The spatial and spatiotemporal autoregressive conditional heteroscedasticity(STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive(STAR) model with STARCH errors, whi...The spatial and spatiotemporal autoregressive conditional heteroscedasticity(STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive(STAR) model with STARCH errors, which can capture the spatiotemporal dependence in mean and variance simultaneously. The Bayesian estimation and model selection are considered for our model. By Monte Carlo simulations, it is shown that the Bayesian estimator performs better than the corresponding maximum-likelihood estimator, and the Bayesian model selection can select out the true model in most times. Finally, two empirical examples are given to illustrate the superiority of our models in fitting those data.展开更多
利用我国2002年第二季度至2010年第三季度数据,运用Debarsy and Ertur(2010)检验方法及广义空间面板数据模型,对我国区域房价的空间相关性进行实证分析,结果表明:我国区域房价的空间相关性既表现出了空间自相关,也表现出了空间误差相关...利用我国2002年第二季度至2010年第三季度数据,运用Debarsy and Ertur(2010)检验方法及广义空间面板数据模型,对我国区域房价的空间相关性进行实证分析,结果表明:我国区域房价的空间相关性既表现出了空间自相关,也表现出了空间误差相关,且区域间房价的空间相关强度与地区间的距离(邻近地区房价对当地房价的影响程度比其它相关地区大)及地区间的经济发展特征相关(相似地区房价对当地房价的影响程度与不相似地区小),而引起区域房价空间相关的主要成因是劳动力流动,资本流动,信息传递,政府政策、企业定价能力及其它外生冲击。展开更多
In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission inten...In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.展开更多
基金supported by National Natural Science Foundation of China (No.12271206)Natural Science Foundation of Jilin Province (No.20210101143JC)Science and Technology Research Planning Project of Jilin Provincial Department of Education (No.JJKH20231122KJ)。
文摘The spatial and spatiotemporal autoregressive conditional heteroscedasticity(STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive(STAR) model with STARCH errors, which can capture the spatiotemporal dependence in mean and variance simultaneously. The Bayesian estimation and model selection are considered for our model. By Monte Carlo simulations, it is shown that the Bayesian estimator performs better than the corresponding maximum-likelihood estimator, and the Bayesian model selection can select out the true model in most times. Finally, two empirical examples are given to illustrate the superiority of our models in fitting those data.
文摘利用我国2002年第二季度至2010年第三季度数据,运用Debarsy and Ertur(2010)检验方法及广义空间面板数据模型,对我国区域房价的空间相关性进行实证分析,结果表明:我国区域房价的空间相关性既表现出了空间自相关,也表现出了空间误差相关,且区域间房价的空间相关强度与地区间的距离(邻近地区房价对当地房价的影响程度比其它相关地区大)及地区间的经济发展特征相关(相似地区房价对当地房价的影响程度与不相似地区小),而引起区域房价空间相关的主要成因是劳动力流动,资本流动,信息传递,政府政策、企业定价能力及其它外生冲击。
基金National Natural Science Foundation of China,No.41601151Natural Science Foundation of Guangdong Province,No.2016A030310149Pearl River S&T Nova Program of Guangzhou(201806010187)
文摘In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.
