IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-05c10005.html
   My bibliography  Save this article

A consistent nonparametric estimation of spatial autocovariances

Author

Listed:
  • Théophile Azomahou

    (BETA, Université Louis Pasteur, Strasbourg 1)

  • Dong Li

    (Department of Economics, Kansas State University)

Abstract

We examine some aspects of estimating sample autocovariances for spatial processes. Especially, we note that for such processes, it is not possible to approximate the expectation by the sample mean, like in the case of time series data. Then, we propose a consistent nonparametric estimation of sample autocovariances for an irregularly scattered spatial process, derived from a transformation of the initial process. We also suggest an L_2-consistent weighting matrix. Monte Carlo simulations are used to evaluate the performance of the proposed estimators in finite samples.

Suggested Citation

  • Théophile Azomahou & Dong Li, 2005. "A consistent nonparametric estimation of spatial autocovariances," Economics Bulletin, AccessEcon, vol. 3(29), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-05c10005
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/pubs/EB/2005/Volume3/EB-05C10005A.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    4. Chen, Xiaoheng & Conley, Timothy G., 2001. "A new semiparametric spatial model for panel time series," Journal of Econometrics, Elsevier, vol. 105(1), pages 59-83, November.
    5. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
    Full references (including those not matched with items on IDEAS)

    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. repec:ebl:ecbull:v:3:y:2005:i:29:p:1-10 is not listed on IDEAS
    2. Théophile Azomahou, 2008. "Minimum distance estimation of the spatial panel autoregressive model," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 2(1), pages 49-83, April.
    3. James G. MacKinnon, 2012. "Thirty Years Of Heteroskedasticity-robust Inference," Working Paper 1268, Economics Department, Queen's University.
    4. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    5. Théophile Azomahou, 2001. "GMM Estimation of Lattice Models Using Panel Data: Application," Working Papers of BETA 2001-09, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    6. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    7. repec:rri:wpaper:200711 is not listed on IDEAS
    8. Wooldridge, Jeffrey M., 2023. "What is a standard error? (And how should we compute it?)," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Indra de Soysa, 2022. "Economic freedom vs. egalitarianism: An empirical test of weak & strong sustainability, 1970–2017," Kyklos, Wiley Blackwell, vol. 75(2), pages 236-268, May.
    10. Andreasen, Martin M. & Christensen, Bent Jesper, 2015. "The SR approach: A new estimation procedure for non-linear and non-Gaussian dynamic term structure models," Journal of Econometrics, Elsevier, vol. 184(2), pages 420-451.
    11. Kaewnern, Hathaipat & Wangkumharn, Sirikul & Deeyaonarn, Wongsathon & Yousaf, Abaid Ullah & Kongbuamai, Nattapan, 2023. "Investigating the role of research development and renewable energy on human development: An insight from the top ten human development index countries," Energy, Elsevier, vol. 262(PB).
    12. Onur Özdemir, 2023. "The determinants of income distribution: the role of progress in human capital," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4193-4227, October.
    13. Trabelsi, Emna & Hichri, Walid, 2021. "Central Bank Transparency with (semi-)public Information: Laboratory Experiments," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    14. Russell Davidson & Victoria Zinde‐Walsh, 2017. "Advances in specification testing," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1595-1631, December.
    15. Peter M Robinson, 2009. "Developments in the Analysis of Spatial Data," STICERD - Econometrics Paper Series 531, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    16. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 106, University of California, Davis, Department of Economics.
    17. Liu, Xiaodong & Saraiva, Paulo, 2015. "GMM estimation of SAR models with endogenous regressors," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 68-79.
    18. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    19. Predrag Petrović & Goran Nikolić, 2018. "Schumpeterian Growth Theory: Empirical Testing Of Barriers To Competition-Proximity To Frontier Algorithm," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 63(217), pages 7-38, April – J.
    20. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    21. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    22. Hirukawa, Masayuki, 2023. "Robust Covariance Matrix Estimation in Time Series: A Review," Econometrics and Statistics, Elsevier, vol. 27(C), pages 36-61.

    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:ebl:ecbull:eb-05c10005. 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: John P. Conley (email available below). General contact details of provider: .

    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.