Spatial Spillover Effects of Foreign Direct Investment Flows to Arab Countries Based on Static and Dynamic Spatial Panel Data Models: A Spatial Panel Modelling Study | ||||
| المجلة العلمية للإقتصاد و التجارة | ||||
| Volume 55, Issue 2, July 2025, Page 223-260 PDF (1.48 MB) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/jsec.2025.442483 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Wael Saad Hsanein El-doakly | ||||
| Faculty of Business, Ain Shams University | ||||
| Abstract | ||||
| The dynamic panel model assumes that each observation unit is independent of each other. But sometimes this assumption is violated, so there are spatial effects in the model. As spatial autocorrelation is one of spatial analysis to identify patterns of relationship or correlation between locations. This method is very important to get information on the dispersal patterns characteristic of a region and linkages between locations. The link among location indicated by a spatial weight matrix. It describe the structure of neighboring and reflects the spatial influence. Selection weighting function is one determinant of the results of the spatial analysis. This study aimed to make modeling of foreign direct investment (FDI) in Arab countries using the Spatial Models during the period 2015-2024. The models used in this study are Spatial Auto Regressive (SAR), Spatial Error Models (SEM), Spatial Durbin Model (SDM), Dinamic Spatial Durbin Model (DSDM). The four models were evaluated using the Akaike Information Criterion (AIC),Bayesian Information Criterion(BIC), and Adj-R2. The results show that there is a spatial interaction in FDI attraction to the above mentioned region. At the same time, factors such as trade openness, market size, population growth, infrastructure and enterprise agglomeration affect FDI inflows in the short and long term. In particular, the trade openness of a country increases FDI inflows of that country and neighboring countries, and enterprise agglomeration of a country increases the FDI inflows of that country and in adjacent countries. Other results showed that (DSDM) was able to explain the diversity in models at 92.4%. Thus, the result of the study proved that (DSDM) is the best model for modelling dynamic FDI panel data in Arab countries during 2015-2024. | ||||
| Keywords | ||||
| Dynamic spatial analysis; Spatial spillover effects; Spatial panel data models; Spatial Auto Regressive (SAR); Spatial Error Models (SEM); Spatial Durbin Model (SDM); Dynamic Spatial Durbin Model (DSDM); Cross-sectional dependence; Spatial weights matrix; Maximum Likelihood Estimation (MLE); Foreign Direct Investment | ||||
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