Regression Models for Spatial Data: An Example from Gross Domestic Regional Bruto in Province Central Java (original) (raw)
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Spatial Spillover Effect Of Transportation Infrastructure On Regional Growth
Economy of Region, 2020
Increased connectivity between regions in Indonesia is believed to impact the productivity capacity of each region, as well as its economic growth. Moreover, the influence of connectivity on the surrounding area is commonly known as the indirect effect (spillover effect). This effect can increase the number of products, goods and services used as production factors. The study aims to examine the effect of transportation infrastructure on economic growth. We used spatial modelling to estimate the impact of transportation infrastructure on the economy of 34 provinces in Indonesia in 2017. We applied the spatial lag of X model (SLX), spatial autoregressive model (SAR), spatial error model (SEM), spatial autoregressive combined model (SAC), spatial Durbin model (SDM), spatial Durbin error model (SDEM), and spatial autoregressive combined mixed model (SAC mixed). According to the estimation results, the SAC mixed model is the best spatial model, as it has the smallest value of the Akaike information criterion (AIC) and significant coefficients of ρ (rho) and λ (lambda) parameters. The results show that the indicators "bus stations", "domestic investment" and "foreign investment" have a direct effect on the economic growth in 34 Indonesian provinces. In addition, we revealed the presence of indirect effects (spillovers) between provinces in Indonesia for the same variables.
Regression models for spatial data: an example from precision agriculture
2010
The important role of a region's transportation infrastructure strongly affects the economic growth of the region and tends to affect the surrounding areas. The effect is called spillover effect. The aim of the research was to recognize the direct effect and spillover effect (indirect) of transportation infrastructure on the economic growth in Central Java. To identify the spillover effects, it is necessary to recognize the different characteristics of each region which have the implications on the various transportation infrastructures at each region in Central Java. Therefore, the spatial modeling was conducted. In this study, the spatial modeling employed was Spatial Durbin Error Model (SDEM). The SDEM is another form of Spatial Error Model (SEM). It does not allow for lag effects of endogenous variables, but it allows for spatial error and spatial lag on exogenous variables in which it simplifies the interpretations on direct effects and spillover effect. According to SDEM estimates, the transportation infrastructures at the districts/municipalities in Central Java had no significant effect on the outputs at each region where the infrastructures were located and their neighboring districts/cities.
The impact of human capital spill over which is manifested by the migration of foreign workers into Central Java Province, and the migration of Indonesian workers abroad is a form of labor mobility that occurs in Central Java Province. Central Java Province is one of the 34 provinces in Indonesia. Central Java Province has contributed to the labor mobility policy since the enactment of the ASEAN Economic Community. This study aims to analyze the impact of human capital of foreign workers on economic growth in Central Java Province. This research method uses spatial autoregressive model (SAR), spatial error model (SEM). The results of this study obtained that the results of the calculation of the SAR-fixed effect is known that there are spatial interactions and spatial effects in each of the 35 districts / cities studied. This is evidenced by the spatial lag coefficient (δ) = 0.83 or the spatial coefficient rho 0.83 showing the magnitude of the interaction of the value of the regency / city GRDP with the value of the neighboring regency / city. Spatial rho 8 percent means that if a district / city grows 8 percent it will affect 35 districts / cities with a correlation of productivity of 4 percent. The results of the SEM-fixed effect calculation are known that there is a relationship of economic growth between 29 districts and 6 other cities.
Spatial Panel Data Model of West Java's Regional Revenue
International Journal of Scientific Research in Science, Engineering and Technology, 2022
The spatial panel data model is the construction of a regression model that is used to explain the spatial dependence on panel data. Space dependence may apply between adjacent areas, as in the economic field. This should not be ignored because if the freedom between regions is not fulfilled. The spatial panel data model may be in the form of a SAR, SEM or GSM model. In this study, the spatial panel data model is used to model regional income in districts/cities in West Java, the results of the analysis obtained are that the SEM model with random effect is the best model because value of R^2-adj is 97.64%.
Spatial Econometric Model on Economic Growth in West Nusa Tenggara
2021
Article history: Received : 14-11-2020 Reviced : 16-04-2021 Accepted : 20-04-2021 Gross Regional Domestic Product (GRDP) is a reflection of a region's economic growth. West Nusa Tenggara (NTB) is one of the provinces that contributes to good GRDP for Indonesia. The purpose of this research is to modeling GRDP in NTB using spatial econmetrics. The data used is the GRDP data of each district / city in NTB Province as a response variable and factors that affect the number of workers, capital value and electrification ratio as predictor variables. The results showed that there is a spatial dependence on the district / city GRDP in NTB Province on the error model so that the model formed is the Spatial Error Model (SEM) with a rho of 71.1% and an AIC value of 173.34. Keyword: Spatial dependence, Spatial Error Model (SEM), Economic growth, Labor, Capital This is an open access article under the CC BY-SA license. DOI: https://doi.org/10.30812/varian.v4i2.912 ————————————————————
Testing the spatial auto-regression (SAR) model on Indonesia's regional economy
Jurnal Ekonomi Pembangunan, 2020
Indonesia's regional economy that is proxy by using Gross Regional Domestic Product (GRDP) per capita to form clusters is investigated. Besides, by using the Spatial Auto-regression (SAR) model, the effect of household consumption in a region to the surrounding area's economy is examined. The study on this topic is rather limited, especially in the regional economic development of the country. Furthermore, Indonesia is a heterogeneous country, and its consequence is that development policy should consider the geographic characteristics of the country. The results show that there are regional economy clusters in Java, Kalimantan, Sulawesi, and Sumatra. In contrast, household consumption in a region has a weak influence on the economy in the surrounding area.
INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021
Gross Regional Domestic Product (GRDP) describes an important indicator to determine the economic development and structure of a region. Economic data which generally describes the value of an area contains spatial effects and can be overcome by using spatial modeling. Maximum likelihood (ML) is a method commonly used in estimating spatial dependency models. In Big Data, the ML method is not efficient, so eigenvector spatial filtering (ESF) is used. To overcome spatial dependence, ESF adding a linear combination of eigenvectors of spatial weighting matrix to regression model specification. The objective of this study is to determine the spatial regression model of GRDP data of regencies/cities in Indonesia in 2019 with the ESF approach. Estimation of RE-ESF model uses restricted maximum likelihood (REML). The results showed that the application of ESF approaches increased the accuracy of the model based on AIC and R-square values compared to the spatial autoregressive model (SAR) and spatial error model (SEM). Factors that influence GRDP Indonesia in 2019 are the original local government revenue, the number of workers, and the human development index. The RE-ESF improves the accuracy of regression coefficient estimation with more efficient computation time.
2016
The spatial econometrics models has been developed covering from estimation method, selection of appropriate weight matrix (W) and the issue of spatial spillover. In regional science, spatial spillovers are the main interest. They can be defined as the impact of changes to explanatory variables in a particular unit i on the dependent variable values in other units j (i≠j). The spatial spillover appears because there are endogenous interaction effects among the dependent variable and exogenous interaction effects among the explanatory variables. There are several spatial econometrics model that have been used to cover those interaction effects i.e.: General Nesting Spatial model (GNS), Spatial Lag Combined model (SAC), Spatial Durbin Error Model (SDEM), Spatial Lag Model (SEM) and Spatial Lag of X model (SLX). Compared to the other models, the last model is the simplest model proposed to estimate spatial spillover effect Vega et al (2015) have compared this model to estimate the spil...
Issues in applying spatial autocorrelation on Indonesia's provincial income growth analysis
Research in regional growth analysis has acknowledged the importance of spatial effects as part of the analysis. Recently, there were several attempts to apply regional growth regression in Indonesia that raise the possible necessity to implement spatial effects in the growth regression. However, as the largest archipelagic country in the world, Indonesia has distinctive features in relation to spatial analysis that can hamper the application of spatial effects. The aim of this study is to investigate the necessity and the issues in applying spatial effects on Indonesia’s provincial income per capita growth by introducing the spatial lag and error into the growth regression. The exercise shows the existing problems in applying spatial effects on Indonesia’s regional growth regression. Moreover, the conclusion of the growth regression is hardly changed by the inclusion of spatial effects.
SSRN Electronic Journal, 2014
In the last decade, counties of Aceh Province was transformed into twenty three counties from ten. This tranformation implies to differencing on economic growth these counties between before and after transformation. finding out the conditional convergence economic growth when spatial effect is included. This research is developing in spatial econometrics study. This research is using descriptive statistical analysis; continue with Spatial Durbin Model (SDM) approach which is show significance pattern of the perfomance interpendence. Exploratory Spatial Data Analysis (ESDA) by using Statistical Analysis System (SAS) and Minitab Software. Generally, our results suggests conditional convergence economic growth after transformation among counties in Aceh when spatial effect is included.