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QML estimation with non-summable weight matrices

Author

Listed:
  • Jakub Olejnik

    (University of Lodz)

  • Alicja Olejnik

    (University of Lodz)

Abstract

This paper revisits the theory of asymptotic behaviour of the well-known Gaussian Quasi-Maximum Likelihood estimator of parameters in mixed regressive, high-order autoregressive spatial models. We generalise the approach previously published in the econometric literature by weakening the assumptions imposed on the spatial weight matrix. This allows consideration of interaction patterns with a potentially larger degree of spatial dependence. Moreover, we broaden the class of admissible distributions of model residuals. As an example application of our new asymptotic analysis we also consider the large sample behaviour of a general group effects design.

Suggested Citation

  • Jakub Olejnik & Alicja Olejnik, 2020. "QML estimation with non-summable weight matrices," Journal of Geographical Systems, Springer, vol. 22(4), pages 469-495, October.
    • Handle: RePEc:kap:jgeosy:v:22:y:2020:i:4:d:10.1007_s10109-020-00326-2
    DOI: 10.1007/s10109-020-00326-2
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    Cited by:

    1. Alicja Olejnik & Agata Zoltaszek, 2020. "Tracing The Spatial Patterns Of Innovation Determinants In Regional Economic Performance," Lodz Economics Working Papers 2/2020, University of Lodz, Faculty of Economics and Sociology.

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    More about this item

    Keywords

    Spatial autoregression; Quasi-maximum likelihood estimation; High-order SAR model; Asymptotic analysis; Non-summable matrices;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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