GMM gradient tests for spatial dynamic panel data models

Süleyman Taşpınar*, Osman Doğan, Anil K. Bera

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14 Atıf (Scopus)


In this study, we formulate adjusted gradient tests when the alternative model used to construct tests deviates from the true data generating process for a spatial dynamic panel data (SDPD) model. Following Bera et al. (2010), we introduce these adjusted gradient tests along with their standard counterparts within a generalized method of moments 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 a high 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 estimates from a non-spatial two-way panel data model. We investigate the finite sample size and power properties of these tests through a Monte Carlo study. Our results indicates that the adjusted gradient tests have good finite sample properties. Finally, using an application from the empirical growth literature we complement our findings.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)65-88
Sayfa sayısı24
DergiRegional Science and Urban Economics
Yayın durumuYayınlandı - 1 Tem 2017
Harici olarak yayınlandıEvet

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Publisher Copyright:
© 2017 Elsevier B.V.


We are grateful to the editor and two anonymous referees for many pertinent comments and constructive suggestions. However, we retain the responsibility of any remaining shortcomings of the paper. This research was supported, in part, under National Science Foundation Grants CNS-0958379, CNS-0855217, ACI-1126113 and the City University of New York High Performance Computing Center at the College of Staten Island.

FinansörlerFinansör numarası
City University of New York High Performance Computing Center at the College of Staten Island
National Science FoundationCNS-0958379, ACI-1126113, CNS-0855217

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