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Latipah, Nurul. "ANALISIS PERBANDINGAN MODEL REGRESI LINIER BERGANDA, SPATIAL DURBIN ERROR MODEL (SDEM), DAN SPATIAL LAG X (SLX) DALAM PERMODELAN DATA INDEKS PEMBANGUNAN MANUSIA (IPM) DI PROVINSI KALIMANTAN SELATAN." RAGAM: Journal of Statistics & Its Application 3, no. 1 (2024): 1. http://dx.doi.org/10.20527/ragam.v3i1.11622.
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This research aims to determine the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag In this research there are three independent variables, namely poverty severity (2022), population density (2022) and pure participation rate (2019), while the dependent variable is the human development index (2022). This research data is secondary in nature, namely obtained from the website of the South Kalimantan Central Statistics Agency. Based on the results and discussion, it is concluded that the best model from the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag x (SLX) in modeling human development index (HDI) data in South Kalimantan province is the spatial durbin error model (SDEM). This is because the spatial durbin error model (SDEM) has the smallest AIC value compared to the multiple linear regression model, and spatial lag x (SLX).
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Mukrom, Maghfiroh Hadadiah, Hasbi Yasin, and Arief Rachman Hakim. "PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL." Jurnal Gaussian 10, no. 1 (2021): 44–54. http://dx.doi.org/10.14710/j.gauss.v10i1.30935.
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Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.
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Lacombe, Donald J., and James P. LeSage. "Using Bayesian posterior model probabilities to identify omitted variables in spatial regression models." Papers in Regional Science 94, no. 2 (2013): 365–83. http://dx.doi.org/10.1111/pirs.12070.
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AbstractLeSage and Pace (2009) consider the impact of omitted variables in the face of spatial dependence in the disturbance process of a linear regression relationship and show that this can lead to a spatial Durbin model. Monte Carlo experiments and Bayesian model comparison methods are used to distinguish between spatial error and Durbin model specifications that arise with varying levels of correlation between included and omitted variables. The Monte Carlo results suggest use of the common factor relationship developed in Burridge (1981) as a way to test for the presence of omitted variables bias influencing specific explanatory variables.
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Chaturvedi, Anoop, Shalabh, and Sandeep Mishra. "Generalized Bayes Estimator for Spatial Durbin Model." Journal of Quantitative Economics 19, S1 (2021): 267–85. http://dx.doi.org/10.1007/s40953-021-00271-x.
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Bekti. "MAXIMUM LIKELIHOOD ESTIMATION FOR SPATIAL DURBIN MODEL." Journal of Mathematics and Statistics 9, no. 3 (2013): 169–74. http://dx.doi.org/10.3844/jmssp.2013.169.174.
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Xiao Li. "The Construction of Economic Education Model Based on the Spatial Durbin Model." Journal of Electrical Systems 20, no. 6s (2024): 619–29. http://dx.doi.org/10.52783/jes.2714.
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This research attempts to address the complexities and the nuances that are a part of the economic education by proposing an innovative model that is grounded in the Spatial Durbin Model (SDM). The spatial interdependencies which are crucial for the understanding of the regional variations in the economic implications and the efficacy of the educational policies are sometimes neglected by the conventional main frames of the economic education. The integration of the SDM, which is a sophisticated spatial econometric tool, aims to offer a thorough and detailed understanding of the intricate relationships between the education and the economic development in context of the diverse geographics. The model that is proposed in this research study extends beyond the traditional linear regressions as it accounts for the spatial autocorrelation and the spatial lag effects which thereby captures the spatial spillover and the diffusion of the educational investments and policies. With the help of the empirical analysis which moves forward by the utilization of the spatial econometric techniques such as the spatial lag and the spatial error models, strives to explore the spatial dynamics of the economic education while identifying the spatial patterns, the dependencies involved and the disparities that come up in the attainment of the educational and the economic performance. By incorporation of the spatial dimensions into the economic education model, the policymakers and the educators are able to get their hands on the in depth insights. This is into the spatially differentiated impacts of the educational interventions which tends to enable a more targeted and a more effective design and implementation of the policy. Furthermore, the proposed model also aims to facilitate the identification of the spatially targeted strategies in order to address the regional disparities with respect to the educational outcomes and the economic growth. Hence, it contributes to a more inclusive and a sustainable development trajectory. Through the interdisciplinary effort of bridging the economics, education and the spatial analysis, this research extends a valuable main frame for the policymakers, educators and the researchers which helps in attaining better understanding and helps to address the multifaceted challenges as well as the opportunities in the economic education and the regional development. Ultimately, this has enhanced the understanding of the spatial dimensions of the economic education which leads to the evidence based policies and interventions which are aimed at nurturing the equitable and resilient economic growth. In this paper, we utilize the spatial Durbin model as a framework to investigate the role that advanced degrees play in driving technological innovation in various parts of the world. Spatial self-correlation and instability in the geographic distribution of post-graduates in China were shown by data from the regional panel in China between 2004 and 2018. Advanced degrees facilitated the advancement of cutting-edge technologies.
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Fauzi, Fatkhurokhman Fauzi, Gabriella Hilary Wenur, and Rochdi Wasono. "SPATIAL DURBIN MODEL OF UNEMPLOYMENT RATE IN CENTRAL JAVA." Parameter: Journal of Statistics 3, no. 1 (2023): 7–18. http://dx.doi.org/10.22487/27765660.2023.v3.i1.16423.
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Unemployment is a labor problem that is often faced by developing countries like Indonesia. The number of unemployed in Indonesia has fluctuated from year to year, including in Central Java Province. One of the efforts made to overcome this problem is to know the factors that influence unemployment. The region effect greatly affects the open unemployment rate. Modeling involving area effects is very precise, one of which is the Spatial Durbin Model (SDM). In this study, modeling of the open unemployment rate was carried out using a spatial approach in each district/city in Central Java. The models used in this study are Ordinary Last Square (OLS), Spatial Auto Regressive (SAR), Spatial Error Models (SEM), Spatial Durbin Model (SDM), Spatial Error Durbin Model (SDEM). The five methods were evaluated using the Akaike Information Criteria (AIC). The spatial weighting used in this study is Queen Contiguity. Based on the smallest AIC value (115.42), the best method in this study is HR. Meanwhile, the significant factors are the percentage of labor force participation rate (X1), the number of poor people (X4), the lag of economic growth, the lag of poverty, and the lag of the district/city minimum wage
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Alvitiani, Siska, Hasbi Yasin, and Mochammad Abdul Mukid. "PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL." Jurnal Gaussian 8, no. 2 (2019): 220–32. http://dx.doi.org/10.14710/j.gauss.v8i2.26667.
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Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model
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Muradi, Hengki. "SPATIAL DURBIN MODEL UNTUK PERAMALAN INFLASI DI PULAU JAWA." Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam 2, no. 2 (2019): 149. http://dx.doi.org/10.32493/jsmu.v2i2.3322.
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Inflasi merupakan indikator makro ekonomi yang penting bagi sebuah negara. Inflasi yang rendah dan terkendali merupakan harapan bagi semua bangsa di dunia. Penelitian ini bertujuan untuk memodelkan inflasi di Pulau Jawa dengan mengguankan pendekatan model spasial durbin. Data yang digunakan adalah data sekunder BPS selama periode 2015-2018. Peubah yang digunakan untuk menduga inflasi adalah tingkat kemiskinan dan UMK. Pada penelitian ini diperoleh nilai , yang berarti model Spasial Durbin yang dibangun mampu menjelaskan variasi pada Inflasi sebanyak 64,92%, pada inflasi terdapat pengaruh spasial autoregresive. Kemudian, ada pengaruh signifikan peubah tingkat kemiskinan dan UMK terhadap inflasi, namun secara spasial keduanya tidak berpengaruh signifikan.
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Karim, Abdul. "Regional Economic Growth: A Spatial Durbin Model Approach." Jurnal Matematika MANTIK 7, no. 2 (2021): 147–54. http://dx.doi.org/10.15642/mantik.2021.7.2.147-154.
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The purpose of this study is to determine the effect of spatial dependence on Gross Regional Domestic Product (GRDP) in Central Java Province. Spatial Durbin Model (SDM) is a regression model consisting of a spatial data structure which is the development of the Spatial Autoregressive Model (SAR). There is an additional spatial effect on the component of the independent variable that is not included in the SAR model or commonly referred to as an indirect effect on the independent variable. This indicates that SDM has advantages compared to SAR because there are spatial effects on the dependent and independent variables, the spatial weighted matrix used in this study is row-normalized binary contiguity. The data used in this study is sourced from the Central Java Statistics Agency (BPS) in 2019 for 35 districts and cities, which GRDP as the dependent variable, labor, human resources, and road infrastructure as independent variables. Based on the results of the analysis, the AIC value shows that SDM is significantly better than the ordinary least square (OLS) and SAR models. SDM results show that human resources have a positive sign and a direct effect of 88.5 percent and an indirect effect of 13.1 percent. In addition, the labor variable has an indirect effect on GRDP of 22.2 percent.
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Sawitri, Dhian. "The Contribution of Road Construction on Regional Economic Development in Indonesia." Jurnal Perencanaan Pembangunan: The Indonesian Journal of Development Planning 7, no. 3 (2023): 313–33. http://dx.doi.org/10.36574/jpp.v7i3.409.
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The development process in Indonesia's provinces is expected to contribute to regional economic growth. This research aims to evaluate road construction's impact on regional economic development and the spatial spillover effect in 34 Indonesian provinces. This paper constructs Spatial Autoregressive Model (SAR), Spatial Durbin Model (SDM), Spatial Autocorrelation Model (SAC), Spatial Error Model (SEM), and Generalised Spatial Random Effect Model (GSPRE) for 34 provinces in Indonesia from 2010 to 2020 to capture the effect of road construction on regional economic development and spatial spillover effect. Compared to other models, the spatial Durbin model has the minimum Akaike information criterion (AIC) value, indicating that it is the most suited model. The result indicates that road infrastructure has little impact on regional economic development. Moreover, road development has a negative value and negligible spatial spillover effect.
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Laia, Marthin Luter, Rahmat Deswanto, Erma Shofi Utami, and Rokhana Dwi Bekti. "METODE SPATIAL DURBIN MODEL UNTUK ANALISIS DEMAM BERDARAH DENGUE DI KABUPATEN BANTUL." Jurnal Nasional Teknologi Terapan (JNTT) 3, no. 2 (2021): 1. http://dx.doi.org/10.22146/jntt.64246.
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Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus which is transmitted through the bite of the Aedes aegepty and Aedes albopictus mosquitoes which are widespread in homes and public places throughout the territory of Indonesia. The high number of DHF cases in Bantul Regency, Indonesia is an indication that eradication of Aedes aegepty mosquitoes and Aedes albopictus mosquitoes has not succeeded in the Bantul Regency. Spatial Regression is an analysis that evaluates the relationship between one variable with several other variables by providing spatial effects in several locations that are the center of observation. Three type of models are Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). This study uses secondary data in 2017 in Bantul Regency, Special Region of Yogyakarta, Indonesia. The dependent variable is DHF cases and the independent variables are medical personnel and health facilities in each sub-district. The spatial model used is SDM. Based on Moran’s I test, there was a spatial autocorrelation about DHF among sub-district, so the spatial model can be used. The durbin spatial model gives the result that all estimation parameters in SDM model have P value less than α = 5%, so that medical personnel and health facilities significantly affect dengue cases in Bantul Regency. Keywords: dengue hemorrhagic fever, moran’s I test, spatial durbin model.
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Hakim, Arief Rachman, Budi Warsito, and Hasbi Yasin. "Live Expectancy Modelling using Spatial Durbin Robust Model." Journal of Physics: Conference Series 1655 (October 2020): 012098. http://dx.doi.org/10.1088/1742-6596/1655/1/012098.
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Zhang, Hong, Xiaojie Gao, and Keqiang Dong. "The Global Transmission of Stock Market: A Spatial Analysis." Mathematical Problems in Engineering 2022 (July 5, 2022): 1–8. http://dx.doi.org/10.1155/2022/5049014.
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The stock markets, exhibiting complex self-correlation or cross-correlation over a broad range of time scales, are correlated not only in time but also in space. The conventional spatial weight matrix in the econometric analysis is short of economic relation between nonadjacent economic entities. Therefore, this paper applies the detrended cross-correlation analysis coefficient and partial correlation coefficient to analyze the global spatial interaction. This study computes the spatial Moran’s I value by the two types of weight matrix for the 15 typical stock indices around the world, to explore the spatial agglomeration phenomenon. Then, the Spatial Durbin Model is applied to investigate the transmission of the stock market. The result from the Moran’s I value indicates that the 15 typical stock indices are spatially correlated. The result of the Spatial Durbin Model gives the relationship among the closing price, the opening price, the highest price, and the lowest price.
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Gao, Lu-Hui, Guo-Qing Wang, and Jing Zhang. "Industrial Agglomeration Analysis Based on Spatial Durbin Model: Evidence from Beijing-Tianjin-Hebei Economic Circle in China." Complexity 2021 (July 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/3788784.
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Based on the data of Beijing-Tianjin-Hebei Economic Circle from 2010 to 2019, this paper uses the spatial Durbin model to empirically analyze the impact of financial development and technological innovation on industrial agglomeration. The following are the conclusions of this study: (1) financial development has a positive effect on industrial agglomeration; however, a significant difference exists in the weight effect of the geographic distance matrix compared to the weight of the economic distance matrix; (2) in the spatial Durbin model with two matrix weights, technological innovation has a significant positive effect on industrial agglomeration; and (3) in the spatial Durbin model with two matrix weights, the interaction has a significant negative effect on industrial agglomeration. Therefore, the government should further implement the coordinated development strategy, promoting regional technological innovation for a long time to realize its integration with financial development.
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Zhu, Yanli, Xiaoyi Han, and Ying Chen. "Bayesian estimation and model selection of threshold spatial Durbin model." Economics Letters 188 (March 2020): 108956. http://dx.doi.org/10.1016/j.econlet.2020.108956.
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Lokang, Yulita Putri, and Ignatius Aris Dwiatmoko. "ANALISIS REGRESI SPASIAL DURBIN UNTUK MENGANALISIS FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN PERSENTASE PENDUDUK MISKIN." Jurnal Ilmiah Matrik 21, no. 2 (2019): 118–27. http://dx.doi.org/10.33557/jurnalmatrik.v21i2.565.
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The percentage of poor people is the percentage of the population who have a monthly per capita expenditure below the poverty line. In this study, spatial Durbin regression used to know the factors which is giving effect to the percentage of poor population with maximum likelihood method to estimate parameters. This method used because it can maximize the probability of occurrence of each parameter. From 6 independent variables that are thought to be related to the percentage of poor people, there are only 4 independent variables that can be modeled by the spatial Durbin regression model because of the presence of significant spatial autocorrelation based on the Moran Index test. That are school participation rates aged 16-18 years, inflation, life expectancy at birth, and the human development index. The measure of the goodness of the Durbin spatial regression model calculated by looking for the value of R2 is 68,4%
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Suaib, Tri Putri Andayani, Junaidi Junaidi, and Fadjryani Fadjryani. "PENERAPAN SPATIAL DURBIN MODEL (SDM) PADA INDEKS PEMBANGUNAN GENDER DI PULAU SULAWESI." Majalah Ilmiah Matematika dan Statistika 22, no. 1 (2022): 82. http://dx.doi.org/10.19184/mims.v22i1.29581.
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The Gender Development Index (GDI) is a development index of the quality of human life that is more concerned with gender status. GDI can be used to determine human development between males and females. This study uses the Spatial Durbin Model (SDM) method. The SDM method was formed due to the spatial influence on the dependent and independent variables. The purpose of this study is to determine the GDI model in Sulawesi Island and the factors that influence it. The factors that have a significant effect on the Gender Development Index (GDI) in Sulawesi Island using the Spatial Durbin Model (SDM) are Life Expectancy, per capita contests, average years of schooling, and labor force participation.Keywords: GDI, AIC, SDMMSC2020: 62H11
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Hakim, Arief Rachman, Hasbi Yasin, and Agus Rusgiyono. "MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL." MEDIA STATISTIKA 12, no. 2 (2019): 152. http://dx.doi.org/10.14710/medstat.12.2.152-163.
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Central Java in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find out what factors that the regression analysis method can use to find out what factors influence the life expectancy. But in observations found data that have a spatial effect (location) called spatial data, a spatial regression method was developed such as linear regression analysis by adding spatial effects. One form of spatial regression is Spatial Durbin Model (SDM) which has a form like the Spatial Autoregressive Model (SAR). The difference between the two if in the SAR model the effect of spatial lag taken into account in the model is only on the response variable (Y) but in the SDM method, effect of spatial lag on the predictor variable (X) and response (Y) are also taken into account. Selection of the best model using Mean Square Error (MSE), obtained by the MSE value of 1.156411, the number mentioned is relatively small 0, which indicates that the model is quite good.
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Aspiansyah, Aspiansyah, and Arie Damayanti. "Model Pertumbuhan Ekonomi Indonesia: Peranan Ketergantungan Spasial." Jurnal Ekonomi dan Pembangunan Indonesia 19, no. 1 (2019): 62–83. http://dx.doi.org/10.21002/jepi.v19i1.810.
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This study aims to examine the role of spatial dependence on Indonesia’s regional economic growth based on panel data of all provinces in Indonesia during 1990–2015. By using spatial durbin model, the authors found that spatial dependence plays an important role in achieving regional economic growth in Indonesia. Indonesia’s regional economic growth model that controls spatial dependence, yields better estimates than growth model that does not control spatial dependence. The researchers also found positive spatial spillover to Indonesia’s regional economic growth sourced from other region’s economic growth and initial per capita incomes, as well as population growth in other regions. ============================ Penelitian ini bertujuan untuk mengkaji peranan ketergantungan spasial terhadap pertumbuhan ekonomi regional Indonesia berdasarkan data panel seluruh provinsi di Indonesia selama tahun 1990–2015. Dengan menggunakan model durbin spasial, penulis menemukan bahwa ketergantungan spasial berperan penting dalam pencapaian pertumbuhan ekonomi regional di Indonesia. Model pertumbuhan ekonomi regional Indonesia yang mengontrol ketergantungan spasial menghasilkan estimasi yang lebih baik daripada model pertumbuhan ekonomi regional Indonesia yang tidak mengontrol ketergantungan spasial. Peneliti jugamenemukan terjadinya spatial spillover yang positif terhadap pertumbuhan ekonomi regional Indonesia yang bersumber dari pertumbuhan ekonomi wilayah lain, pendapatan per kapita awal dari wilayah lain dan pertumbuhan penduduk wilayah lain.
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Khofifah, Hanna Nurul. "Robust Spatial Durbin Model (RSDM) untuk Pemodelan Tingkat Pengangguran Terbuka (TPT) di Provinsi Jawa Barat." Jurnal Riset Statistika 1, no. 2 (2022): 135–42. http://dx.doi.org/10.29313/jrs.v1i2.522.
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Abstract. Spatial regression is used to determine the relationship between the response variable and predictor variables that have a spatial influence in it. If the response variables and predictor variables have a spatial effect, then the model formed is the Spatial Durbin Model (SDM). Outliers in spatial data are often found when conducting the research. Robust Regression is generally used to overcome outliers. Robust regression used in spatial data is a combination of the SDM methods and Robust regression, thus it form a method called Robust Spatial Durbin Model (RSDM). The estimation method used is the Maximum Likelihood type estimation (M-estimator), with expectation that it could accomodate the existence of outliers in the spatial regression model. In this study the response variable is the Open Unemployment Rate, and the predictor variable is the Human Development Indeks, District/City Minimum Wage, Dependency Ratio. Labor Force Participation Rate, education level, and number of poor population. From the results of the study the value Adjusted R2 0,9850 which means 98,5% TPT is influenced by the variables in the model. It means that RSDM is a good model to explain TPT in West Java Province.
 Abstrak. Regresi spasial digunakan untuk mengetahui hubungan antara variabel respon dan variabel prediktor yang memiliki pengaruh spasial di dalamnya. Jika dalam variabel respon dan variabel prediktor mempunyai pengaruh spasial, maka model yang dibentuk yaitu Spatial Durbin Model (SDM). Pencilan pada data spasial sering ditemukan ketika melakukan penelitian. Secara umum metode yang dapat digunakan untuk mengatasi pencilan yaitu regresi robust. Regresi robust yang digunakan pada data spasial merupakan kombinasi dari metode SDM dan Regresi robust sehingga membentuk suatu metode yang disebut Robust Spatial Durbin Model (RSDM). Metode estimasi yang digunakan yaitu estimasi Maximum Likelihood type (M-estimator), dengan harapan dapat mengakomodasi keberadaan pencilan dalam model regresi spasial. Pada penelitian ini variabel respon adalah Tingkat Pengangguran Terbuka (TPT) dan variabel prediktor adalah Indeks Pembangunan Manusia (IPM), Upah Minimum Kabupaten/Kota (UMK), Rasio Ketergantungan, Tingkat Partisipasi Angkatan Kerja (TPAK), Tingkat Pendidikan, dan Jumlah Penduduk Miskin. Dari hasil penelitian diperoleh nilai Adjusted R2 sebesar 0,9850 yaitu 98,5% TPT dipengaruhi variabel yang ada didalam model. Hal ini menunjukkan RSDM merupakan model yang baik untuk menjelaskan TPT di Provinsi Jawa Barat.
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P. "Spatial Durbin Model to Identify Influential Factors of Diarrhea." Journal of Mathematics and Statistics 8, no. 3 (2012): 396–402. http://dx.doi.org/10.3844/jmssp.2012.396.402.
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Mur, Jesús, and Ana Angulo. "The Spatial Durbin Model and the Common Factor Tests." Spatial Economic Analysis 1, no. 2 (2006): 207–26. http://dx.doi.org/10.1080/17421770601009841.
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Tientao, A., D. Legros, and M. C. Pichery. "Technology spillover and TFP growth: A spatial Durbin model." International Economics 145 (May 2016): 21–31. http://dx.doi.org/10.1016/j.inteco.2015.04.004.
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Marliana, Ana. "Pemodelan Regresi Global (Glm) dan Regresi Spasial (Sar Dan Sdm) Pada Kasus Indeks Pembangunan Manusia Di Provinsi Kalimantan Selatan." RAGAM: Journal of Statistics & Its Application 2, no. 2 (2024): 104. http://dx.doi.org/10.20527/ragam.v2i2.11614.
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The Human Development Index (HDI) is a parameter that functions to assess the success of the quality of human life. The factors used in the research are the severity of poverty, population density and net participation rate. The research carried out aims to see what factors influence the HDI in South Kalimantan Province in 2022, the HDI value for South Kalimantan Province is below the Indonesian HDI value and quite a few regencies/cities in South Kalimantan Province have HDI values below the HDI value. Indonesia. The statistical analysis used is a spatial approach, where the SAR and HR spatial regression models will be searched. The Global Regression Model (GLM) obtained in this study is , while the Spatial Autoregressive (SAR) model is and Spatial Durbin Model is The best model that can be obtained is the Spatial Durbin Model (SDM) with an AIC value of 52,82654 and an value of 95,86%.
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Musa, M., and M. Fauziyah. "Pemodelan Ekonometrika Spasial Persentase Penduduk Miskin di Provinsi Papua." JURNAL ILMIAH MATEMATIKA DAN TERAPAN 19, no. 2 (2022): 192–203. http://dx.doi.org/10.22487/2540766x.2022.v19.i2.16158.
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Kemiskinan merupakan salah satu permasalahan yang terjadi di Indonesia. Pada tahun 2020 persentase penduduk miskin di Indonesia sebesar 9,78%, dengan persentase tertinggi berada di Provinsi Papua yaitu sebesar 911,37 ribu orang (26,64%). Hal ini menunjukkan perlunya dilakukan suatu penelitian untuk mengetahui faktor-faktor yang berpengaruh terhadap persentase penduduk miskin di Provinsi Papua. Penelitian ini menggunakan pemodelan ekonometrika spasial dengan pendekatan Spatial Autoregressive Model (SAR) dan Spatial Durbin Model (SDM). Kedua model tersebut digunakan karena hasil uji dependensi spasial menunjukkan bahwa terjadi dependensi spasial antar lokasi yang berdekatan pada variabel dependen dan beberapa variabel independen. Diperoleh hasil penelitian dengan menggunakan model regresi klasik (AIC sebesar 189,5021), SAR (AIC sebesar 188,3680), dan SDM (AIC sebesar 190,8151). Model yang paling baik digunakan berdasarkan kriteria AIC terkecil adalah SAR, dengan variabel yang berpengaruh signifikan terhadap persentase penduduk miskin di Provinsi Papua, yaitu variabel PDRB per kapita (X1), rata-rata lama sekolah (X3) dan alokasi dana desa (X5).
 Kata kunci : Ekonometrika, Kemiskinan, Spatial Autoregressive Model, Spatial Durbin Model.
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Zhou, Chuhan. "Spatial and Temporal Coupling Coordination and Influencing Factors of Digital Inclusive Finance and Ecological Civilization Construction in China." Transactions on Economics, Business and Management Research 3 (December 25, 2023): 29–38. http://dx.doi.org/10.62051/vol3pp29-38.
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This paper uses the spatial coupled coordination model, spatial autocorrelation analysis and spatial Durbin model to explore the spatio-temporal pattern and influencing factors of the coupled coordination index between ecological civilization construction (ECC) and digital finance (DF). The results show that: (1) From 2011 to 2021, the coupling coordination degree between ECC and DF in China shows an increasing trend. Besides, the degree of coupling coordination has obvious spatial diversity. The eastern provinces are generally larger than the western provinces, and this pattern is relatively stable. (2) The coupling degree between neighboring provinces and cities has a strong positive correlation, and it is growing every year recently. (3) The test results of spatial Durbin model show that the human capital, economic growth, technological innovation and trade factors can help coupling coordination to raise. cities development and industrial structure have negative effects on coupling coordination.
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Amalia, Tika, and Lisnur Wachidah. "Fixed Effect Panel Spatial Durbin Error Model pada Indeks Pembangunan Manusia di Jawa Barat Tahun 2017-2020." Bandung Conference Series: Statistics 2, no. 2 (2022): 44–52. http://dx.doi.org/10.29313/bcss.v2i2.3050.
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Abstract. The statistical method used to determine the causal relationship of the independent variable to the dependent variable that has a dependency relationship between observations or regions is the spatial regression method. One approach in Spatial Regression Analysis is the Spatial Durbin Error Model (SDEM). Spatial Durbin Error Model (SDEM) is a regression model that has a spatial lag on the error variable (ɛ) and also on the independent variable (X). In addition, because the data used consists of cross-sectional units and time series, a panel data model is used with one approach, namely the fixed effect. In this study, the data used is the Human Development Index (HDI) data in West Java Province by Regency/City in 2017-2020. The variable used in this study is the Human Development Index (Y) as the dependent variable. Average Length of School (X1), Poor Population (X2), Life Expectancy (X3), Per capita Expenditure (X4), and Number of Health Facilities (X5) as independent variables. Based on the analysis, the Adjusted R-Square value is 99.99% and the five independent variables directly affect the Human Development Index, while the variables that have a spatial effect are Average Length of School, Number of Poor Population, and Number of Health Facilities.
 Abstrak. Metode statistika yang digunakan untuk menentukan hubungan sebab-akibat dari peubah bebas terhadap peubah tak bebas yang memiliki hubungan ketergantungan antar pengamatan atau wilayah adalah metode regresi spasial. Salah satu pendekatan dalam Analisis Regresi Spasial yaitu Spatial Durbin Error Model (SDEM). Spatial Durbin Error Model (SDEM) merupakan model regresi yang memiliki spasial lag pada variabel error (ɛ) dan juga pada variabel bebas (X). Selain itu, karena data yang digunakan terdiri dari unit cross-section dan time-series maka digunakan model data panel dengan salah satu pendekatan yaitu fixed effect. Dalam penelitian ini, data yang digunakan adalah data Indeks Pembangunan Manusia (IPM) di Provinsi Jawa Barat menurut Kabupaten/Kota tahun 2017-2020. Variabel yang digunakan pada penelitian ini yaitu Indeks Pembangunan Manusia (Y) sebagai variabel tak bebas. Rata-rata Lama Sekolah (X1), Penduduk Miskin (X2), Angka Harapan Hidup (X3), Pengeluaran Perkapita (X4), dan Jumlah Fasilitas Kesehatan (X5) sebagai variabel bebas. Berdasarkan analisis diperoleh nilai Adjusted R-Square sebesar 99,99% dan kelima variabel bebas tersebut berpengaruh secara langsung terhadap Indeks Pembangunan Manusia, sedangkan variabel yang memiliki spatial effect yaitu Rata-rata Lama Sekolah, Jumlah Penduduk Miskin, dan Jumlah Fasilitas Kesehatan.
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Farida, Yuniar, Mayandah Farmita, Putroue Keumala Intan, Hani Khaulasari, and Achmad Teguh Wibowo. "MODELING CRIME IN EAST JAVA USING SPATIAL DURBIN MODEL REGRESSION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 3 (2024): 1497–508. http://dx.doi.org/10.30598/barekengvol18iss3pp1497-1508.
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The high crime rate will create unrest and losses for the community. One of the provinces with high crime rates is East Java. This study aims to analyze the factors that influence criminality in East Java to ensure appropriate crime prevention and control measures can be taken. The factors that potentially influence crime in East Java studied include population density, the number of poor people, unemployment, Human Development Index (HDI), Gross Regional Domestic Product (GRDP), and per Capita Expenditure, which are associated with geographical conditions in each region (regency/city) collected from BPS East Java in 2022. Meanwhile, the number of crimes is collected from the East Java Regional Police. This research uses a statistical method, namely the Spatial Durbin Model (SDM), which is a particular form of the Spatial Autoregressive Model (SAR) method with Queen Contiguity weighting by analyzing geographically (spatial processes). Based on the results of the analysis, it was found that the influential factors were unemployment, HDI, GRDP, and per Capita Expenditure, and the R-square result was obtained at 85.18%. This shows a relationship between spatial accessibility and crime, where unemployment, HDI, GRDP, and per Capita Expenditure in an area can affect regional vulnerability to crime
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WANG, Xiaoying, Yilong WANG, Lei SHEN, Xiaoman LI, and Qiangtao Du. "Spatial effect of informatization on China’s energy intensity：Based on spatial Durbin error model." 资源科学 43, no. 9 (2021): 1752–63. http://dx.doi.org/10.18402/resci.2021.09.04.
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Santi, V. M., A. Djuaraidah, I. M. Sumertajaya, A. Fitrianto, and W. Rahyu. "Modeling of West Java inflation with Spatial Durbin Model (SDM)." Journal of Physics: Conference Series 1869, no. 1 (2021): 012149. http://dx.doi.org/10.1088/1742-6596/1869/1/012149.
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Debarsy, Nicolas. "The Mundlak Approach in the Spatial Durbin Panel Data Model." Spatial Economic Analysis 7, no. 1 (2012): 109–31. http://dx.doi.org/10.1080/17421772.2011.647059.
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Seya, Hajime, Morito Tsutsumi, and Yoshiki Yamagata. "Income convergence in Japan: A Bayesian spatial Durbin model approach." Economic Modelling 29, no. 1 (2012): 60–71. http://dx.doi.org/10.1016/j.econmod.2010.10.022.
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Li, Jing, Yan Shen, Lili Sun, and Tao Sun. "The Impact of the Digital Economy on the High-Quality Development of Green Marketing in China's Manufacturing Industry: Applications of the Spatial Durbin Model." Rocznik Ochrona Środowiska 26 (October 1, 2024): 449–64. http://dx.doi.org/10.54740/ros.2024.043.
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The digital economy provides China's manufacturing industry with a pathway to establish new national competitive advantages and is an essential driving force for the high-quality advancement of green marketing. Utilizing data from 30 Chinese provinces from 2013 to 2022, this study employs the spatial Durbin model to investigate the spatial relationship between the digital economy and the advancement of green marketing at a high quality. The findings indicate that the growth of the digital economy significantly fosters high-quality development in green marketing and exhibits positive spatial spillover effects on neighboring regions. The Application of spatial Durbin regression to multi-province areas has revealed that the more economically developed the region, significantly influences of the digital economy on the high-quality development of green marketing, with a local siphoning phenomenon currently visible in the eastern region. The western region exhibits a spatially positive spillover effect from the high-quality development of green marketing in other regions. Furthermore, developing the digital economy in neighboring regions significantly enhances the high-quality development of green marketing in this region. The findings remain stable when evaluated using the alternative variables method and the control fixed effects approach.
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Du, Qunyang, Danqing Deng, and Jacob Wood. "Differences in Distance and Spatial Effects on Cross-Border E-Commerce and International Trade." Journal of Global Information Management 30, no. 2 (2022): 1–24. http://dx.doi.org/10.4018/jgim.20220301.oa6.
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Distance and space are important factors affecting international trade, but they have different effects on cross-border e-commerce (CBE) due to the creation of the Internet. This study utilizes spatial autocorrelation, the multi-dimension gravity model and the Spatial Durbin model to conduct an comparative analysis of international trade and CBE within one-belt one-road (BR) countries. Our study obtained several key findings. Firstly, the spatial autocorrelation effect which exists in international trade does not exist in CBE. Secondly, the geographical distance effect of CBE is not significant, which is different from that of international trade. Thirdly, CBE is affected by GDP, culture, policy and institution distances which is not entirely consistent with international trade. Finally, the Spatial Durbin model shows that the spillover effect of CBE and international trade are both significant in the inverse distance weight matrix. These findings provide not only important theoretical contributions but also a practical guide for Government policy makers of the BR and CBE.
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Hao-Chen, Huang, Huang Jung-Hua, and Liao Ting-Hsiu. "The Spatial Spill-over Effects of Fiscal Expenditures and Employment Structures on Disposable Personal Income: Evidence from Taiwan." Empirical Economics Review 12, no. 3-4 (2023): 23–40. https://doi.org/10.5281/zenodo.8374144.
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<strong>Abstract: </strong>With the main purpose of exploring the determinants (including fiscal expenditure and employment structure) influencing average disposable personal incomes in counties and cities, this study uses panel data of various regions in Taiwan from 2000 to 2020 for spatial econometric model analysis. First, global spatial autocorrelation and local spatial autocorrelation are used to analyze the overall spatial agglomeration degree of average disposable personal incomes in counties and cities and the agglomeration of specific areas. Results of the spatial Durbin model analysis show average disposable personal incomes in counties and cities are influenced by local general government expenditures, expenditures on education, science, and culture, and the proportion of employees with a college education or above. In terms of spatial spillover effects, average disposable personal incomes in counties and cities are influenced by educational, scientific, and cultural expenditures, as well as the proportion of employees with a college education or above in neighboring counties and cities. The results can serve as a reference for the central or local governments to make policies. <strong>Keywords: </strong>Fiscal Expenditure, Employment Structure, Disposable Personal Income, Spatial Autocorrelation, Spatial Durbin Model
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Chen, Sibei. "Study of the Digital Inclusive Finance, Entrepreneurial Activism and Rural Revitalization Based on Provincial Panel Data in China." BCP Business & Management 25 (August 30, 2022): 570–78. http://dx.doi.org/10.54691/bcpbm.v25i.1883.
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According to the Central Document No. 1 of 2022, digital technology-driven rural financial inclusion will continue to take root across the country, and innovative applications of digitalization in financial services will continue to emerge, providing innovative solutions for rural revitalization. On the basis of the rural revitalization index measured by the entropy weight method, mediation effect model and spatial Durbin model are empirically tested to examine the inherent mechanism. Firstly, the findings indicate that digital inclusive finance significantly contributes to rural revitalization, even after modifying the method of robustness testing to employ the entropy TOPSIS method and selecting historical data as an instrumental variable. Second, stimulating mass entrepreneurship is an effective mechanism for digital inclusive finance to promote rural revitalization. Third, The positive impact of digital inclusive finance is accompanied by spatial spillovers, as demonstrated by spatial autoregression models and spatial Durbin models.
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Liang, Xi, and Pingan Li. "Empirical Study of the Spatial Spillover Effect of Transportation Infrastructure on Green Total Factor Productivity." Sustainability 13, no. 1 (2020): 326. http://dx.doi.org/10.3390/su13010326.
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Transportation infrastructure promotes the regional flow of production. The construction and use of transportation infrastructure have a crucial effect on climate change, the sustainable development of the economy, and Green Total Factor Productivity (GTFP). Based on the panel data of 30 provinces in China from 2005 to 2017, this study empirically analyses the spatial spillover effect of transportation infrastructure on the GTFP using the Malmquist–Luenberger (ML) index and the dynamic spatial Durbin model. We found that transportation infrastructure has direct and spatial spillover effects on the growth of GTFP; highway density and railway density have significant positive spatial spillover effects, and especially-obvious immediate and lagging spatial spillover effects in the short-term. We also note that the passenger density and freight density of transportation infrastructure account for a relatively small contribution to the regional GTFP. Considering environmental pollution, energy consumption, and the enriching of the traffic infrastructure index system, we used the dynamic spatial Durbin model to study the spatial spillover effects of transportation infrastructure on GTFP.
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King, Maxwell L., and Merran A. Evans. "Locally Optimal Properties of the Durbin-Watson Test." Econometric Theory 4, no. 3 (1988): 509–16. http://dx.doi.org/10.1017/s0266466600013426.
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Although originally designed to detect AR(1) disturbances in the linear-regression model, the Durbin-Watson test is known to have good power against other forms of disturbance behavior. In this paper, we identify disturbance processes involving any number of parameters against which the Durbin–Watson test is approximately locally best invariant uniformly in a range of directions from the null hypothesis. Examples include the sum of q independent ARMA(1,1) processes, certain spatial autocorrelation processes involving up to four parameters, and a stochastic cycle model.
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Zhu, Di, Yefei Li, Ejimofor Bruno Chiedozi, and Hui Pan. "The Impact of Foreign Direct Investment on Income Convergence in China - A Spatial Panel Data Analysis." Journal of Economics and Management Sciences 4, no. 4 (2021): p1. http://dx.doi.org/10.30560/jems.v4n4p1.
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After taking into account the spatial dependence effects in the panel data consisting of all 31 provinces, direct-controlled municipalities, and autonomous regions in China between the years 1998 and 2017, it found significant spatial autocorrelation effects in both traditional absolute and conditional β income convergence models. At the national level, using the spatial econometric models (Spatial Error Model for absolute convergence and Spatial Durbin Model for conditional convergence), the analysis shows that in the past 19 years from 1999 to 2017, there is no absolute β income convergence. However, there is conditional β income convergence after controlling for all growth factors, while the positive effect of fixed asset investment on regional economic growth is significant, and the effect of population growth is significantly negative. The other growth factors such as FDI inflow, export, and higher education enrollment were surprisingly found no statistically significant effects on regional economic growth. From regional level (Spatial Durbin Model and Spatial Lag Model), there is no conditional β income convergence within each four economic regions. Nonetheless, the northeast region showed an income divergence trend, where only the fixed asset investment is positively significant. This study results imply that China should continue to improve fixed asset investment and control population growth to stimulate regional economic growth and income convergence.
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Gu, Yimiao, Wanwan Liu, and Hui Shan Loh. "Port Efficiency Based on the Super-Efficiency EBM-DEA-SDM Model: Empirical Evidence from China." Future Transportation 3, no. 1 (2022): 23–37. http://dx.doi.org/10.3390/futuretransp3010002.
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Guangdong Province enjoys a very high economic status in China especially in terms of port construction. In response to the port development directions in China, the Guangdong government released a policy about the construction of Guangdong ports in the next 15 years. Based on the policy, this study proposes to evaluate the port efficiency of major ports in Guangdong Province during 2011–2020 using the Super-efficiency EBM-DEA model that considers undesirable outputs, and the spatial effect of port efficiency and its influencing factors is further analyzed using the spatial Durbin model. The empirical results shows that the overall port efficiency in Guangdong Province is not high and varies widely among port clusters, thereby lacking synergistic development. The results of the spatial Durbin model show that port efficiency is positively correlated with the level of economic development, port-city relationship and transportation structure, as well as negatively correlated with the efficiency of neighboring ports. The findings have a far-reaching impact on the development of port construction.
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Tereshchenko, Dmitrii. "Interregional Effects of Innovations in Russia: Analysis from the Bayesian Perspective." Spatial Economics 20, no. 1 (2024): 125–43. http://dx.doi.org/10.14530/se.2024.1.125-143.
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This study analyzes the interregional effects of innovation in Russia. The hypothesis of the presence of interregional effects is tested by combining the methods of spatial econometrics and Bayesian approach. Using panel data on Russian regions for the period from 2000 to 2021, the author calculates posterior probabilities for a set of spatial regression models that model interregional effects of innovation in different ways. Within the framework of Bayesian approach 6 models were selected for comparison: model without spatial effects (OLS), model with spatial lag of the dependent variable (SAR), model with spatial lag of the error (SEM), model with spatial lags of the explanatory variables (SLX), spatial Durbin model with lags of dependent and explanatory variables (SDM), as well as spatial Durbin model with lags of the explanatory variables and error (SDEM). Based on our calculations, we can conclude that the spatial correlation of innovation in Russian regions is not as strong as it has been assumed in previous studies. This can be considered as evidence in favor of the fact that the concept of interregional spillovers of innovations is poorly consistent with the historical, institutional and territorial peculiarities of Russia, and the methods generally accepted in other countries for such analysis are unsuitable in the Russian context. The results obtained can be taken into account in further research involving spatial modeling of regional innovations. More attention should be paid to the spatial effects of explanatory variables, in particular, interregional spillovers of R&D expenditures, as well as dynamics in the innovation process
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Folfas, Paweł. "Income Absolute Beta-Convergence of NUTS 3 Level Regions in New EU Member States before and During a Crisis." Folia Oeconomica Stetinensia 16, no. 2 (2016): 151–62. http://dx.doi.org/10.1515/foli-2016-0031.
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Abstract This paper is aimed at answering the question of whether absolute income (GDP per capita) beta-convergence exists in the case of regions in new EU Member States before the period of 2000–2008 and during the 2008–2011 crisis. The sample consists of 211 regions (NUTS 3-level) of Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia and Slovakia. The research is based on econometric models, namely on the spatial lagged model (SLM), the spatial error model (SEM) and the Durbin spatial model which contrary to the ordinary least squares the (OLS) model include possible spatial dependencies. The SLM and SEM models as well as the Durbin spatial model detect the absolute income beta-convergence on the level of about 1% during the years 2000–2008. Additionally, models do not confirm the existence of absolute income beta-convergence during the crisis of 2008–2011. SLM models (which offer the most reliable findings) find a spatial correlation (measured by the rho-parameter) at a level of 0.75 during 2000–2008 and 0.35 during 2008–2011. Thus, absolute income beta-convergence in the case of NUTS 3 regions in 10 new EU Member States existed only in the pre-crisis period and this period is characterized by much stronger spatial dependencies than the period of 2008–2011.
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Ackah, Ishmael. "Does bad company corrupt good character? A spatial econometric analysis of oil resource management in Africa." International Journal of Energy Sector Management 11, no. 3 (2017): 480–502. http://dx.doi.org/10.1108/ijesm-10-2016-0002.
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Purpose A widely held belief before the 1990s – referred to as the oil-blessing hypothesis – was that oil discovery and production should promote economic growth and development and lead to poverty reduction. However, the so-called ‘oil-curse’ hypothesis, postulated by Sachs and Warner in 1995, challenged this belief, thus provoking a heated debate on the theme. The oil-curse hypothesis has been traditionally tested by means of cross-sectional and panel-data models. The author goes beyond these traditional methods to test whether the presence of spatial effects can alter the hypothesis in oil-producing African countries. In particular, this paper aims to investigate the effects on economic growth of oil production, oil resources and oil revenues along with the quality of democratic institutions, investment and openness to trade. Design/methodology/approach A Durbin spatial model, a cross-sectional model and panel-data model are used. Findings First, the validity of the spatial Durbin model is vindicated. Second, consistently with the oil-curse hypothesis, oil production, resources, rent and revenues have a negative and generally significant effect on economic growth. This result is robust for across the panel data, spatial Durbin and spatial autoregressive models and for different measures of spatial proximity between countries. Third, the author finds that the extent to which the business environment is perceived as benign for investment has a positive and marginally effect on economic growth. Additionally, economic growth of a country is further stimulated by a spatial proximity of a neighbouring country if the neighbouring country has created strong institutions protecting investments. Fourth, openness to international trade has a positive and marginally significant effect on economic growth. Originality/value This paper examines theories and studies that have been done before. However, as the related literature on the growth–resource abundance nexus has rarely examined spatial effects, this study seeks to test jointly the spatial effect and the neighbouring effect on the oil curse hypothesis.
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Taqiyyuddin, Teguh Ammar, and Muhammad Irfan Rizki. "Pemodelan Fixed Effect Panel Spatial Durbin Error Model Pada Tingkat Kemiskinan." Seminar Nasional Official Statistics 2021, no. 1 (2021): 90–98. http://dx.doi.org/10.34123/semnasoffstat.v2021i1.767.
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Permasalahan yang ada di setiap negara khususnya negara berkembang termasuk Indonesia adalah kemiskinan. Program dalam mengentaskan kemiskinan merupakan pokok tujuan dari Sustainable Development Goals (SDGs). Jawa Barat yang merupakan salah satu provinsi dengan jumlah penduduk miskin terbanyak perlu mengatasi permasalah tersebut seperti yang tertuang dalam RPJMD. Dalam hal ini pemerintah seringkali menentukan pembangunan dengan memprioritaskan pembangunan ekonomi pada daerah perkotaan ataupun pusat perekonomian yang mengakibatkan daerah lainnya tertinggal dan kemiskinan menjadi tidak merata. Hal tersebut tentunya memperlihatkan faktor yang berhubungan dengan ekonomi diduga terdapat aspek spasial sehingga harus menggunakan spasial lag variabel prediktor sebagai prediktor variabel, selain itu kemiskinan merupakan masalah multidimensial sehingga banyak faktor yang mempengaruhi tingkat kemiskinan tidak dimasukkan ke dalam pemodelan. Variabel prediktor yang tidak dimasukkan ke dalam pemodelan dinamakan omitted variables. Berdasarkan permasalahan itu, dalam mengetahui faktor-faktor kemiskinan di Jawa Barat diperlukan suatu pendekatan yang mampu mengakomodasi lag spasial prediktor variabel dan error model yang berkorelasi spasial, serta mampu mengatasi bias taksiran akibat omitted variables. Maka dalam penelitian ini dilakukan pendekatan model regresi spasial Durbin Error Model. Pembobot spasial yang digunakan yaitu queen contiguity. Berdasarkan penelitian ini didapatkan bahwa variabel Indeks Pembanguna Manusia (IPM) dan persentase penduduk berpengaruh terhadap tingkat kemiskinan di Provinisi Jawa Barat, dengan nilai R-Square sebesar 98%. Maka hasil tersebut diharapkan dapat menjadi pertimbangan bagi pemerintah Jawa Barat untuk menanggulangi masalah kemiskinan dalam upaya mencapai tujuan pertama SDGs yaitu tanpa kemiskinan.
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Wang, Xueyang, Xiumei Sun, Haotian Zhang, and Mahmood Ahmad. "Digital Economy and Environmental Quality: Insights from the Spatial Durbin Model." International Journal of Environmental Research and Public Health 19, no. 23 (2022): 16094. http://dx.doi.org/10.3390/ijerph192316094.
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Recent developments in attaining carbon peaks and achieving carbon neutrality have had enormous effects on the world economy. Digitalization has been considered a viable way to curtail carbon emissions (CE) and promote sustainable economic development, but scant empirical studies investigate the link between digitalization and CE. In this context, this study constructs the digitalization index using the entropy value method and spatial Markov chain, and the spatial Durbin model is employed to analyze its impact mechanism and influence on urban CE in 265 prefecture-level cities and municipalities in China from 2011 to 2017. The results indicate that: (1) The overall development level of the digital economy (DE) posed a significant spatial effect on urban environmental pollution. However, the effect varies according to the different neighborhood backgrounds. (2) The DE impedes urban environmental deterioration directly and indirectly through the channels of industrial structure, inclusive finance, and urbanization. (3) The development of the DE significantly reduces pollution in cities belonging to urban agglomerations, while the development of the DE escalates emissions in nonurban agglomeration cities. Finally, based on the results, important policy implications are put forward to improve the environmental quality of cities.
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Aminizadeh, Milad, Hosein Mohammadi, and Alireza Karbasi. "Determinants of fishing grounds footprint: Evidence from dynamic spatial Durbin model." Marine Pollution Bulletin 202 (May 2024): 116364. http://dx.doi.org/10.1016/j.marpolbul.2024.116364.
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Atikah, Nur, Basuki Widodo, Mardlijah, Swasono Rahardjo, Sri Harini, and Riski Dinnullah. "Monte Carlo Simulation of The Multivariate Spatial Durbin Model for Complex Data Sets." International Journal of Mathematics and Computer Science 20, no. 1 (2024): 223–33. http://dx.doi.org/10.69793/ijmcs/01.2025/widodo.
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The Multivariate Spatial Durbin Model (MSDM) is a significant advance in spatial econometrics, very relevant in the context of research problems. This model extends spatial analysis by capturing the complexity and dynamism of interactions between variables in a spatial context that is often ignored by classical spatial models. Furthermore, this article aims to estimate the parameters of MSDM model applied to large and complex data sets through Monte Carlo simulations. This model was then estimated using Maximum Likelihood Estimation (MLE), and to test the accuracy of the model using the Maximum Likelihood Ratio Test (MLRT) with a computational approach. The research results show that the MSDM model para-meter estimates are accurate as indicated by an accuracy value that is smaller than the 5% significance level. The model becomes more efficient as the sample size increases.
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Falah, Annisa Nur, Yudhie Andriyana, Budi Nurani Ruchjana, et al. "An Expanded Spatial Durbin Model with Ordinary Kriging of Unobserved Big Climate Data." Mathematics 12, no. 16 (2024): 2447. http://dx.doi.org/10.3390/math12162447.
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Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed.
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Liu, Zhongyang, Yunquan Song, and Yi Cheng. "Robust Variable Selection with Exponential Squared Loss for the Spatial Durbin Model." Entropy 25, no. 2 (2023): 249. http://dx.doi.org/10.3390/e25020249.
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With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is proposed for the spatial Durbin model. Under mild conditions, we establish the asymptotic and “Oracle” properties of the proposed estimator. However, in model solving, nonconvex and nondifferentiable programming problems bring challenges to solving algorithms. To solve this problem effectively, we design a BCD algorithm and give a DC decomposition of the exponential squared loss. Numerical simulation results show that the method is more robust and accurate than existing variable selection methods when noise is present. In addition, we also apply the model to the 1978 housing price dataset in the Baltimore area.