Academic journal article Canadian Social Science

An Application of Spatial-Panel Analysis: Provincial Economic Growth and Logistics in China/UNE APPLICATION DE L'ANALYSE DE PANEL SPATIAL: LA CROISSANCE ÉCONOMIQUE PROVINCIALE ET LA LOGISTIQUE EN CHINE

Academic journal article Canadian Social Science

An Application of Spatial-Panel Analysis: Provincial Economic Growth and Logistics in China/UNE APPLICATION DE L'ANALYSE DE PANEL SPATIAL: LA CROISSANCE ÉCONOMIQUE PROVINCIALE ET LA LOGISTIQUE EN CHINE

Article excerpt

Abstract: This paper introduces the spatial panel autocorrelation model, utilizes C-D production functions, constructs the spatial econometric model and finally studies the spatial correlativity between provincial economic growth and logistics. By using the spatial package of Matlab software, it verifies the possibility if there is the remarkable autocorrelation of the Chinese provincial economic growth and local logistics. On the base of building the spatial panel model, we research the spatial quantitative autocorrelation of the Chinese provincial economic growth and local logistics.

Keywords: economic growth; logistics; spatial panel autocorrelation

Résumé: Cet article présente le modèle d'autocorrélation de panel spatial, utilise les fonctions de production de C-D, construit le modèle économétrique spatial et enfin étudie la corrélativité spatiale entre la croissance économique provinciale et la logistique. En utilisant le paquet spatial de logiciel Matlab, il vérifie la possibilité de l'existence d'une autocorrélation remarquable de la croissance économique provinciale chinoise et la logistique locale. Sur la base de la construction d'un modèle de panel spatial, nous étudions l'autocorrélation spatiale quantitative de la croissance économique provinciale chinoise et la logistique locale.

Mots-clés: croissance économique; logistique; autocorrélation de panel spatial

(ProQuest: ... denotes formula omitted.)

Modern economic growth depends strongly on logistics. Logistics has become one of the most important factors to promote economic growth, adjust industrial layout and drive the evolution of economic spatial structure. Previous studies of the relationship between economic growth and logistics, limited in time series, which ignored the differences between locations. This paper introduces the spatial factor into a unified analytical framework, considers not only the spatial heterogeneity but also spatial correlation between economic growth and logistics. This paper uses individual fixed-effect model as the basic panel-data model, and uses latest spatial panel-data model to study the correlation between provincial economic growth and local logistics in China.

1. SPATIAL-PANEL MODEL AND CORRELATION TEST

1.1 Spatial-panel Models

Spatial effects of the spatial econometrics include spatial autocorrelation and spatial differences. The former is the correlation of the observations between a regional sample and other regional samples. The latter is the spatial-effect non-uniform at the regional level caused by the heterogeneity of spatial units (Anselin, 1988a). Spatial autocorrelation in the spatial autoregressive model is reflected in the error term and the lagged item of dependent variable. Therefore there are two basic spatial econometric models, one is Spatial Auto Regressive Model (SAR), the other is Spatial Error Model (SEM), and the basic formulas of two models are:

Spatial Auto Regressive Model (SAR):

y = pWNy + ρW^sub N^y + X'β+ ε (1)

Spatial Error Model (SEM):

y = X'β+ μ

μ = λW^sub N^ μ+ 7egr; (2)

y is the dependent variable, X is the vector of independent variables (including constant term), ß is variable factors, ? is spatial regression coefficients, ? is spatial autocorrelation coefficients, e is the error components obeying the normal distribution, Wn is the spatial matrix of ? x ? (? is the number of region), the weight coefficient can defined on actual conditions.

The above-mentioned model is a model for the cross-sectional data. In order to apply it to panel data, we need to change the model to meet the basic formula of panel data model. This paper uses individual fixed-effect model (Elhorst 2003). The model controls two kinds of non-observable effects: spatial fixed-effect and time fixed-effect, the former is the effect of background variables which changed with the location, but no changed with time (such as economic structure and natural endowments, etc. …

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