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INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2024年第3期9卷 537-556页

作者： Anekawati, Anik Purhadi Rofik, Mohammad Hidayat, Syaifurrahman Univ Wiraraja Fac Teacher Training & Educ Sumenep Indonesia Inst Teknol Sepuluh Nopember Dept Stat Surabaya Indonesia Univ Wiraraja Fac Econ & Business Sumenep Indonesia Univ Wiraraja Fac Hlth Sci Sumenep Indonesia

spatial regression, particularly the spatial error model (SERM), was utilized in prior studies to analyze Human Development Index (HDI) modeling. However, the studies were unable to determine which dimension among the three defined by the UN and BPS had the significant impact on HDI, as they constructed models based on the indicators used for the interpretation of the dimensions. Therefore, a comprehensive analysis combining spatial regression and Structural Equation modeling (SEM), known as spatial SEM, was deemed necessary. This is the reason the current study aimed to develop SERM-SEM modeling holistically. The model parameters were estimated using the Generalized Method of Moments (GMM). To assess spatial dependency, the Lagrange Multiplier (LM) method was employed, with a distinct model error distribution compared to the error distribution of the traditional spatial model. The result of the LM test development showed that, under the null hypothesis, the LM test statistics followed a distribution. The results of the SERM-SEM model development were applied to HDI modeling using data in 2022 with three latent variables, namely a Long and Healthy Life (LHL), Knowledge (Know_L), and a Decent Standard of Living (DLS) (based on UN standards). The assessment of the outer model in SEM was based on the loading factor values that exceed 0.5 and their significance. This evaluation aimed to identify indicators that effectively explained or measured latent variables, so it got the revised model in SEM. These indicators are LHL2 and LHL 4 to form LHL. DLS1 and DLS3 are indicators to make up DLS, and for Know_L, they are K2 and K3. The revised SEM model was analyzed using spatial. The results of the spatial dependency test showed that the HDI model significantly led to the SERM-SEM model. Knowledge and a decent standard of living variables significantly influence HDI.

关键词： Structural equation modeling spatial spatial error model Human development index spatial SEM

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Spatio-temporal heterogeneity of logistics CO2 emissions and their influencing factors in China: An analysis based on spatial error model and geographically and temporally weighted regression model

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ENVIRONMENTAL TECHNOLOGY & INNOVATION 2022年 28卷

作者： Bai, Dongling Dong, Qianli Khan, Syed Abdul Rehman Li, Jinfeng Wang, Dongfang Chen, Yan Wu, Jiani Changan Univ Sch Econ & Management Xian 710064 Peoples R China Zhengzhou Univ Aeronaut Sch Management Engn Zhengzhou 450046 Peoples R China Xuzhou Univ Technol Sch Engn & Management Xuzhou 221018 Peoples R China ILMA Univ Dept Business Adm Karachi Pakistan Henan Univ Anim Husb Econ Sch Logist & Elect Commerce Zhengzhou 450044 Peoples R China Wuyi Univ Sch Business Wuyishan 354300 Peoples R China

As one of the ultra-large carbon emitters in China, the logistics industry plays an increasingly essential role in mitigating carbon emissions. Previous studies on logistics CO2 emissions (LCEs) seldom considered the issue of carbon leakage from electricity and the spatio-temporal nonstationarity of factors influencing LCEs. Therefore, using regional grid carbon emission factors, this study calculated the LCEs of 30 Chinese provinces from 2005 to 2019 and, thereafter, employed global and local spatial econometric models - spatial error model (SEM) and Geographically and Temporally Weighted Regression (GTWR) model, respectively - to uncover the spatio-temporal heterogeneous impact of influencing factors on LCEs. The results suggest that: (1) The overall LCEs continue to rise, presenting significant spatial difference with an increasing gradient pattern of the "Western-Central-Eastern."(2) The LCEs show a spatial agglomeration, with high- high (H-H) and low-low (L-L) clusters as the main types. (3) Economic development level, energy intensity, and electricity consumption significantly promote the LCEs, and the intensity of the promotion presenting a downward, steady, and upward trend, respectively. (4) Population urbanization inhibits LCEs in north China, but boosts them in south China;the inhibition effect of industrial structure on LCEs, which is mainly pronounced in the provinces along the Yangtze River Economic Belt, tends to transform into a promoting effect.

关键词： LogisticsCO(2) emissions Carbon leakage Spatio-temporal heterogeneity spatial error model Geographically and temporally weighted regression

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Digital Financial Inclusion and Urban-rural Income Gap -- Empirical Analysis based on spatial error model

Frontiers in Economics and Management

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Frontiers in Economics and Management 2022年第8期3卷 648-656页

作者： Lingyu Zhong

Based on the panel data of 30 provinces in China from 2013 to 2019, a spatial econometric model was constructed to empirically test the impact of digital financial inclusion on the income gap between urban and rural residents. The results show that (1)both the development of digital financial inclusion and the urban-rural income gap have positive spatial correlation. The development of digital financial inclusion can not only narrow the urban-rural income gap in the province, but also drive the development of neighboring provinces and narrow the urban-rural income gap in the neighboring provinces.(2)The spatial spillover effect of digital financial inclusion mainly depends on the coverage of financial services.

关键词： Digital Financial Inclusion Urban-rural Income Gap spatial error model

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Robust estimation approach for spatial error model

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2020年第9期90卷 1618-1638页

作者： Yildirim, Vural Kantar, Yeliz Eskisehir Tech Univ Inst Earth & Space Sci Eskisehir Turkey Eskisehir Tech Univ Dept Stat Fac Sci TR-26470 Eskisehir Turkey

spatial regression models, used to model spatial relationships, have received considerable attention in recent years since numerous data sets have been collected with geographical references in space. The spatial error model (SEM), among spatial regression models, has been widely applied for spatial data in the literature due to its simple structure. However, it is known that the classical estimation methods such as the maximum likelihood and generalized moment can be influenced by the presence of outliers in the data. In this article, a robust estimation approach based on the robustified likelihood equations for SEM is proposed. The results of the simulation study show that the proposed estimator for SEM has smaller bias and mean squared errors and exhibits more robust empirical influence function than the classical methods, when there are outliers in the dataset. The results of all analysis show that the proposed estimator is robust to outliers.

关键词： spatial error model outlier robust estimation influence function robustness

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Kernel-based spatial error model for analyzing spatial panel data

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model Assisted Statistics and Applications 2020年第3期15卷 239-248页

作者： Shim, Jooyong Lee, Sang Bum Kim, Daiwon Yu, Jung-Suk Hwang, Chanha Department of Statistics Institute of Statistical Information Inje University Gyeongsangnamdo South Korea Department of Software Science Dankook University Gyeonggido South Korea Incheon Metropolitan City Development Corporation 42 Incheon South Korea School of Urban Planning and Real Estate Studies Dankook University Gyeonggido South Korea Department of Applied Statistics Dankook University Gyeonggido South Korea

spatial panel data model captures spatial interactions across spatial units and over time. Lots of effort have been devoted to develop effective estimation methods for parametric and nonparametric spatial panel data models. Varying coefficient model has received a great deal of attention as an important tool for modeling panel data. In this paper we propose a kernel-based spatial error model for the purpose of analyzing spatial panel data. This model is based on the idea of fixed effect time-varying coefficient model and the kernel technique of support vector machine along with the technique of regularization. A generalized cross validation method is also considered for choosing the hyperparameters which affect the performance of the proposed model. The proposed model is evaluated through numerical studies. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Fixed effect generalized cross validation kernel technique model selection spatial error model spatial panel data time-varying coefficient model

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STATISTICA NEERLANDICA 2017年第1期71卷 4-30页

作者： Al-Momani, Marwan Hussein, Abdulkadir A. Ahmed, S. E. King Fahd Univ Petr & Minerals Dept Math & Stat POB 1017 Dhahran 31261 Saudi Arabia Univ Windsor Dept Math & Stat 401 Sunset Ave Windsor ON N9B 3P4 Canada Brock Univ Dept Math 500 Glenridge Ave St Catharines ON L2S 3A1 Canada

spatial autoregressive models are powerful tools in the analysis of data sets from diverse scientific areas of research such as econometrics, plant species richness, cancer mortality rates, image processing, analysis of the functional Magnetic Resonance Imaging (fMRI) data, and many more. An important class in the host of spatial autoregressive models is the class of spatial error models in which spatially lagged error terms are assumed. In this paper, we propose efficient shrinkage and penalty estimators for the regression coefficients of the spatial error model. We carry out asymptotic as well as simulation analyses to illustrate the gain in efficiency achieved by these new estimators. Furthermore, we apply the new methodology to housing prices data and provide a bootstrap approach to compute prediction errors of the new estimators.

关键词： spatial autoregressive models spatial error model preliminary test shrinkage penalty estimation

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Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 1页

作者： Cai, Jiaxin Hu, Weiwei Yang, Yuhui Yan, Hong Chen, Fangyao Xian Jiaotong Univ Hlth Sci Ctr Sch Publ Hlth Dept Epidemiol & Biostat 76 Yanta West Rd Xian 710061 Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Dis Prevent & Control & Hlth Promot Shaanx Xian 710061 Shaanxi Peoples R China Xi An Jiao Tong Univ Dept Radiol Affiliate Hosp 1 Xian 710061 Shaanxi Peoples R China

Background Outliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) *** We propose the spatial-Theta-Iterative Procedure for Outlier Detection (spatial-Theta-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple *** Simulation results showed that the masking and JD (Joint Detection) indicators of our spatial-Theta-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Theta-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling *** Our proposed spatial-Theta-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative super

关键词： Outliers Iterative procedure for outlier detection Mean-shift outlier model spatial error model Robust estimation

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An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study

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HABITAT INTERNATIONAL 2015年 46卷 196-205页

作者： Zhang, Ran Du, Qingyun Geng, Jijin Liu, Biao Huang, Yikun Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Ctr Assessment & Dev Real Estate Shenzhen 518000 Peoples R China Wuhan Univ Key Lab Geog Informat Syst Minist Educ Wuhan 430079 Peoples R China Chinese Univ Hong Kong Geoinformat Sci Hong Kong Hong Kong Peoples R China Univ Hong Kong Real Estate & Construct Hong Kong Hong Kong Peoples R China

Due to the rapid urbanization in China, the government must explore a new sustainable source of fiscal revenue. Therefore, property tax reform has been the topic of active discussion. As an important technical support for property tax reform, mass appraisal is an effective technique for implementing the tax base assessment. To achieve high precision and reduce the cost of valuation, an innovative framework for commercial real estate appraisal is proposed through the introduction of a spatial error model (SEM), fuzzy mathematics and econometrics. Several modifications are made to the conventional SEM so that it can be extended to the solution of commercial property mass appraisals. The fuzzy mathematics approach is adopted to specify the spatial weight matrix (SWM). Additionally, based on econometrics, the impacts of the entity and locatio on factors on commercial real estate prices are discussed. The proposed method is then implemented for commercial real estate, based on the empirical analysis of Shenzhen. Experiments demonstrate the accuracy of the assessment results and that the consistency among different appraisal objects is improved. Further, through the introduction of fuzzy mathematics, we extend the SWM construction from a single variable to multi-variables. This advantage allows us to increase the usage of our technique and to provide technical support for the undergoing property tax reform in the Chinese real estate market. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Mass appraisal spatial analysis spatial error model Fuzzy mathematics

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spatial green growth in China: exploring the positive role of investment in the treatment of industrial pollution

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第4期30卷 10272-10285页

作者： Imran, Muhammad Hayat, Naveed Saeed, Muhammad Ali Sattar, Abdul Wahab, Salman Bahria Univ Sch Business Studies Islamabad Pakistan Univ Educ Dept Econ Lahore Pakistan Millennium Universal Coll Islamabad Pakistan Bahria Univ Bahria Business Sch Islamabad Pakistan Qingdao Univ Sch Econ Qingdao Peoples R China

The industrial sector of China is critical to the country's economic growth. On the other side, industrialisation has resulted in a high rate of emissions, pushing China to spend extensively on industrial pollution remediation. As a result, this study looks at the relationship between investment completed in the treatment of industrial pollution and economic development. Initially, the study used the global Moran's I test (Queen's contiguity matrix) to find spatial autocorrelation for the 'investment completed in the treatment of industrial pollution' factor, where the study found a positive association across Chinese provinces, and suggest the existence of spatial autocorrelation. Thereafter, a time-fixed effect spatial error model was used due to the lowest Akaike information criterion and Bayesian information criterion to analyse regional data of China from 1999 to 2018. The data reveal a positive association between investment completed in the treatment of industrial pollution and regional economic growth, both in the short and long term. Furthermore, the negative consequences of urban wages and foreign investment on investment completed in the treatment of industrial pollution are having the reverse effect on regional green development, necessitating ecologically friendly actions to mitigate the negative environmental effects of both. The results highlight the need for policymakers in other countries to review their plans for economic expansion and create environmentally friendly legislation. By implementing the Chinese green economic growth model, policymakers in industrially polluting nations can reduce industrial pollution and foster green growth in their nation.

关键词： Financial investment Green growth Industrial pollution Urban wage spatial error model

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spatial analysis of multidimensional poverty in Pakistan: Do income and poverty score of neighboring regions matter?

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GEOJOURNAL 2023年第3期88卷 2823-2849页

作者： Khan, Sami Ullah Sloboda, Brian William Gomal Univ Dept Econ Dera Ismail Khan Pakistan Univ Maryland Dept Econ & Finance Global Campus Largo MD USA

Eradication of multidimensional poverty remains a significant issue for a number of low-income countries. It has significantly influenced economic development worldwide, and its abolition would improve human well-being and prosperity. In this study, a multidimensional poverty (MDP) index has been constructed by adopting the Alkire-Foster methodology and Geographic Information System application to (i) measure the multidimensional poverty in all districts of Pakistan, (ii) recognize the spatial patterns concerning MDP in Pakistan, (iii) examine the impact of per-capita income on MDP in Pakistan and, (iv) assess the spatial impact of neighbors' poverty scores on MDP. The findings of the study indicated that the magnitude of both headcount and MDP varies across provinces and was found to be higher and lower in Balochistan and Punjab, respectively. The spatial results revealed that in terms of both MDP and headcount poverty, the majority of Punjab districts were significantly clustered in low and very low quantiles (low-low quadrants) whereas districts from Balochistan were concentrated in high and very high quantiles (high-high quadrants). Furthermore, the spatial regression showed that per capita income had substantially reduced the incidence of poverty both directly and indirectly. Most importantly, the poverty score of neighboring districts has significantly influenced the incidence of district own MDP. Based on the instant spatial results, Balochistan is considered the highly poverty-stricken province of Pakistan that needs government intervention and policy makers' prioritization towards the study indicators to eradicate the incidence of multidimensional poverty.

关键词： Living standard Multidimensional poverty Sociodemographic indicators spatial autocorrelation spatial error model spatial lag model

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