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

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  • Yang, Cynthia Fan

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.

Suggested Citation

  • Yang, Cynthia Fan, 2017. "Common Factors and Spatial Dependence: An Application to US House Prices," MPRA Paper 89032, University Library of Munich, Germany, revised 20 Aug 2018.
  • Handle: RePEc:pra:mprapa:89032
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    File URL: https://mpra.ub.uni-muenchen.de/89032/1/MPRA_paper_89032.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2019. "Estimation and inference for spatial models with heterogeneous coefficients: an application to U.S. house prices," CESifo Working Paper Series 7542, CESifo Group Munich.
    2. George Kapetanios & M. Hashem Pesaran & Simon Reese, 2018. "A Residual-based Threshold Method for Detection of Units that are Too Big to Fail in Large Factor Models," CESifo Working Paper Series 7401, CESifo Group Munich.

    More about this item

    Keywords

    Cross-sectional dependence; Common factors; Spatial panel data models; Generalized method of moments; House prices;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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