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Common Factors and Spatial Dependence: An Application to US House Prices

Cynthia Fan Yang ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper considers panel data models with cross-sectional dependence arising from both spatial autocorrelation and unobserved common factors. It derives conditions for model identification and proposes estimation methods that employ cross-sectional averages as factor proxies, including the 2SLS, Best 2SLS, and GMM estimations. The proposed estimators are robust to unknown heteroskedasticity and serial correlation in the disturbances, unrequired to estimate the number of unknown factors, and computationally tractable. The paper establishes the asymptotic distributions of these estimators and compares their consistency and efficiency properties. Extensive Monte Carlo experiments lend support to the theoretical findings and demonstrate the satisfactory finite sample performance of the proposed estimators. The empirical section of the paper finds strong evidence of spatial dependence of real house price changes across 377 Metropolitan Statistical Areas in the US from 1975Q1 to 2014Q4. The results also reveal that population and income growth have significantly positive direct and spillover effects on house price changes. These findings are robust to different specifications of the spatial weights matrix constructed based on distance, migration flows, and pairwise correlations.

Keywords: Cross-sectional dependence; Common factors; Spatial panel data models; Generalized method of moments; House prices (search for similar items in EconPapers)
JEL-codes: C13 C23 R21 R31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
Date: 2017-11-01, Revised 2018-08-20
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