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An improved generalized moments estimator for a spatial moving average error model

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  • Baltagi, Badi H.
  • Liu, Long

Abstract

Following Arnold and Wied (2010), we suggest an improved generalized moments estimator for the spatial moving average error model which takes explicitly into account that the moment conditions are based on OLS residuals rather than the true disturbances.

Suggested Citation

  • Baltagi, Badi H. & Liu, Long, 2011. "An improved generalized moments estimator for a spatial moving average error model," Economics Letters, Elsevier, vol. 113(3), pages 282-284.
    • Handle: RePEc:eee:ecolet:v:113:y:2011:i:3:p:282-284
    DOI: 10.1016/j.econlet.2011.08.015
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    References listed on IDEAS

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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Arnold, Matthias & Wied, Dominik, 2010. "Improved GMM estimation of the spatial autoregressive error model," Economics Letters, Elsevier, vol. 108(1), pages 65-68, July.
    3. Bernard Fingleton, 2009. "A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 35-57, Springer.
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    Citations

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    Cited by:

    1. Sheena Yu-Hsien Kao & Anil K. Bera, 2018. "Testing spatial regression models under nonregular conditions," Empirical Economics, Springer, vol. 55(1), pages 85-111, August.
    2. Matthias Arnold & Dominik Wied, 2014. "Improved GMM estimation of random effects panel data models with spatially correlated error components," Papers in Regional Science, Wiley Blackwell, vol. 93(1), pages 77-99, March.
    3. Wang, Luya & Li, Kunpeng & Wang, Zhengwei, 2014. "Quasi maximum likelihood estimation for simultaneous spatial autoregressive models," MPRA Paper 59901, University Library of Munich, Germany.
    4. Eric S. Lin & Ta-Sheng Chou, 2018. "Finite-sample refinement of GMM approach to nonlinear models under heteroskedasticity of unknown form," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 1-28, January.
    5. Osman Doğan, 2015. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term," Econometrics, MDPI, vol. 3(1), pages 1-27, February.
    6. Doğan, Osman & Taşpınar, Süleyman, 2013. "GMM estimation of spatial autoregressive models with moving average disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 903-926.
    7. Osman Dogan, 2013. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term," Working Papers 2, City University of New York Graduate Center, Ph.D. Program in Economics.
    8. José-María Montero & Gema Fernández-Avilés & Tiziana Laureti, 2021. "A Local Spatial STIRPAT Model for Outdoor NO x Concentrations in the Community of Madrid, Spain," Mathematics, MDPI, vol. 9(6), pages 1-33, March.

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    More about this item

    Keywords

    Method of moments estimation; Spatial moving average; Regression residuals;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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