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GMM Gradient Tests for Spatial Dynamic Panel Data Models

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  • Taspinar, Suleyman
  • Dogan, Osman
  • Bera, Anil K.

Abstract

In this study, we formulate the adjusted gradient tests when the alternative model used to construct tests deviates from the true data generating process for a spatial dynamic panel data model (SDPD). Following Bera et. al. (2010), we introduce these adjusted gradient tests along with the standard ones within a GMM framework. These tests can be used to detect the presence of (i) the contemporaneous spatial lag terms, (ii) the time lag term, and (iii) the spatial time lag terms in an higher order SDPD model. These adjusted tests have two advantages: (i) their null asymptotic distribution is a central chi-squared distribution irrespective of the mis-specified alternative model, and (ii) their test statistics are computationally simple and require only the ordinary least-squares (OLS) estimates from a non-spatial two-way panel data model. We investigate the finite sample size and power properties of these tests through Monte Carlo studies. Our results indicates that the adjusted gradient tests have good finite sample properties.

Suggested Citation

  • Taspinar, Suleyman & Dogan, Osman & Bera, Anil K., 2017. "GMM Gradient Tests for Spatial Dynamic Panel Data Models," MPRA Paper 82830, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:82830
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    Cited by:

    1. Jieun Lee, 2022. "Testing Endogeneity of Spatial Weights Matrices in Spatial Dynamic Panel Data Models," Papers 2209.05563, arXiv.org.
    2. Anil K. Bera & Osman Doğan & Süleyman Taşpınar & Monalisa Sen, 2020. "Specification tests for spatial panel data models," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-39, December.
    3. Carmelo Algeri & Antonio F. Forgione & Carlo Migliardo, 2022. "Do spatial dependence and market power matter in the diversification of cooperative banks?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(3), November.
    4. Bera, Anil K. & Doğan, Osman & Taşpınar, Süleyman, 2018. "Simple tests for endogeneity of spatial weights matrices," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 130-142.
    5. Zhenlin Yang, 2021. "Joint tests for dynamic and spatial effects in short panels with fixed effects and heteroskedasticity," Empirical Economics, Springer, vol. 60(1), pages 51-92, January.
    6. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.
    7. Yu Hao & Shang Gao & Yunxia Guo & Zhiqiang Gai & Haitao Wu, 2021. "Measuring the nexus between economic development and environmental quality based on environmental Kuznets curve: a comparative study between China and Germany for the period of 2000–2017," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16848-16873, November.
    8. Ruslan Safarov & Zhanat Shomanova & Yuriy Nossenko & Zhandos Mussayev & Ayana Shomanova, 2024. "Digital Visualization of Environmental Risk Indicators in the Territory of the Urban Industrial Zone," Sustainability, MDPI, vol. 16(12), pages 1-40, June.

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    More about this item

    Keywords

    Spatial Dynamic Panel Data Model; SDPD; GMM; Robust LM Tests; GMM Gradient Tests; Inference;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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