Estimation and Inference in Heterogeneous Spatial Panel Data Models with a Multifactor Error Structure
Jia Chen (),
Yongcheol Shin and
Discussion Papers from Department of Economics, University of York
In this paper, we develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and unobserved common factors. To tackle the challenging issues of endogeneity caused by both the spatial lagged term and the correlation between regressors and factors, we propose to approximate common factors by cross-section averages of independent variables only, and deal with the spatial endogeneity via the instrumental variables. We develop the individual estimators as well as the Mean Group and the Pooled estimators, and establish their consistency and asymptotic normality. Monte Carlo simulations confirm that the finite sample performance of our proposed estimators are quite satisfactory. We demonstrate the usefulness of our approach with an application to a gravity model of bilateral trade flows for 91 pairs of 14 European Union (EU) countries, and find that the trade flows between the UK and EU members would fall substantially following a hard Brexit.
Keywords: Cross Section Dependence; Heterogeneous Spatial Panel Data Model; Factor Model; Instrumental Variable Analysis (search for similar items in EconPapers)
JEL-codes: C13 C15 C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-ore and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:yor:yorken:20/03
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