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Common factors and spatial dependence: an application to US house prices

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  • Cynthia Fan Yang

Abstract

This article considers panel data models with cross-sectional dependence arising from both spatial autocorrelation and unobserved common factors. It 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 article 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 article finds strong evidence of spatial dependence of real house price changes across 377 Metropolitan Statistical Areas in the US from 1975Q1 to 2014Q4.

Suggested Citation

  • Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    • Handle: RePEc:taf:emetrv:v:40:y:2021:i:1:p:14-50
    DOI: 10.1080/07474938.2020.1741785
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    Cited by:

    1. Jia Chen & Yongcheol Shin & Chaowen Zheng, 2020. "Estimation and Inference in Heterogeneous Spatial Panel Data Models with a Multifactor Error Structure," Discussion Papers 20/03, Department of Economics, University of York.
    2. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2021. "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 18-44, January.
    3. Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2023. "Regional Productivity Network in the EU," CESifo Working Paper Series 10404, CESifo.
    4. Bailey, N. & Ditzen, J. & Holly, S., 2025. "My neighbour's neighbour is not my neighbour: Instrumentation and causality in spatial models," Cambridge Working Papers in Economics 2501, Faculty of Economics, University of Cambridge.
    5. Pesaran, M. Hashem & Pick, Andreas & Timmermann, Allan, 2022. "Forecasting with panel data: estimation uncertainty versus parameter heterogeneity," CEPR Discussion Papers 17123, C.E.P.R. Discussion Papers.
    6. 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.
    7. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    8. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Correction to: Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 65(3), pages 1509-1509, September.
    9. Zhaoxing Gao & Sihan Tu & Ruey S. Tsay, 2025. "High-Dimensional Spatial Arbitrage Pricing Theory with Heterogeneous Interactions," Papers 2511.01271, arXiv.org.
    10. Tavassoli, Solaleh & Khiabani, Nasser, 2023. "Spatio-Temporal Diffusion of Housing Prices in Iran (in Persian)," The Journal of Planning and Budgeting (٠صلنامه برنامه ریزی و بودجه), Institute for Management and Planning studies, vol. 28(3), pages 3-42, December.
    11. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2023. "Robust dynamic space–time panel data models using $$\varepsilon $$ ε -contamination: an application to crop yields and climate change," Empirical Economics, Springer, vol. 64(6), pages 2475-2509, June.
    12. Xiaowen Dai & Shidan Huang & Libin Jin & Maozai Tian, 2023. "Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
    13. Giuseppe Feo & Francesco Giordano & Sara Milito & Marcella Niglio & Maria Lucia Parrella, 2025. "Clustering and classification of spatio-temporal data using spatial dynamic panel data models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(2), pages 387-435, June.
    14. Mattera, Raffaele & Franses, Philip Hans, 2025. "Forecasting house price growth rates with factor models and spatio-temporal clustering," International Journal of Forecasting, Elsevier, vol. 41(1), pages 398-417.
    15. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.
    16. Sofia Vale & Felipa de Mello-Sampayo, 2021. "Effect of Hierarchical Parish System on Portuguese Housing Rents," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    17. Dai, Siqi & Hong, Yongmiao & Li, Haiqi & Zheng, Chaowen, 2025. "Shrinkage estimation of spatial panel data models with multiple structural breaks and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 251(C).

    More about this item

    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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