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A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices

In: Spatial Econometrics

Author

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  • Bernard Fingleton

    (Cambridge University)

Abstract

This paper proposes a new GMM estimator for spatial regression models with moving average errors. Monte Carlo results are given which suggest that the GMM estimates are consistent and robust to non-normality, and the Bootstrap method is suggested as a way of testing the significance of the moving average parameter. The estimator is applied in a model of English real estate prices, in which the concepts of displaced demand and displaced supply are introduced to derive the spatial lag of prices, and the moving average error process represents spatially autocorrelated unmodelled variables.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stecpp:978-3-7908-2070-6_3
    DOI: 10.1007/978-3-7908-2070-6_3
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    Keywords

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    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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