The objective of this paper is to develop a GME formulation for the class of spatial structural equations models (S-SEM) into a panel data framework. In this respect, two innovatory aspects are introduced: (i) the formalization of the GME estimation approach of SEM to allow for spatial heterogeneity and spatial dependence (spatially sampled data); (ii) the extension of the methodology panel data.

Analysis of spatial panel data is of great importance and interest in spatial econometrics. Here we consider cigarette demand in a spatial panel of 46 states of the US over a 30-year period. We construct a demand equation to examine the elasticity of per pack cigarette price and per capita disposable income. The existing spatial panel models account for both spatial autocorrelation and state-wi...

China has witnessed a fast economic growth in the recent two decades. However, the heavy energy exploitation seems to show a negative relation to regional economic growth. Thus, the issue is whether the energy production is a curse or blessing for the regional economic growth in China. The present study deploys a comprehensive approach to rigorously prove the validity of a proposed panel data m...

the purpose of this study is to investigate the factors affecting the manufacturing industry wage among selected developing countries based on new economic geography theory. more specifically, we use a panel data model to study the spatial structure of wages in 136 countries for the period 1998-2007. the results indicate that this theory provides a good description of the spatial structure of w...


 The agricultural sector is strategic and the third-largest contributor to GRDP of West Java Province, but its growth has slowed during 2016–2019 period. slow rate can be caused by determinants itself very likely affected spatial interaction between regions in province. Therefore, this study aims analyze effect on identify factors that influence within province using analysis panel data. ...

In the present note we demonstrate how to implement the Lee and Yu (2010) procedure for fixed effects spatial panel data models available from the R (R Development Core Team 2012) package splm (Millo and Piras 2012). Additionally, we also show how to compute the impact estimates introduced by Kelejian, Tavlas, and Hondroyiannis (2006) and formalized in LeSage and Pace (2009). Unlike Matlab (MAT...

This article considers quasi-maximum likelihood estimations (QMLE) for two spatial panel data regression models: mixed effects model with spatial errors and transformed mixed effects model (where response and covariates are transformed) with spatial errors. One aim of transformation is to normalize the data, thus the transformed models are more robust with respect to the normality assumption co...

This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation. In particular, we consider a simple demand equation for cigarettes based on a panel of 46 states over the period 1963-1992. The spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. We derive the best linear un...