GMM Gradient Tests for Spatial Dynamic Panel Data Models

Suleyman Taspinar, Osman Dogan and Anil K. Bera

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Spatial Dynamic Panel Data Model; SDPD; GMM; Robust LM Tests; GMM Gradient Tests; Inference (search for similar items in EconPapers)
JEL-codes: C13 C21 C31 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-ecm and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Published in Regional Science and Urban Economics 65 (2017): pp. 65-88

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https://mpra.ub.uni-muenchen.de/83570/8/MPRA_paper_83570.pdf revised version (application/pdf)

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