Interaction matrix selection in spatial econometrics with an application to growth theory

Nicolas Debarsy and Cem Ertur

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Abstract: The interaction matrix, or spatial weight matrix, is the fundamental tool to model cross-sectional interdependence between observations in spatial econometric models. However, it is most of the time not derived from theory, as it should be ideally, but chosen on an ad hoc basis. In this paper, we propose a modified version of the J test to formally select the interaction matrix. Our methodology is based on the application of the robust against unknown heteroskedasticity GMM estimation method, developed by Lin & Lee (2010). We then implement the testing procedure developed by Hagemann (2012) to overcome the decision problem inherent to non-nested models tests. An application is presented for the Schumpeterian growth model with worldwide interactions (Ertur & Koch 2011) using three different types of interaction matrix: genetic distance, linguistic distance and bilateral trade flows and we find that the interaction matrix based on trade flows is the most adequate. Furthermore, we propose a network based innovative representation of spatial econometric results.

Keywords: Bootstrap; GMM; Interaction matrix; J tests; Non-nested models; Heteroscedasticity; Spatial autoregressive models; Heteroskedasticity (search for similar items in EconPapers)
Date: 2019-03
New Economics Papers: this item is included in nep-ecm and nep-gro
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01278545v3
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Published in Regional Science and Urban Economics, 2019, 75, pp.49-69. ⟨10.1016/j.regsciurbeco.2019.01.002⟩

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Working Paper: Interaction matrix selection in spatial econometrics with an application to growth theory (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-01278545

DOI: 10.1016/j.regsciurbeco.2019.01.002

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