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Specification and estimation of a periodic spatial panel autoregressive model

Author

Listed:
  • Marius Amba

    (Université de Yaoundé II)

  • Julie Le Gallo

    (CESAER - Centre d'économie et de sociologie rurales appliquées à l'agriculture et aux espaces ruraux - UBFC - Université Bourgogne Franche-Comté [COMUE] - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Dijon - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

Abstract

Conventional estimation methods for the the spatial autoregressive (SAR) model rely on the key assumption that the spatial lag parameter is time-invariant for the entire study period. This strong assumption is likely be violated in many economic situations where spillovers may change over time. At the other extreme, a time-varying model where the spatial lag coefficient changes every period might be unnecessary. This paper specifies a periodic spatial autoregressive model with fixed effects and develops three estimation methods: two-stage instrumental variable (2SLS) method, quasi-maximum likelihood estimation (QMLE) approach and generalized method of moments (GMM). The Monte Carlo study investigates the small-sample properties of the proposed estimators under various scenarios and evaluates the costs of misspecification of the Data Generating Process, pointing to the usefulness of the periodic spatial autoregressive model from an applied perspective.

Suggested Citation

  • Marius Amba & Julie Le Gallo, 2022. "Specification and estimation of a periodic spatial panel autoregressive model," Post-Print hal-03910243, HAL.
    • Handle: RePEc:hal:journl:hal-03910243
    DOI: 10.1007/s43071-022-00028-5
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    References listed on IDEAS

    as
    1. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    2. Blasques, Francisco & Koopman, Siem Jan & Lucas, Andre & Schaumburg, Julia, 2016. "Spillover dynamics for systemic risk measurement using spatial financial time series models," Journal of Econometrics, Elsevier, vol. 195(2), pages 211-223.
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    5. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
    6. Yang, Zhenlin, 2018. "Unified M-estimation of fixed-effects spatial dynamic models with short panels," Journal of Econometrics, Elsevier, vol. 205(2), pages 423-447.
    7. 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.
    8. Leopoldo Catania & Anna Gloria Billé, 2017. "Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1178-1196, September.
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    Cited by:

    1. Marius C. O. Amba & Taoufiki Mbratana & Julie Gallo, 2023. "Spatial panel simultaneous equations models with error components," Empirical Economics, Springer, vol. 65(3), pages 1149-1196, September.

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    More about this item

    Keywords

    Periodical spatial dependence; Spatial autoregressive model; Fixed effects;
    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

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