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A spatial panel data model with time varying endogenous weights matrices and common factors

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  • Shi, Wei
  • Lee, Lung-fei

Abstract

Many spatial panel data sets exhibit cross sectional and/or intertemporal dependence from spatial interactions or common factors. In an application of a spatial autoregressive model, a spatial weights matrix may be constructed from variables that may correlate with unobservables in the main equation and therefore is endogenous. Some common factors may be unobserved and correlate with included regressors in the equation. This paper presents a unified approach to model spatial panels with endogenous time varying spatial weights matrices and unobserved common factors. We show that the proposed QML estimator is consistent and asymptotically normal. As its limiting distribution may have a leading order bias, an analytical bias correction is proposed. Monte Carlo simulations demonstrate good finite sample properties of the estimators. This model is empirically applied to examine the effects of house price dynamics on reverse mortgage origination rates in the United States.

Suggested Citation

  • Shi, Wei & Lee, Lung-fei, 2018. "A spatial panel data model with time varying endogenous weights matrices and common factors," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 6-34.
    • Handle: RePEc:eee:regeco:v:72:y:2018:i:c:p:6-34
    DOI: 10.1016/j.regsciurbeco.2017.03.007
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    5. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    6. Román Mínguez & Roberto Basile & María Durbán, 2020. "An alternative semiparametric model for spatial panel data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 669-708, December.
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    8. Debarsy, Nicolas & Yang, Zhenlin, 2018. "Editorial for the special issue entitled: New advances in spatial econometrics: Interactions matter," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 1-5.
    9. Rudra P. Pradhan & Mak B. Arvin & Mahendhiran Nair & Sara E. Bennett, 2020. "Sustainable economic growth in the European Union: The role of ICT, venture capital, and innovation," Review of Financial Economics, John Wiley & Sons, vol. 38(1), pages 34-62, January.
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    13. Higgins, Ayden & Martellosio, Federico, 2023. "Shrinkage estimation of network spillovers with factor structured errors," Journal of Econometrics, Elsevier, vol. 233(1), pages 66-87.

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    More about this item

    Keywords

    Spatial panel data; Endogenous spatial weighting matrix; Multiplicative individual and time effects; QMLE; Reverse mortgages;
    All these keywords.

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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