Bayesian SAR Model with Stochastic Volatility and Multiple Time-Varying Weights

This study investigates the impact of regional spillover effects on economic growth in Central Java’s regencies and cities, focusing on Knowledge Spillover, Industry Spillover, Private Investment Spillover, and Government Expenditure Spillover using a spatial econometrics panel model. The results indicate significant effects on local economic growth; however, direct spillover effects, which are represented by the variable Wx in the Spatial Durbin Model (SDM), were not significant. This may be due to limited resource mobility, varying regional absorption capacities, and temporal dynamics. Direct spillover effects typically involve immediate economic interactions between neighboring regions, but in this study, these effects did not materialize as expected. Conversely, indirect spillover effects, reflected by spatial parameters ρ in the Spatial Autoregressive Model (SAR) and λ in the Spatial Error Model (SEM), highlight the importance of broader spatial dynamics across regions. These effects underscore the significance of interregional linkages rather than direct regional interactions, showing that regional synergies play a crucial role in fostering economic growth. From a policy perspective, the study recommends strengthening interregional cooperation by enhancing mechanisms such as information flow, technology transfer, and investment. Investment in digital infrastructure, the creation of innovation hubs, and cross-regional investment funds can help bridge the development gap and foster sustainable growth. Policies that support labor mobility and skill-building opportunities, especially for marginalized groups and women, are essential to promote equitable and inclusive economic development across regions

Is It a Curse or a Blessing to Live Near Rich Neighbors? Spatial Analysis and Spillover Effects of House Prices in Beijing

Study on the spatial spillover effects of cement production on air pollution in China

Testing for random effects and serial correlation in spatial autoregressive models

Capturing household-level spatial influence in agricultural management using random effects regression

Previous studies neglected the implicit information in the spatial pattern of the aerosol optical depth (AOD), such as spatial configuration characteristics, spatial heterogeneity and spatial dependence. The understanding of AOD is helpful in accurately estimating the environmental and climatic effects from aerosols. In this study, a method of spatial autocorrelation statistics from geostatistics was applied to investigate the spatial pattern of AOD in Hubei province in central China during the period 2003–2008. Spatial autoregressive models were used to quantize the correlations between AOD and affecting factors, such as elevation, forest coverage and population density. In addition, the difference between the standard linear regression model and the spatial models was discussed. The results were as follows: the spatial pattern of AOD in Hubei province shows significant spatial autocorrelation, indicating that AODs are clustered such that higher AODs tend to be surrounded by higher AOD neighbors, while lower AODs are surrounded by lower AOD neighbors, and the spatial autocorrelation scale of the AOD over Hubei is approximately 400 km. The high–high zone is mainly distributed in the Wuhan city circle and the Jianghan plain areas, while the low–low zones are mainly located in the middle and high mountain areas of northwest Hubei province. The overall degree and pattern of spatial autocorrelation do not change largely from 2003 to 2008, which indicates a stable spatial configuration of AOD. A significant negative spatial autocorrelation exists between AOD and elevation, which may suggest that AOD and elevation have an inverse spatial distribution, and the same applies for forest coverage. Population density and the AOD show a significant positive spatial autocorrelation, which may imply that they have similar spatial distribution, while industrial production and AOD do not show an obvious positive spatial autocorrelation. Spatial autoregressive models show better predictive ability and stability than the standard linear regression model because the spatial autocorrelation of the AOD is taken into consideration.

PurposeThe study aims to discover the spatial relationship between financial development, energy consumption and economic growth in 11 ASIA countries, using panel data from 1980 to 2016.Design/methodology/approachThe study applies three popular spatial models, namely, (1) spatial error model (SEM), (2) spatial autoregressive model (SAR) and (3) spatial Durbin model (SDM), to explore the direct and spillover effect of financial development and energy consumption on economic growth. Furthermore, a novel test proposed by Juodis et al. (2020) is employed to check the Granger non-causality between each pair of variables.FindingsThe empirical outcomes found direct and spillover effects of financial development and energy consumption on economic growth in 11 ASIA countries. Accordingly, an expansion of the financial development in country i is beneficial for the growth of the host country and neighboring countries, and vice versa. However, an increase in energy consumption in country i leads to a decrease in the economic growth of neighboring countries. The test of Granger non-causality indicated a bidirectional causality between financial development and economic growth, and unidirectional causality running from economic growth to energy consumption.Research limitations/implicationsSpillover effects of financial development and energy consumption on growth have largely been ignored in previous studies, especially in emerging countries. Thus, the study enriches the literature and provides some policy implications for ASIA countries.Practical implicationsSpillover effects of financial development and energy consumption on growth have largely been ignored in previous studies, especially in emerging countries. Thus, the study enriches the literature and provides some policy implications for ASIA countries.Originality/valueSpillover effects of financial development and energy consumption on growth have largely been ignored in previous studies, especially in emerging countries. Thus, the study enriches the literature and provides some policy implications for ASIA countries.

PurposeThe purpose of this study is to examine the spatial fluctuation spillover effect of green total factor productivity (GTFP) under the influence of Internet development.Design/methodology/approachUsing panel data from 283 cities in China for the period 2003–2016, this paper explores the spatial fluctuation spillover effect of internet development on GTFP by applying the spatial autoregressive with autoregressive conditional heteroscedasticity model (SARspARCH).FindingsThe results of Moran's I test of the residual term and the Bayesian information criterion (BIC) value indicate that the GTFP has a spatial fluctuation spillover effect, and the estimated results of the SARspARCH model are more accurate than the spatial autoregressive (SAR) model and the spatial autoregressive conditional heteroscedasticity (spARCH) model. Specifically, the internet development had a positive spatial fluctuation spillover effect on GTFP in 2003, 2011, 2012 and 2014, and the volatility spillover effect weakens the positive spillover effect of internet development on GTFP. Moreover, Internet development has a significant positive spatial fluctuation spillover effect on GTFP averagely in eastern China and internet-based cities.Research limitations/implicationsThe results of this study provide digital solutions for policymakers in improving the level of GTFP in China, with more emphasis on regional synergistic governance to ensure growth.Originality/valueThis paper expands the research ideas for spatial econometric models and provides a more valuable reference for China to achieve green development.

The spatial J ‐test ( SJ ‐test) and the spatial JA ‐test ( SJA ‐test) are outlined to determine the appropriate spatial weight structure for the spatial autoregressive model against a single, or a set of, non‐nested alternatives. These two non‐nested tests are based on the maximum likelihood framework and can be applied to both the simple spatial autoregressive model and the general spatial autoregressive model. The M onte C arlo simulation results showed that the SJ ‐test and the SJA ‐test had clearly distinct performance in determining the appropriate spatial weight structure for a spatial autoregressive model against a single alternative and against two alternatives. The SJ ‐test performed better than the SJA ‐test in failing to reject a true null hypothesis when compared against a single alternative, whereas the SJA ‐test performed better than the SJ ‐test in failing to reject a true hypothesis when compared against two alternatives. Therefore, the SJ ‐test should be applied to determine the spatial weight structure against a single alternative, and the SJA ‐test should be applied to determine the spatial weight structure against a set of alternatives. Performance improved with a larger sample size and with a larger magnitude of spatial autocorrelation. The power of the tests depended on the degree of similarity between the alternatives and the underling true structure. The power of the tests was likely to be lower with two alternatives rather than a single alternative. This is due to the introduction of a false spatial weight matrix into the alternative with the true spatial weight matrix. El artículo describe la prueba J espacial ( J‐test o SJ‐test ) y la prueba JA espacial prueba espacial ( SJA‐test ), las cuales son utilizadas para determinar la estructura de ponderación espacial apropiada para el modelo autorregresivo espacial frente a una alternativa nonestad única o frente a un conjunto de alternativas nonnested . Estas dos pruebas nonnested se basan en el marco de máxima verosimilitud ( máximum likelihood‐ ML) y se pueden aplicar tanto al modelo autorregresivo espacial simple como al modelo autorregresivo espacial general. Los resultados de la simulación Monte Carlo presentados aquí muestran que SJ‐test y SJA‐test tienen una performance claramente distintiva en cuanto a la determinación de la estructura espacial de ponderación apropiada para un modelo autorregresivo espacial versus una alternativa única y versus dos alternativas. El SJ‐test funcionó mejor que el SJA‐test al no rechazar una hipótesis nula verdadera cuando se compara con una alternativa única, mientras que el SJA‐test funcionó mejor que el SJ‐test al no rechazar una hipótesis verdadera cuando se le compara con dos alternativas. Por lo tanto, los autores concluyen que el SJ‐test se debe aplicar para determinar la estructura espacial de peso versus una alternativa única, mientras que la SJA‐test se debe aplicar para determinar la estructura espacial de ponderación versus un conjunto de alternativas. Los resultados de performance mejoraron con un tamaño de muestra más grande y con una magnitud mayor de autocorrelación espacial. El poder estadístico de las pruebas depende del grado de similitud entre las alternativas y la estructura espacial verdadera subyacente (la matriz). El poder estadístico de las pruebas analizadas tiende a ser inferior cuando las dos alternativas nonestad son utilizadas en lugar de la alternativa única. Esto se debe a la introducción de una matriz falsa de ponderación espacial en la alternativa que contiene la matriz verdadera de ponderación espacial. 本文阐述了空间J检验(SJ‐test)和JA检验(SJA‐test)，以确定在单个或者一系列非嵌套备择空间自回归模型中恰当的空间权重结构。两种非嵌套检验方法均基于最大似然框架，并可用于简单的和广义的空间自回归模型。蒙特卡洛模拟结果显示，在单个和两种备择方案下，SJ检验和SJA检验对空间自回归模型的空间权重结构的确定效果明显不同。与在单个备择方案比较时，SJ在未能拒绝真实零假设方面检验效果优于SJA检验；反之，在与两个备择方案比较时，SJA检验在未能拒绝真假设方面的效果则优于SJ检验。因此，SJ检验应该应用于单备择的空间权重结构确定，而SJA检验则应用于多备择的空间权重结构确定。当进行更大样本量和更大量级的空间自相关分析时，模型的性能和效果会得到提升。检验方法的能力取决于备择方案与下属真实结构的相似度，在具有两种（或多种）备择方案情况下，模型的检验能力会降低，并可能导致将错误的空间权重矩阵引入真实的空间权重结构矩阵构建中。

Africa's sustainable socioeconomic development is increasingly challenged by institutional disparities, where weak governance undermines progress in human capital indicators such as life expectancy and educational attainment. Despite recognition of institutional quality's role in fostering transparency, accountability, and stability, existing research often overlooks its spatial spillover effects across interconnected African nations. This study aims to examine the direct and spillover impacts of institutional quality proxied by transparency (corruption perception), rule of law, and political stability on life expectancy and mean years of schooling in 42 African countries over the period 2012–2022. Employing spatial panel econometric models, including the Spatial Durbin Model (SDM), Spatial Autoregressive Model (SAR), and Spatial Error Model (SEM), the analysis controls for GDP per capita and population growth. The results reveal significant positive spatial autocorrelation in both outcomes (Moran's I: 0.536 for life expectancy; 0.469 for schooling). For life expectancy, institutional quality (particularly the rule of law) exhibits strong positive direct and spillover effects, amplified by regional GDP and population dynamics, indicating that governance improvements in one country enhance health outcomes in neighbors through policy diffusion and cross‐border cooperation. In contrast, educational attainment shows no significant spillovers, driven primarily by domestic GDP, population, and rule of law, highlighting fragmented regional education systems. These findings underscore institutional quality as a regional public good, offering nuanced insights for harnessing spatial interdependence in Africa's development agenda.

The Open Unemployment Level (OUL) is the percentage of the unemployed to the total labor force. One of the provinces with the highest OUL score in Indonesia is West Java Province. If an object of observation is affected by spatial effects, namely spatial dependence and spatial diversity, then the regression model used is the Spatial Autoregressive (SAR) model. Quantile regression minimizes absolute weighted residuals that are not symmetrical. It is perfect for use on data distribution that is not normally distributed, dense at the ends of the data distribution, or there are outliers. The Spatial Autoregressive Quantile Regression (SARQR) is a model that combines spatial autoregressive models with quantile regression. This research used the data regarding OUR in West Java in 2020 from the Central Bureau of Statistics. This study develops to modeling the Open Unemployment Level in all province in Indonesia using modified spatial autoregressive model with the quantile regression approach. This study compares the estimation results based on SAR and SARQR models to obtain an acceptable model. In this study, it was found that the SARQR model is better than SAR at dealing with the problems of dependency and diversity in spatial data modeling and is not easily affected by the presence of outlier data.

Spatial autoregressive (SAR) models with varying coefficients are useful for capturing heterogeneous effects of the impacts of covariates as well as spatial interaction in empirical studies, and a wide range of popular models can be seen as its special cases, such as linear SAR models. In this study, we will propose a unified model selection method for the SAR model with varying coefficients to achieve two targets simultaneously: (1) variable selection (eliminate irrelevant covariates), and (2) identification of the covariates with constant effect among the relevant covariates. To do so, we follow the idea of group LASSO to incorporate two penalty functions to simultaneously do model selection and estimation. Monte Carlo experiments show that the proposed method performs well in finite samples. Finally, we illustrate the method with an application to the housing data of Chinese cities.

This paper studies the fact that 37 percent of the internal migrants in China do not sign a labor contract with their employers, as revealed in a nationwide survey. These contract-free jobs pay lower hourly wages, require longer weekly work hours, and provide less insurance or on-the-job training than regular jobs with contracts. We find that the co-villager networks play an important role in a migrant's decision on whether to accept such insecure and irregular jobs. By employing a comprehensive nationwide survey in 2011 in the spatial autoregressive logit model, we show that the common behavior of not signing contracts in the co-villager network increases the probability that a migrant accepts a contract-free job. We provide three possible explanations on how networks influence migrants' contract decisions: job referral mechanism, limited information on contract benefits, and the mini labor union formed among co-villagers, which substitutes for a formal contract. In the sub-sample analysis, we also find that the effects are larger for migrants whose jobs were introduced by their co-villagers, male migrants, migrants with rural Hukou, short-term migrants, and less educated migrants. The heterogeneous effects for migrants of different employer types, industries, and home provinces provide policy implications.

This paper analyzes the current situation of university English teaching and constructs a trinity of EGP+ESP+EIC multiple teaching innovation systems for university English based on the principles of English teaching innovation. In order to analyze this innovative teaching system of English, a stratified statistical model and a spatial autoregressive model are combined, and four ordinal stratified spatial autoregressive Probit models are innovatively constructed to model data where the dependent variable is ordinal, and there is a spatial dependency situation with a stratified structure. The analysis shows that in the process of continuous innovation of English teaching mode, the number of people who are dissatisfied with English teaching decreases by 30%, the number of people who are very satisfied increases by 35%, and the number of people who are very satisfied accounts for 42.5% of the total number of people and 90% of the total number of people who are satisfied in the final stage of innovation of English teaching in university. The English teaching innovation strategy for university students based on this study is feasible.

As the driving force for the upgrading of the global industrial structure, digital technology has been at the helm of transforming the current economic and technological paradigm. This study empirically analyzes the role of the digital economy in the upgrading industrial structure using panel data from 237 prefecture-level cities in China from 2011–2019. Empirical results show that the development of the digital economy has a significant positive and direct effect on the upgrading of industrial structure, as measured by the two indicators of advancement and servitization. In addition, the digital economy significantly promotes upgrading of the technological progress and human capital of Chinese cities, thus promoting upgrading of the industrial structure. Moreover, the spatial autoregressive model (SAR) and the Spatial Durbin model (SDM) both show that the digital economy has a positive spatial spillover effect on upgrading of the industrial structure.