Estimation and inference for spatial models with heterogeneous coefficients: an application to U.S. house prices
Natalia Bailey () and
M Pesaran ()
No 7542, CESifo Working Paper Series from CESifo
This paper considers the problem of identification, estimation and inference in the case of spatial panel data models with heterogeneous spatial lag coefficients, with and without (weakly) exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of spatial coefficients are derived. Regularity conditions are established for the QML estimators of individual spatial coefficients, as well as their means (the mean group estimators), to be consistent and asymptotically normal. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations for Gaussian and non-Gaussian errors, and with spatial weight matrices of differing degrees of sparsity. The simulation results are in line with the paper's key theoretical findings even for panels with moderate time dimensions, irrespective of the number of cross section units. An empirical application to U.S. house price changes during the 1975-2014 period shows a significant degree of heterogeneity in spill-over effects over the 338 Metropolitan Statistical Areas considered.
Keywords: spatial panel data models; heterogeneous spatial lag coefficients; identification; quasi maximum likelihood (QML) estimators; non-Gaussian errors; house price changes; Metropolitan Statistical Areas (search for similar items in EconPapers)
JEL-codes: C21 C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
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Journal Article: Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_7542
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