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

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 av...

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Bibliographic Details
Published in:Empirical economics 2008-01, Vol.34 (1), p.35-57
Main Author: Fingleton, Bernard
Format: Article
Language:English
Subjects:
Bootstrap method
Econometric models
Econometrics
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
England
Estimating techniques
Finance
Generalized method of moments
Housing prices
Insurance
Management
Original Paper
Prices
Real estate
Real estate market
Regression analysis
Spatial models
Statistics for Business
Studies
United Kingdom
Variables
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Summary: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.
ISSN:0377-7332
1435-8921
DOI:10.1007/s00181-007-0151-4