, P.M., 2008. "Correlation testing in time series, spatial and cross-sectional data," Journal of Econometrics, Elsevier, Elsevier, vol. 147(1), pages 5-16, November.
    13. Chamberlain, Gary, 1992. "Efficiency Bounds for Semiparametric Regression," Econometrica, Econometric Society, Econometric Society, vol. 60(3), pages 567-96, May.
    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 in new window

    Cited by:
    1. Zhang, Xiang & Zheng, Yanbing, 2012. "A note on spatial–temporal lattice modeling and maximum likelihood estimation," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2145-2155.
    2. Jungyoon Lee & Peter M Robinson, 2013. "Series Estimation under Cross-sectional Dependence," STICERD - Econometrics Paper Series /2013/570, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:167:y:2012:i:2:p:521-542. 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: (Zhang, Lei).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.