我国交通与经济增长关系研究
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摘要
改革开放以来,作为经济与社会发展的重要组成部分,我国交通运输业也得到了迅速发展。国家统计局数据显示,至2010年我国高铁里程数在世界排名第一,高速公路里程数排名第二,铁路里程数排名第三。从改革开放到2010年我国综合交通网络总里程增长了3.4倍,其中公路里程增加4.5倍,铁路里程增加1.8倍,机场数量增加2.2倍。交通系统所能达到的旅客运输量增加了8.7倍,货物运输量增加了5.1倍。我国交通与经济增长的关系究竟如何是本文研究的主要内容。弄清楚我国交通与经济增长之间的关系,有助于未来交通发展战略计划的制定,为经济的进一步增长奠定基础。
本文从不同角度来研究我国交通与经济增长的关系,分别采用了不同的方法和模型,并在实证研究中灵活的将时间维度和空间维度相结合,全面而立体的展示我国交通与经济增长关系的时空格局。
本文第二章整理归纳了本文实证分析所需的经济学和计量经济学理论基础。根据前人对交通与经济的关系和相互作用的研究成果,归纳总结并形成了交通与经济发展的良性循环图,为解释交通与经济增长的复杂关系奠定了理论基础。本文第三章首先从整体上分析了我国交通与经济增长之间的关系,包括从时间维度上分析各交通变量与我国经济增长的趋势是否一致,从空间范围上来看各地区交通的运量与经济的发达程度是否一致,有何特点。结果显示：我国交通客运量和货运量的增长与经济增长基本同步；公路运输是我国最主要的运输方式,分别占总客运量和总货运量的90%和80%以上,其次为铁路运输,但各个省份的交通结构差异很大；公路运输和铁路运输与我国经济发达水平的空间分布较为一致。我国水运主要从事货物运输,主要分布在沿海口岸、长江和珠江流域；而我国民航主要从事旅客运输,主要分布在我国各大中城市。
第三章是从时间和空间上对我国交通与经济增长关系的一个直观展示,近年来我国交通的区域性发展是否平衡,和经济的空间关系又如何呢?这即是第四章的主要研究内容。第四章采用空间重心分析方法,结合2001年和2010年我国地级市的交通和经济数据,研究了我国交通与经济的空间关系,以揭示我国各交通的区域发展的平衡性和与经济的空间相关程度。结果显示：我国各种交通变量均向经济重心靠拢,说明交通与经济的联系更加密切,其中总货运量、铁路客运与经济重心距离较小,较其他变量与经济增长关系更紧密。整体上来说我国经济、公路、水路和民航的区域发展较为均衡,而铁路的重心迁移较大,无论是客运量还是货运量都向西南地方迁移,说明这期间西南地区的铁路运量大为增强,可能是由于西部大开发中西南地区铁路大规模的建设导致铁路供给的增加。
前面所做的研究未揭示交通与经济增长的数量关系,本文第五章和第六章内容则采用回归分析方法来揭示其数量关系。第五章研究主要有两个目的,第一是研究交通的经济弹性(即经济增长一个百分点,交通运量增长多少)以及近十年来经济弹性的趋势,第二是研究交通的外部性(即一个地区的交通对周围地区有何影响)。本章所采用的是双对数模型(弹性模型)和我国2001年到2010年省份和地级市的交通和经济数据,并以客货运量为主要研究对象。采用了不同尺度下交通的经济弹性的差异来分析交通的外部性。由于本章所采用的数据存在较多异常值,还采用了MM稳健回归方法来剔除异常值对回归结果的破坏作用。本章研究结果显示：不同尺度下各交通变量的弹性差异较大,时间趋势也不相同。其中总客运量、铁路客运和铁路货运货运有正的外部性,而总货运量、公路货运量和公路客运量有负的外部性。
第五章所研究的弹性关系是以经济发展速度为基准的,即研究经济增长快的地方交通增长的快慢,而研究交通与经济增长之间的关系还可以以经济发达水平为基准,即研究经济发达地区的交通增长的快慢问题,该问题即为交通的收敛性。本文第六章建立了交通的收敛性模型,以我国客货运量为研究对象,采用了2001年和2010年地级市的交通和经济数据。由于本章所采用的数据和模型所存在严重的空间相关性,采用普通线性回归拟合会造成较为严重的偏差,故本章用地理加权回归来进行分析。结果显示,地理加权回归比普通线性回归有更好的拟合能力,并能有效解决空间相关性问题。整体上来说,我国交通无论是客运量还是货运量都是收敛的,即欠发达地区交通运量增长的更快。但交通的收敛性有有着明显的地域特征,我国东部地区交通呈现收敛之势,而西部地区则是发散的。
本文第七章分析了我国“十五”和“十一五”期间交通发展的成果与不足,并结合本文前面所做的实证分析结果,提出了我国交通运输业发展的建议与设想,包括：我国运输结构仍需要大力调整,提高除公路外的其他运输方式的比重,充分发挥铁路客运和水路货运的优势,分担交通运输压力,并积极发展民航业；实行各种专项交通政策,打造各经济圈和产业链的专项交通,提高经济圈和产业链的运输能力；根据各交通运输的外部性来合理安排发达地区和欠发达地区的各种交通投资,加强交通对经济的带动作用等。本文还对未来交通与经济增长关系研究提出了建议与设想。
本文研究从趋势、重心、弹性、外部性以及收敛性等不同角度分析了我国交通与经济增长的关系,将时间和空间维度在分析中有机结合,并采用了较为先进的研究方法,其研究结果可与我国交通发展的实际情况相结合,给我国未来交通建设发展提出建议,具有理论和实践意义。
As an important part of the economic and social development since the reform and opening up, China's transportation industry has been developing rapidly. National Bureau of Statistics data show that from the reform and opening up to2010, China's high-speed rail mileage ranked second in the world, railway mileage ranks third in the world. Total mileage of China's comprehensive transportation network during this period increased by3.4times,4.5times increase in highway mileage, railway mileage increased by1.8-fold,2.2-fold increase in the number of airports. Transportation system that can be achieved in passenger traffic increased8.7-fold,5.1-fold increase in the amount of cargo transport. Clarify the relationship between China's transportation and economic growth will contribute to the strategic planning of the future development of traffic, and to lay the foundation for further economic growth, and is the main content of this paper.
In this paper the study of the relations between traffic and economic growth in China are from different angles, using different methods and models in empirical research, and flexible combination of the dimension of time and space. The full multi-dimensional display of China's relationships of transportation and economic growth.
Chapter2summarize the theoretical basis of empirical economics and econometrics. According to previous research of relations and interactions of transportation and economic growth, a virtuous circle diagram for transportation and economic development is summarized and formed to lay a theoretical foundation to explain the complex relationship between transport and economic growth.
In chapter3we first analyze the overall relationship between China's transportation and economic growth, including from the time dimension whether the transport's trend is consistent with the trend of China's economic growth, the level of transport volume is consistent with the level of economic growth spatially. The results show that China's road transport is still the mainstream of transportation, and accounted for90%passenger transport and80%of the amount of freight transport, and the proportion are quite stable; substantial increase in railway passenger and waterborne freight; lags behind the development of civil aviation lags behind, but rapidly developed; structural differences of each province and prefecture-level city traffic are large.
Chapter3is the visual display of the relationships between transportation and economic growth from the time and space dimension. But how is the balanced regional development of transport in recent years, and the spatial relationship of transport and economy? This is the main content of chapter4. Chapter4uses space center of gravity analysis methods, combined with the year2001and2010China's prefecture-level city data of traffic and economy, to investigate China's transportation and economic spatial relationship and to reveal the balanced regional development of transport and economic growth. The results show that the center of gravity of various traffic variables are closer to the economic center of gravity during the ten years, transport and economic ties are closer. The most related transport variables are the total cargo and passengers by rail. Overall speaking, the regional development of China's economic, highways, waterways and civil aviation are quite balanced these days. However the migration of the center of gravity of the railway, both passenger traffic and cargo migrated southwestward. This means that southwestern China's railway traffic in this period greatly enhanced may be due to large-scale construction of large-scale development of the western region in southwest China Railway led to the increase in the railway transport supply.
Earlier study in this paper did not reveal the numerical relationship between traffic and economic growth. While it is the content of chapters5and6. There are two objective in chapter5:to reveal the economic elasticity of transport (i.e. how much the traffic volume growth if economic grow by one percentage) and the transport externalities (i.e. how a region's transport impact the economies of surrounding areas).Double logarithmic model (elastic model) and China's provinces data and prefecture-level city data from2001to2010are used in this chapter, and passenger and cargo traffic as the main object. Different results in different scales can used to reveal the transport externalities. Since the data used in this chapter, there are many outlier in the data used in this chapter, MM robust regression method is used to weed out the damaging effects of outliers on the regression results. The results in this chapter show that the results of the elasticity is quite different, and even not in the same trends for different levels. This paper argues that the differences of elasticity shows externalities of different transport variables, for example, the total passengers, passenger and freight by rails are have positive externalities, and the total cargo, passenger and freight by roads have negative externalities.
The elasticity study in chapter4are based on the speed of economic growth, which means how much the transport growth considering the speed of economic growth. While there are other measures, such as based on the economic development levels, which means how fast the transport developed for rich areas and poor areas. This problem is the convergence of the traffic, which is the main point in chapter6. Chapter6uses traffic convergence model and traffic and economic data of the prefecture-level cities in2001and2010. There is a serious spatial non-stationary for the data and models used in this chapter, so the Geographically Weighted Regression analysis method is employed. The results show that the Geographically Weighted Regression fit better than the ordinary linear regression, and can effectively solve the problem of spatial correlation. Overall speaking, both passenger traffic and cargo traffic are convergence, which means it grows faster in underdeveloped regions. However in different regions the convergence trends of transport are different. In eastern China, transports are convergence, and in the western region it is divergence.
Chapter7of this paper analyzes the achievements and shortcomings of the transport development of China's "Tenth Five-Year" and "Eleventh Five-Year" period, combined with the empirical results of this paper. Suggestions and ideas of the development of China's transportation are promoted, including:China transport structure still need to vigorously adjust, increase the proportion of other modes of transport other than road outside, give full play to the advantages of railway passenger and waterborne freight transportation to share transport pressures, and the development of the civil aviation should still be fast; strengthen rural transportation construction, in particular to ensure rural roads construction quality and maintenance. It is an effective solution to rural residents travel difficult problem, and led the development of the rural economy; implementation of various specialized traffic policy, such as the implementation of the separation of vehicles and passenger construction, reducing the probability of traffic accidents; special transport building in economic circles and industry chain, improve the transport capacity of economic circle and the industry chain; reasonable arrangements for transportation, invest in which have positive externalities in developed regions and invest in which have negative externalities in less developed regions, strengthen the role of traffic driven economy; etc. This paper also put forward suggestions and ideas for the future study of traffic and economic growth relationships.
This study analyzed from different angles of the trend, the center of gravity, elasticity, and convergence of traffic and economic growth, combing time and space dimension in the analysis, and uses state-of-the-art research methods. Its findings can be combined with the actual situation of China's transport development, and to make recommendations to our future transportation development, have both theoretical and practical significance.
本文从不同角度来研究我国交通与经济增长的关系,分别采用了不同的方法和模型,并在实证研究中灵活的将时间维度和空间维度相结合,全面而立体的展示我国交通与经济增长关系的时空格局。
本文第二章整理归纳了本文实证分析所需的经济学和计量经济学理论基础。根据前人对交通与经济的关系和相互作用的研究成果,归纳总结并形成了交通与经济发展的良性循环图,为解释交通与经济增长的复杂关系奠定了理论基础。本文第三章首先从整体上分析了我国交通与经济增长之间的关系,包括从时间维度上分析各交通变量与我国经济增长的趋势是否一致,从空间范围上来看各地区交通的运量与经济的发达程度是否一致,有何特点。结果显示：我国交通客运量和货运量的增长与经济增长基本同步；公路运输是我国最主要的运输方式,分别占总客运量和总货运量的90%和80%以上,其次为铁路运输,但各个省份的交通结构差异很大；公路运输和铁路运输与我国经济发达水平的空间分布较为一致。我国水运主要从事货物运输,主要分布在沿海口岸、长江和珠江流域；而我国民航主要从事旅客运输,主要分布在我国各大中城市。
第三章是从时间和空间上对我国交通与经济增长关系的一个直观展示,近年来我国交通的区域性发展是否平衡,和经济的空间关系又如何呢?这即是第四章的主要研究内容。第四章采用空间重心分析方法,结合2001年和2010年我国地级市的交通和经济数据,研究了我国交通与经济的空间关系,以揭示我国各交通的区域发展的平衡性和与经济的空间相关程度。结果显示：我国各种交通变量均向经济重心靠拢,说明交通与经济的联系更加密切,其中总货运量、铁路客运与经济重心距离较小,较其他变量与经济增长关系更紧密。整体上来说我国经济、公路、水路和民航的区域发展较为均衡,而铁路的重心迁移较大,无论是客运量还是货运量都向西南地方迁移,说明这期间西南地区的铁路运量大为增强,可能是由于西部大开发中西南地区铁路大规模的建设导致铁路供给的增加。
前面所做的研究未揭示交通与经济增长的数量关系,本文第五章和第六章内容则采用回归分析方法来揭示其数量关系。第五章研究主要有两个目的,第一是研究交通的经济弹性(即经济增长一个百分点,交通运量增长多少)以及近十年来经济弹性的趋势,第二是研究交通的外部性(即一个地区的交通对周围地区有何影响)。本章所采用的是双对数模型(弹性模型)和我国2001年到2010年省份和地级市的交通和经济数据,并以客货运量为主要研究对象。采用了不同尺度下交通的经济弹性的差异来分析交通的外部性。由于本章所采用的数据存在较多异常值,还采用了MM稳健回归方法来剔除异常值对回归结果的破坏作用。本章研究结果显示：不同尺度下各交通变量的弹性差异较大,时间趋势也不相同。其中总客运量、铁路客运和铁路货运货运有正的外部性,而总货运量、公路货运量和公路客运量有负的外部性。
第五章所研究的弹性关系是以经济发展速度为基准的,即研究经济增长快的地方交通增长的快慢,而研究交通与经济增长之间的关系还可以以经济发达水平为基准,即研究经济发达地区的交通增长的快慢问题,该问题即为交通的收敛性。本文第六章建立了交通的收敛性模型,以我国客货运量为研究对象,采用了2001年和2010年地级市的交通和经济数据。由于本章所采用的数据和模型所存在严重的空间相关性,采用普通线性回归拟合会造成较为严重的偏差,故本章用地理加权回归来进行分析。结果显示,地理加权回归比普通线性回归有更好的拟合能力,并能有效解决空间相关性问题。整体上来说,我国交通无论是客运量还是货运量都是收敛的,即欠发达地区交通运量增长的更快。但交通的收敛性有有着明显的地域特征,我国东部地区交通呈现收敛之势,而西部地区则是发散的。
本文第七章分析了我国“十五”和“十一五”期间交通发展的成果与不足,并结合本文前面所做的实证分析结果,提出了我国交通运输业发展的建议与设想,包括：我国运输结构仍需要大力调整,提高除公路外的其他运输方式的比重,充分发挥铁路客运和水路货运的优势,分担交通运输压力,并积极发展民航业；实行各种专项交通政策,打造各经济圈和产业链的专项交通,提高经济圈和产业链的运输能力；根据各交通运输的外部性来合理安排发达地区和欠发达地区的各种交通投资,加强交通对经济的带动作用等。本文还对未来交通与经济增长关系研究提出了建议与设想。
本文研究从趋势、重心、弹性、外部性以及收敛性等不同角度分析了我国交通与经济增长的关系,将时间和空间维度在分析中有机结合,并采用了较为先进的研究方法,其研究结果可与我国交通发展的实际情况相结合,给我国未来交通建设发展提出建议,具有理论和实践意义。
As an important part of the economic and social development since the reform and opening up, China's transportation industry has been developing rapidly. National Bureau of Statistics data show that from the reform and opening up to2010, China's high-speed rail mileage ranked second in the world, railway mileage ranks third in the world. Total mileage of China's comprehensive transportation network during this period increased by3.4times,4.5times increase in highway mileage, railway mileage increased by1.8-fold,2.2-fold increase in the number of airports. Transportation system that can be achieved in passenger traffic increased8.7-fold,5.1-fold increase in the amount of cargo transport. Clarify the relationship between China's transportation and economic growth will contribute to the strategic planning of the future development of traffic, and to lay the foundation for further economic growth, and is the main content of this paper.
In this paper the study of the relations between traffic and economic growth in China are from different angles, using different methods and models in empirical research, and flexible combination of the dimension of time and space. The full multi-dimensional display of China's relationships of transportation and economic growth.
Chapter2summarize the theoretical basis of empirical economics and econometrics. According to previous research of relations and interactions of transportation and economic growth, a virtuous circle diagram for transportation and economic development is summarized and formed to lay a theoretical foundation to explain the complex relationship between transport and economic growth.
In chapter3we first analyze the overall relationship between China's transportation and economic growth, including from the time dimension whether the transport's trend is consistent with the trend of China's economic growth, the level of transport volume is consistent with the level of economic growth spatially. The results show that China's road transport is still the mainstream of transportation, and accounted for90%passenger transport and80%of the amount of freight transport, and the proportion are quite stable; substantial increase in railway passenger and waterborne freight; lags behind the development of civil aviation lags behind, but rapidly developed; structural differences of each province and prefecture-level city traffic are large.
Chapter3is the visual display of the relationships between transportation and economic growth from the time and space dimension. But how is the balanced regional development of transport in recent years, and the spatial relationship of transport and economy? This is the main content of chapter4. Chapter4uses space center of gravity analysis methods, combined with the year2001and2010China's prefecture-level city data of traffic and economy, to investigate China's transportation and economic spatial relationship and to reveal the balanced regional development of transport and economic growth. The results show that the center of gravity of various traffic variables are closer to the economic center of gravity during the ten years, transport and economic ties are closer. The most related transport variables are the total cargo and passengers by rail. Overall speaking, the regional development of China's economic, highways, waterways and civil aviation are quite balanced these days. However the migration of the center of gravity of the railway, both passenger traffic and cargo migrated southwestward. This means that southwestern China's railway traffic in this period greatly enhanced may be due to large-scale construction of large-scale development of the western region in southwest China Railway led to the increase in the railway transport supply.
Earlier study in this paper did not reveal the numerical relationship between traffic and economic growth. While it is the content of chapters5and6. There are two objective in chapter5:to reveal the economic elasticity of transport (i.e. how much the traffic volume growth if economic grow by one percentage) and the transport externalities (i.e. how a region's transport impact the economies of surrounding areas).Double logarithmic model (elastic model) and China's provinces data and prefecture-level city data from2001to2010are used in this chapter, and passenger and cargo traffic as the main object. Different results in different scales can used to reveal the transport externalities. Since the data used in this chapter, there are many outlier in the data used in this chapter, MM robust regression method is used to weed out the damaging effects of outliers on the regression results. The results in this chapter show that the results of the elasticity is quite different, and even not in the same trends for different levels. This paper argues that the differences of elasticity shows externalities of different transport variables, for example, the total passengers, passenger and freight by rails are have positive externalities, and the total cargo, passenger and freight by roads have negative externalities.
The elasticity study in chapter4are based on the speed of economic growth, which means how much the transport growth considering the speed of economic growth. While there are other measures, such as based on the economic development levels, which means how fast the transport developed for rich areas and poor areas. This problem is the convergence of the traffic, which is the main point in chapter6. Chapter6uses traffic convergence model and traffic and economic data of the prefecture-level cities in2001and2010. There is a serious spatial non-stationary for the data and models used in this chapter, so the Geographically Weighted Regression analysis method is employed. The results show that the Geographically Weighted Regression fit better than the ordinary linear regression, and can effectively solve the problem of spatial correlation. Overall speaking, both passenger traffic and cargo traffic are convergence, which means it grows faster in underdeveloped regions. However in different regions the convergence trends of transport are different. In eastern China, transports are convergence, and in the western region it is divergence.
Chapter7of this paper analyzes the achievements and shortcomings of the transport development of China's "Tenth Five-Year" and "Eleventh Five-Year" period, combined with the empirical results of this paper. Suggestions and ideas of the development of China's transportation are promoted, including:China transport structure still need to vigorously adjust, increase the proportion of other modes of transport other than road outside, give full play to the advantages of railway passenger and waterborne freight transportation to share transport pressures, and the development of the civil aviation should still be fast; strengthen rural transportation construction, in particular to ensure rural roads construction quality and maintenance. It is an effective solution to rural residents travel difficult problem, and led the development of the rural economy; implementation of various specialized traffic policy, such as the implementation of the separation of vehicles and passenger construction, reducing the probability of traffic accidents; special transport building in economic circles and industry chain, improve the transport capacity of economic circle and the industry chain; reasonable arrangements for transportation, invest in which have positive externalities in developed regions and invest in which have negative externalities in less developed regions, strengthen the role of traffic driven economy; etc. This paper also put forward suggestions and ideas for the future study of traffic and economic growth relationships.
This study analyzed from different angles of the trend, the center of gravity, elasticity, and convergence of traffic and economic growth, combing time and space dimension in the analysis, and uses state-of-the-art research methods. Its findings can be combined with the actual situation of China's transport development, and to make recommendations to our future transportation development, have both theoretical and practical significance.
引文
[I]Ablramovitz,M, Catching Up, Forging Ahead, and Falling Behind, Journal of Economicc History,1986 (46):385-406
[2]Ahmed R, Mahabub H. Development Impact of Rural Infrastructure in Bangladesh[M]. Norway, International Food Policy Research institute,1990
[3]Anselin L. Spatial Econometrics:Methods and Models[M]. Dordrecht:Kluwer Academic Publishers,1988:34-57
[4]Anselin L, Bera A. Spatial dependence in linear regression models with an introduction to spatial econometrics[A]. In:Ullah A, Giles D E A(Eds.). Handbook of Applied Economic Statistics[C]. New York:Marcel Dekker,1998:237-289.
[5]Aschauer, A.D.:Is public expenditure productive? [J] J. Monetary Econ.1989,23(2),177-200
[6]Baumol. W, Production Growth, Convergence and Welfare:What the Long-run Data Show, American Economic Review,1986 (76):1072-1085
[7]Berechman, J., Ozmen, D., Ozbay, K., Empirical analysis of transportation investment and economic development at state, county and municipality levels[J]. Transportation,2006,33(6), 537-551.
[8]Beenhakker H L, Issues in Agricultural Marketing Strategy and Pricing Policy [J], The World Bank Discussion Paper,1987, Transportation Issues Series No TRP7.
[9]Bitter, C., Mulligan, G.F., Dall'erba, S., Incorporating spatial variation in housing attribute prices:a comparison of Geographically Weighted Regression and the Spatial Expansion Method[J]. Journal of Geographical Systems,2007,9(1),7-27.
[10]Boarnet, M.G.:The direct and indirect economic effects of transportation infrastructure[J]. Working Paper No.340, The Transportation Center, University of California,1996, Berkeley, CA
[11]Bowman A W. An alternative method of cross-validation for the smoothing of densityestimates[J]. Biometrika,1984 71:353-60
[12]Brunsdon C, Fotheringham AS, Charlton M, Geographically weighted regression:a method for exploring spatial non-stationarity[J]. Geogr Anal,1996,,28(4):281-298
[13]Brunsdon C, Fotheringham AS, Charlton ME.Geographically weighted regression-modelling spatial non-stationarity[J], Journal of the Royal Statistical Society, Series D-The Statistician,1998,47(3):431-443
[14]Brown B.M., Statistical uses of the spatial median[J], Journal of the Royal Statistical Society. 1983, Series B:4525-30.
[15]Calvo, E., Escolar, M., The local voter:a Geographically Weighted Approach to ecological inference[J]. American Journal of Political Science,2003,47(1),189-204.
[16]Casetti E. Generating models by the expansion method:applications to geographical research[J]. Geographical Analysis,1972,4(1):81-91.
[17]Casetti E. The expansion method, mathematical modeling, and spatial econometrics[J]. International Regional Science Review,1997,20(1):9-33.
[18]Chor Foon Tang, A Re-examination of the relationship between electricity consumption and economic growth in Malaysia, Energy Policy,2008(36):3077-3085
[19]Cleveland W S. Robust locally weighted regression and smoothing scatterplots[J]. Journalof the American Statistical Association,1979,74:829-36
[20]Costa, J.S., Ellson R.W., Martin R.C.:Public capital, regional output, and development:some empirical evidence[J]. J. Regional Sci.1987:419-437
[21]Creightley C D, Transport and Economic Performance:A Survey of Economic Performance[M], Washington:World Bank,1993:8-10
[22]Croux C., Haesbroeck G., Principal component analysis based on robust estimators of the covariance or correlation matrix:influence function and efficiencies[J], Biometrika,2000, 87:603-618.
[23]Croux C., Rousseeuw P.J., Time-efficient algorithms for two highly robust estimators of scale[A], in:Y. Doge, J. Whittaker (Eds.), Computational Statistics, vol.1, Physica-Verlag, Heidelberg,1992, pp.411-428.
[24]Deller, D., Rural poverty, tourism and spatial heterogeneity [J]. Annals of Tourism Research,2009,37(1),180-205.
[25]Duffy-Deno, K.T., Eberts, R.W.:Public infrastructure and regional economic development:a simultaneous equations approach[J]. J. Urban Econ.1991,30(3),329-343
[26]Eberts, R.W., Estimating the contribution of urban public infrastructure to regional economic growth[J]. Federal Reserve Bank of Cleveland, Working Paper,1986, No.8610
[27]Eakin, D.H.:Public sector capital and productivity puzzle[J]. The Rev. Econ. Stat.1994,76(1), 12-21
[28]Eakin, D.H., Schwartz, A.E.:Spatial productivity spillovers from public infrastructure: evidence from state highways[J]. International Tax and Public Finance,1995
[29]Faber S, Paez A, A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations [J], J Geogr Syst,2007,9 (4):371-396
[30]Fotheringham A S. Trends in quantitative methods I:stressing the local[J]. Progress in Human Geography.1997,21(1):88-96.
[31]Fotheringham A S, Brunsdon C, Charlton M. Geographically Weighted Regression:The Analysis of Spatially Varying Relationships [M]. Hoboken:Wiley,2002:25-41.
[32]Forkenbrock, D.J., Foster, N.S.J.:Economic benefits of a corridor highway investment[J]. Transport. Res.1990,24A(4),303-312
[33]Garcia-Milla, T., McGuire T.J.:The contribution of publicly provided inputs to States' economies[J]. Regional Sci. Urban Econ.1992,22,229-241
[34]Gnanadesikan R., Kettenring J.R., Robust estimates, residuals, and outlier detection with multiresponse data[J], Biometrics,1972,28:81-124.
[35]Goldstein H. Multilevel models in education and social research[M]. London:Oxford University Press,1987:7-31.
[36]Hampel F.R., A general qualitative definition of robustness[J], Annals of Mathematical Statistics,1971,42:1887-1896.
[37]Hampel F.R., The influence curve and its role in robust estimation[J], Journal of the American Statistical Association,1974,69:383-393.
[38]Hampel F.R., Ronchetti E.M., Rousseeuw P.J., Stahel W.A., Robust Statistics:the Approach Based on Influence Functions[J], Wiley,1986, New York
[39]Hastie T J, Tibshirani R J. Generalized additive models[M]. London:Chapman & Hall,1990
[40]Harrisl, R., Singleton, A., Grose, D., Brunsdon, C., Longley, P., Grid-enabling Geographically Weighted Regression:a case study of participation in higher education in England[J]. Transactions in GIS,2010,14(1),43-61.
[41]Haughwout, A.F.:Public infrastructure investments, productivity and welfare in fixed geographic areas[J]. Federal Reserve Bank of New York,2000,Staff Report 104, May
[42]Hoaglin D C, Welsch R E.1978 The hat matrix in regression and ANOVA[J]. The American Statistician,1978,32:17-28
[43]Hove H., Liang Y.-Z., Kvalheim O.M., Trimmed object projection:a nonparametric latent-structure decomposition method[J], Chemometrics and Intelligent Laboratory Systems,1995,27:33-40.
[44]Huber P.J., Robust Statistics[M],1981, Wiley, New York
[45]Hubert M., Rousseeuw P.J., Verboven S., A fast method for robust principal components with applications to chemometrics[J], Chemometrics and Intelligent Laboratory Systems,2002, 60:101-111.
[46]Hubert M., BrandenV. K., Robust methods for partial least squares regression[J], Journal of Chemometrics,2003:17:537-549.
[47]Hubert M., Verboven S., A robust PCR method for high-dimensional regressors[J], Journal of Chemometrics,2003,17:438-452.
[48]Hurvich C M, Simonoff J S, Tsai C-L. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion[J]. Journal of the Royal Statistical Society Series B,1998,60:271-93
[49]Isik, O., Pinarcioglu, M.M., Geographies of a silent transition:a Geographically Weighted Regression approach to regional fertility differences in Turkey [J]. Humans, Social Sciences and Law,2006,22(4),399-421.
[50]Jones J P III, Casetti E. Applications of the Expansion Method[M]. London:Routledge,1992: 17-22.
[51]Jones K. Specifying and estimation multilevel models for geographical research[J]. Transactions of the Institute of British Geographers,1991,16(2):148-159.
[52]Jones K. Multilevel Models for Geographical Research[M]. Norwich:Environmental Publications,1991:5-13.
[53]Kveiborg, O., Fosgerau, M., Decomposing the decoupling of Danish road freight traffic growth and economic growth[J], Transport Policy,2007,14(1),39-48.
[54]Markandya.A., Pedroso.S., and Streimikiene.D.,Energy efficiency in transition economies:is there convergence towards the EU average?Social science research network electronic paper, NOTA DI LAVORO 89
[55]Lakshmanan, T.R., The broader economic consequences of transport infrastructure investments [J]. Journal of Transport Geography,2011,19,1-12.
[56]Li G., Chen Z.L., Projection pursuit approach to robust dispersion matrices and principal components-primary theory and Monte-Carlo[J], Journal of the American Statistical Association,1985,381:759-766.
[57]Loader C. Local regression and likelihood[M]. New York:Springer,1999
[58]Lynde, C., Richmond J.:The role of public capital in production[J]. Rev. Econ. Stat,1992,74, 37-44
[59]Malinowski E.R., Factor Analysis in Chemistry[M],1991, John Wiley and Sons.INC, New York
[60]Martens H, Naes T, Mutivariate Calibration[M],1989, John Wiley and Sons, Chichester, UK.
[61]Moomaw, R.L., Willians, M.:Total factor productivity growth in manufacturing:further evidence from the States[J]. J. Regional Sci.1991,31(1),17-34
[62]Munnell, A.H.:How does public infrastructure affect regional economic performance [J]. New Engl. Econ. Rev.1990, September-October,11-32
[63]Munnell, A.H.:Policy watch:infrastructure investment and economic growth [J]. J. Econ. Perspectives 6(4),1992,189-198
[64]Naes T., Isaksson T., Fearn T., Davis T, Multivariate Calibration and Classification[M],2002, NIR Publications, Chichester, UK
[65]Nakaya T. Local spatial interaction modelling based on the geographically weighted regression approach[A]. In Thomas R, Boots B, Okabe A (eds) Modelling geographical systems: statistical and computational applications. Dordrecht:Kluwer,2002
[66]Owen, D., Hogarth, T., Green, A.E., Skills, transport and economic development:evidence from a rural area in England[J]. Journal of Transport Geography,2012,21,80-92.
[67]Ozbay K., Ozmen D., Berechman J.:Empirical analysis of the relationship between accessibility and economic development[J]. ASCE, J. Urban Planning and Develop.2003, 129(2),97-119
[68]Paez A, Uchida T, Miyamoto K, A general framework for estimation and inference of geographically weighted regression models:1, location-specific kernel bandwidths and a test for locational heterogeneity[J], Environ plan A,2002,34(4):733-755.
[69]RajkoR., Treatment of model error in calibration by robust and fuzzy procedures[J], Analytical Letters,1994,27:215-228.
[70]Rosenberg B. A survey of stochastic parameter regression[J]. Annals of Economic and Social Measurement,1973,1:381-397
[71]Rousseeuw P.J., Least median of squares regression[J], Journal of the American Statistical Association,1984,79,871-880.
[72]Rousseeuw P.J., Leroy A.M., Robust Regression and Outlier Detection[M], John Wiley & Sons Inc.,1987, New York
[73]Rousseeuw P.J., Multivariate estimation with high breakdown point[A], in:W. Grossmann, G. Pflug, I. Vinche (Eds.), Mathematical Statistics and Applications, vol. B, Reidel Dordrecht, 1985, pp.283-297.
[74]Rousseeuw P.J., Croux C., Alternatives to median absolute deviation[J], Journal of the American Statistical Association,1993,88:1273-1283.
[75]Sarbu C., Pop H.F., Fuzzy robust estimation of central location[J], Talanta,2001,54:128-130.
[76]Small C.G., A survey of multidimensional medians, International Statistical Review[J],1990,58: 263-277.
[77]Schuurmanl, N., Peters, P.A., Oliver, L.N., Are obesity and physical activity clustered? A spatial analysis linked to residential density[J]. Epidemiology,2009,17(12),2202-2209.
[78]Schwartz G. Estimating the dimension of a model[J]. The Annals of Statistics,1978,6:461-4
[79]Serneels S., Croux C., Filzmose P.r, Van Espen P.J., Partial Robust M-Regression[J], Chemometrics and Intelligent Laboratory Systems,2005,79:55-64.
[80]Shearmur, R., Apparicio, P., Lizion, P., Polese, M., Space, time, and local employment growth: an application of spatial regression analysis[J]. Growth and Change,2007,38(4),696-722.
[81]Spjotvoll E. Random coefficients regression models:a review[J]. Mathematische Operations for schungund Statistik,1977,8:69-93
[82]Swamy P A V B. Statistical Inference in Random Coefficient Regression Models[M]. Berlin: Springer,1971:4-21.
[83]Trig D W, Leach D H. Exponential smoothing with an adaptive response rate[J]. Operational Research Quarterly,1968,18(1):53-59.
[84]Wakeling I.N., H.J.H. Macfie H.J.H., A robust PLS procedure[J], Journal of Chemometrics,1992,4:189-198.
[85]Walczak B., Massart D.L., Robust principal components regression as a detection tool for outliers[J], Chemometrics and Intelligent Laboratory Systems,'1995,27:41-54.
[86]Waters, W.G.II.:Empirical Studies of Infrastructure Investment and Economic Activity: Evidence from Canada[M]. World Conference on Transport Research,2004, Istanbul
[87]Weber A., Uber den standort der industrien[M], Tubingen.1909 English translation by C. Friedrich Alfred Weber's Theory of Location of Industries, University of Chicago Press,1929
[88]蔡防,都阳,中国地区经济增长的趋同于差异——对西部开发战略的启示,经济研究,2000(10)：30-37
[89]蔡剑红,朱道林.多空间尺度普通住宅用地的合理地价研究[J].中国房地产,2012,2：38-42
[90]曹建军,刘永娟,李金莲.江苏省区域经济差异的多尺度研究[J].地域研究与开发,2010,29(5)：55-59
[91]曹宗龙,陈松林.基于GIS的经济与产业重心空间演变及动态分析——以福州市为例[J].亚热带水土保持,2011,23(2)：22-32
[92]陈小瑜.基于多尺度空间聚类的经济区域划分研究[J].重庆师范大学学报(自然科学版),2011,28(5)：81-92
[93]陈秀山,徐瑛.中国区域差距影响因素的实证研究[J].中国社会科学,2004,4(5)：119-129
[94]戴鞍钢,交通与经济的互为制约——以近代中国西部省份为例[J],中国延安干部学院学报,2010,2(14)：69-74
[95]董艳,于涛,李淑俊,区域经济与教育投入的空间计量分析[J],石家庄学院学报,2010,12(6)：91-94
[96]董先安,浅释中国地区收入差距：1952-2002,经济研究,2004(9)：48-59
[97]管卫华,林振山,顾朝林.中国区域经济发展差异及其原因的多尺度分析[J].经济研究,2006,7：117-125
[98]海成,李健,杨艳,中国公路交通与经济发展关系的实证研究[J],长安大学学报(社会科学版)2007,9(2)：8-13
[99]何志斌,等.荒漠植被植物种多样性对空间尺度的依赖[J].生态学报,2004,24(6)：1146-1149
[100]简美锋,万智恩.石家庄市人口重心与经济重心的演变轨迹对比研究[J].经济视角,2011,2：4-7
[101]姜磊,季民河,基于空间异质性的中国能源消费强度研究——资源禀赋、产业结构、技术进步和市场调节机制的视角[J],产业经济研究,2011,53：61-70
[102]兰国玉,朱华,曹敏.西双版纳热带雨林树种多样性的尺度效应[J].西北植物学报,2012,32(7)：1454-1458
[103]李蓉,李宇,西部地区交通区域划分问题的研究[J],华东交通大学学报,2006,23(2)：40-43
[104]李志,周生路,张红富,姚鑫,吴巍,基于GWR模型的南京市住宅地价影响因素及其边际价格作用研究[J],中国土地科学,2009,23(10)：20-25
[105]刘牧鑫,蒋伟,外商直接投资与区域经济增长：基于地理加权回归模型的研究[J],统计应用研究,2009,24(12),62-65
[106]刘旭华,王劲峰,孟斌.中国区域经济时空动态不平衡发展分析[J].地理研究,2004,23(4)：530-540
[107]卢二坡,黄炳艺,基于稳健MM估计的统计数据质量评估方法[J],统计研究,2010,27(12)：16-22
[108]卢布,等.不同尺度农业预测差异及其处理[J].中国农业资源与区划,2005,26(6)：54-56
[109]卢俊宇,等.基于时空尺度的中国省级区域能源消费碳排放公平性分析[J].自然资源学报,2012,27(12)：2006-2017
[110]吕萍,甄辉,基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J],经济地理,2010,30(3)：472-478
[111]龙莹,空间异质性与区域房地产价格波动的差异——基于地理加权回归的实证研究[J],中央财经大学学报,2010,11：80-85
[112]齐绍洲,罗威,中国地区经济增长与能源消费强度差异分析,经济研究,2007(7)：74-81
[113]齐绍洲,云波,李锴,中国经济增长与能源消费强度差异的收敛性及机理分析,经济研究,2009(4)：56-64
[114]齐艳红.不同空间尺度下城市土地利用绩效评价指标体系的构建[J].国土与自然资源研究,2012,4：30-31
[115]宋帮英,苏方林,我国省域碳排放量与经济发展的GWR实证研究[J],财经科学,2010,265：41-49
[116]苏方林,基于地理加权回归模型的县域经济发展的空间因素分析——以辽宁省县域为例[J],学术论坛,2005,172：81-84
[117]苏方林,省域R&D知识溢出的GWR实证分析[J],数量经济技术经济研究,2007,2：145-153
[118]孙孝文,和谐交通体系构建研究[D],武汉理工大学博士论文,2007
[119]汤庆园,徐伟,艾福利,基于地理加权回归的上海市房价空间分异及其影响因子研究[J],经济地理,2012,32(2)：52-58
[120]汤浒,交通与市场规模的关系研究[D],北京交通大学硕士论文,2009
[121]王彬,王宜强.改革开放以来福建省经济重心格局演变及其空间差异[J],地理研究,2011,30(10)：1882-1890
[122]王斌会,稳健主成分分析方法研究及其在经济管理中的应用[J],统计研究,2007,24(8)：72-76
[123]王成,蒋福霞,王利平.统筹城乡视域下农户土地利用意识多尺度认知研究[J].云南师范大学学报(哲学社会科学版),2012,44(2)：60-66
[124]王武科,李同升,刘笑明.不同尺度下农业创新技术空间扩散的实证研究——以中国果业协会果业技术扩散为例[J].人文地理,2009,1：76-80
[125]文嫮,金雪琴.基于不同区域尺度的中国信息制造业技术创新能力差异研究[J].社会科学辑刊,2010,5：103-108.
[126]魏后凯,刘楷.我国地区差异变动趋势分析与预测[J].中国工业经济,1994,4：28-36
[127]吴玉鸣,空间计量经济模型在省域研发与创新中的应用研究[J],数量经济技术经济研究,2006,5：74-85
[128]吴玉鸣,李建霞,省域经济增长与电力消费的局域空间计量经济分析[J],地理科学,2009,29(1)：30-35
[129]谢静,张阳生,雷昉,黄卓,经济转型期陕北公路交通与经济发展的关联分析[J],人文地理,2010(5)：103-107
[130]徐建华,等.中国区域经济差异的时空尺度分析[J].地理研究,2005,1：55-58
[131]许乃星,蒲之艳,张静晶,高智睿,公路交通与经济发展适应性评价研究[J],交通运输工程与信息学报,2011,9(3)：79-86
[132]徐现祥,李郇,中国城市经济增长的趋同分析,经济研究2004(5)：40-48
[133]杨开忠.中国区域经济差异变动研究[J].经济研究,1991,2：28-33
[134]杨世琦,杨正礼,高旺盛.不同尺度下区域农业系统协调度的评价[J].西北农林科技大学学报(自然科学版)2008,36(5)：64-72
[135]杨宇,刘毅,齐元静.基于不同区域尺度的中国经济发展阶段判断[J].经济问题探索,2012,12：1-6
[136]杨为民.地区收入差距实证研究[J].经济研究,1992,1：70-74.
[137]曾刚,林兰.不同空间尺度的技术扩散影响因子研究[J].科学学与科学技术管理,2006,2：22-26
[138]张晶,封志明,杨艳昭.现阶段中国不同尺度的粮食减产类型分析[J].资源科学,2006,28(6)：28-32
[139]张培峰.不同空间尺度的经济发展与城市化的相关分析[J].资源环境与发展,2007,3：22-26
[140]张耀军,任正委,基于地理加权回归的山区人口分布影响因素实证研究——以贵州省毕节地区为例[J],人口与资源环境,2012,36(4)：53-63
[141]周杰文.基于空间尺度的湖北省经济差异分析[J].商场现代化,2011,8：37-38
[142]周杰文.中部地区经济差异的多尺度分析[J].广西社会科学,2011,10：57-60
[143]周杰文,张璐.中部地区经济差异的空间尺度效应分析[J].地理与地理信息科学,2011,27(1)：49-52
[144]周文兴, 林新朗,中国住房价格与城市化水平的关系研究——动态面板和空间计量的实证分析[J],重庆大学学报(社会科学版),2012,18(5)：1-7
[145]周志龙,考虑区域经济特征的中国综合经济交通运输结果变化实证分析[D],北京交通大学硕士论文,2011
[146]周玉翠,齐清文,冯灿飞,近10年来中国省级经济差异动态变化特征,地理研究,2002(21)：781-790
[2]Ahmed R, Mahabub H. Development Impact of Rural Infrastructure in Bangladesh[M]. Norway, International Food Policy Research institute,1990
[3]Anselin L. Spatial Econometrics:Methods and Models[M]. Dordrecht:Kluwer Academic Publishers,1988:34-57
[4]Anselin L, Bera A. Spatial dependence in linear regression models with an introduction to spatial econometrics[A]. In:Ullah A, Giles D E A(Eds.). Handbook of Applied Economic Statistics[C]. New York:Marcel Dekker,1998:237-289.
[5]Aschauer, A.D.:Is public expenditure productive? [J] J. Monetary Econ.1989,23(2),177-200
[6]Baumol. W, Production Growth, Convergence and Welfare:What the Long-run Data Show, American Economic Review,1986 (76):1072-1085
[7]Berechman, J., Ozmen, D., Ozbay, K., Empirical analysis of transportation investment and economic development at state, county and municipality levels[J]. Transportation,2006,33(6), 537-551.
[8]Beenhakker H L, Issues in Agricultural Marketing Strategy and Pricing Policy [J], The World Bank Discussion Paper,1987, Transportation Issues Series No TRP7.
[9]Bitter, C., Mulligan, G.F., Dall'erba, S., Incorporating spatial variation in housing attribute prices:a comparison of Geographically Weighted Regression and the Spatial Expansion Method[J]. Journal of Geographical Systems,2007,9(1),7-27.
[10]Boarnet, M.G.:The direct and indirect economic effects of transportation infrastructure[J]. Working Paper No.340, The Transportation Center, University of California,1996, Berkeley, CA
[11]Bowman A W. An alternative method of cross-validation for the smoothing of densityestimates[J]. Biometrika,1984 71:353-60
[12]Brunsdon C, Fotheringham AS, Charlton M, Geographically weighted regression:a method for exploring spatial non-stationarity[J]. Geogr Anal,1996,,28(4):281-298
[13]Brunsdon C, Fotheringham AS, Charlton ME.Geographically weighted regression-modelling spatial non-stationarity[J], Journal of the Royal Statistical Society, Series D-The Statistician,1998,47(3):431-443
[14]Brown B.M., Statistical uses of the spatial median[J], Journal of the Royal Statistical Society. 1983, Series B:4525-30.
[15]Calvo, E., Escolar, M., The local voter:a Geographically Weighted Approach to ecological inference[J]. American Journal of Political Science,2003,47(1),189-204.
[16]Casetti E. Generating models by the expansion method:applications to geographical research[J]. Geographical Analysis,1972,4(1):81-91.
[17]Casetti E. The expansion method, mathematical modeling, and spatial econometrics[J]. International Regional Science Review,1997,20(1):9-33.
[18]Chor Foon Tang, A Re-examination of the relationship between electricity consumption and economic growth in Malaysia, Energy Policy,2008(36):3077-3085
[19]Cleveland W S. Robust locally weighted regression and smoothing scatterplots[J]. Journalof the American Statistical Association,1979,74:829-36
[20]Costa, J.S., Ellson R.W., Martin R.C.:Public capital, regional output, and development:some empirical evidence[J]. J. Regional Sci.1987:419-437
[21]Creightley C D, Transport and Economic Performance:A Survey of Economic Performance[M], Washington:World Bank,1993:8-10
[22]Croux C., Haesbroeck G., Principal component analysis based on robust estimators of the covariance or correlation matrix:influence function and efficiencies[J], Biometrika,2000, 87:603-618.
[23]Croux C., Rousseeuw P.J., Time-efficient algorithms for two highly robust estimators of scale[A], in:Y. Doge, J. Whittaker (Eds.), Computational Statistics, vol.1, Physica-Verlag, Heidelberg,1992, pp.411-428.
[24]Deller, D., Rural poverty, tourism and spatial heterogeneity [J]. Annals of Tourism Research,2009,37(1),180-205.
[25]Duffy-Deno, K.T., Eberts, R.W.:Public infrastructure and regional economic development:a simultaneous equations approach[J]. J. Urban Econ.1991,30(3),329-343
[26]Eberts, R.W., Estimating the contribution of urban public infrastructure to regional economic growth[J]. Federal Reserve Bank of Cleveland, Working Paper,1986, No.8610
[27]Eakin, D.H.:Public sector capital and productivity puzzle[J]. The Rev. Econ. Stat.1994,76(1), 12-21
[28]Eakin, D.H., Schwartz, A.E.:Spatial productivity spillovers from public infrastructure: evidence from state highways[J]. International Tax and Public Finance,1995
[29]Faber S, Paez A, A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations [J], J Geogr Syst,2007,9 (4):371-396
[30]Fotheringham A S. Trends in quantitative methods I:stressing the local[J]. Progress in Human Geography.1997,21(1):88-96.
[31]Fotheringham A S, Brunsdon C, Charlton M. Geographically Weighted Regression:The Analysis of Spatially Varying Relationships [M]. Hoboken:Wiley,2002:25-41.
[32]Forkenbrock, D.J., Foster, N.S.J.:Economic benefits of a corridor highway investment[J]. Transport. Res.1990,24A(4),303-312
[33]Garcia-Milla, T., McGuire T.J.:The contribution of publicly provided inputs to States' economies[J]. Regional Sci. Urban Econ.1992,22,229-241
[34]Gnanadesikan R., Kettenring J.R., Robust estimates, residuals, and outlier detection with multiresponse data[J], Biometrics,1972,28:81-124.
[35]Goldstein H. Multilevel models in education and social research[M]. London:Oxford University Press,1987:7-31.
[36]Hampel F.R., A general qualitative definition of robustness[J], Annals of Mathematical Statistics,1971,42:1887-1896.
[37]Hampel F.R., The influence curve and its role in robust estimation[J], Journal of the American Statistical Association,1974,69:383-393.
[38]Hampel F.R., Ronchetti E.M., Rousseeuw P.J., Stahel W.A., Robust Statistics:the Approach Based on Influence Functions[J], Wiley,1986, New York
[39]Hastie T J, Tibshirani R J. Generalized additive models[M]. London:Chapman & Hall,1990
[40]Harrisl, R., Singleton, A., Grose, D., Brunsdon, C., Longley, P., Grid-enabling Geographically Weighted Regression:a case study of participation in higher education in England[J]. Transactions in GIS,2010,14(1),43-61.
[41]Haughwout, A.F.:Public infrastructure investments, productivity and welfare in fixed geographic areas[J]. Federal Reserve Bank of New York,2000,Staff Report 104, May
[42]Hoaglin D C, Welsch R E.1978 The hat matrix in regression and ANOVA[J]. The American Statistician,1978,32:17-28
[43]Hove H., Liang Y.-Z., Kvalheim O.M., Trimmed object projection:a nonparametric latent-structure decomposition method[J], Chemometrics and Intelligent Laboratory Systems,1995,27:33-40.
[44]Huber P.J., Robust Statistics[M],1981, Wiley, New York
[45]Hubert M., Rousseeuw P.J., Verboven S., A fast method for robust principal components with applications to chemometrics[J], Chemometrics and Intelligent Laboratory Systems,2002, 60:101-111.
[46]Hubert M., BrandenV. K., Robust methods for partial least squares regression[J], Journal of Chemometrics,2003:17:537-549.
[47]Hubert M., Verboven S., A robust PCR method for high-dimensional regressors[J], Journal of Chemometrics,2003,17:438-452.
[48]Hurvich C M, Simonoff J S, Tsai C-L. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion[J]. Journal of the Royal Statistical Society Series B,1998,60:271-93
[49]Isik, O., Pinarcioglu, M.M., Geographies of a silent transition:a Geographically Weighted Regression approach to regional fertility differences in Turkey [J]. Humans, Social Sciences and Law,2006,22(4),399-421.
[50]Jones J P III, Casetti E. Applications of the Expansion Method[M]. London:Routledge,1992: 17-22.
[51]Jones K. Specifying and estimation multilevel models for geographical research[J]. Transactions of the Institute of British Geographers,1991,16(2):148-159.
[52]Jones K. Multilevel Models for Geographical Research[M]. Norwich:Environmental Publications,1991:5-13.
[53]Kveiborg, O., Fosgerau, M., Decomposing the decoupling of Danish road freight traffic growth and economic growth[J], Transport Policy,2007,14(1),39-48.
[54]Markandya.A., Pedroso.S., and Streimikiene.D.,Energy efficiency in transition economies:is there convergence towards the EU average?Social science research network electronic paper, NOTA DI LAVORO 89
[55]Lakshmanan, T.R., The broader economic consequences of transport infrastructure investments [J]. Journal of Transport Geography,2011,19,1-12.
[56]Li G., Chen Z.L., Projection pursuit approach to robust dispersion matrices and principal components-primary theory and Monte-Carlo[J], Journal of the American Statistical Association,1985,381:759-766.
[57]Loader C. Local regression and likelihood[M]. New York:Springer,1999
[58]Lynde, C., Richmond J.:The role of public capital in production[J]. Rev. Econ. Stat,1992,74, 37-44
[59]Malinowski E.R., Factor Analysis in Chemistry[M],1991, John Wiley and Sons.INC, New York
[60]Martens H, Naes T, Mutivariate Calibration[M],1989, John Wiley and Sons, Chichester, UK.
[61]Moomaw, R.L., Willians, M.:Total factor productivity growth in manufacturing:further evidence from the States[J]. J. Regional Sci.1991,31(1),17-34
[62]Munnell, A.H.:How does public infrastructure affect regional economic performance [J]. New Engl. Econ. Rev.1990, September-October,11-32
[63]Munnell, A.H.:Policy watch:infrastructure investment and economic growth [J]. J. Econ. Perspectives 6(4),1992,189-198
[64]Naes T., Isaksson T., Fearn T., Davis T, Multivariate Calibration and Classification[M],2002, NIR Publications, Chichester, UK
[65]Nakaya T. Local spatial interaction modelling based on the geographically weighted regression approach[A]. In Thomas R, Boots B, Okabe A (eds) Modelling geographical systems: statistical and computational applications. Dordrecht:Kluwer,2002
[66]Owen, D., Hogarth, T., Green, A.E., Skills, transport and economic development:evidence from a rural area in England[J]. Journal of Transport Geography,2012,21,80-92.
[67]Ozbay K., Ozmen D., Berechman J.:Empirical analysis of the relationship between accessibility and economic development[J]. ASCE, J. Urban Planning and Develop.2003, 129(2),97-119
[68]Paez A, Uchida T, Miyamoto K, A general framework for estimation and inference of geographically weighted regression models:1, location-specific kernel bandwidths and a test for locational heterogeneity[J], Environ plan A,2002,34(4):733-755.
[69]RajkoR., Treatment of model error in calibration by robust and fuzzy procedures[J], Analytical Letters,1994,27:215-228.
[70]Rosenberg B. A survey of stochastic parameter regression[J]. Annals of Economic and Social Measurement,1973,1:381-397
[71]Rousseeuw P.J., Least median of squares regression[J], Journal of the American Statistical Association,1984,79,871-880.
[72]Rousseeuw P.J., Leroy A.M., Robust Regression and Outlier Detection[M], John Wiley & Sons Inc.,1987, New York
[73]Rousseeuw P.J., Multivariate estimation with high breakdown point[A], in:W. Grossmann, G. Pflug, I. Vinche (Eds.), Mathematical Statistics and Applications, vol. B, Reidel Dordrecht, 1985, pp.283-297.
[74]Rousseeuw P.J., Croux C., Alternatives to median absolute deviation[J], Journal of the American Statistical Association,1993,88:1273-1283.
[75]Sarbu C., Pop H.F., Fuzzy robust estimation of central location[J], Talanta,2001,54:128-130.
[76]Small C.G., A survey of multidimensional medians, International Statistical Review[J],1990,58: 263-277.
[77]Schuurmanl, N., Peters, P.A., Oliver, L.N., Are obesity and physical activity clustered? A spatial analysis linked to residential density[J]. Epidemiology,2009,17(12),2202-2209.
[78]Schwartz G. Estimating the dimension of a model[J]. The Annals of Statistics,1978,6:461-4
[79]Serneels S., Croux C., Filzmose P.r, Van Espen P.J., Partial Robust M-Regression[J], Chemometrics and Intelligent Laboratory Systems,2005,79:55-64.
[80]Shearmur, R., Apparicio, P., Lizion, P., Polese, M., Space, time, and local employment growth: an application of spatial regression analysis[J]. Growth and Change,2007,38(4),696-722.
[81]Spjotvoll E. Random coefficients regression models:a review[J]. Mathematische Operations for schungund Statistik,1977,8:69-93
[82]Swamy P A V B. Statistical Inference in Random Coefficient Regression Models[M]. Berlin: Springer,1971:4-21.
[83]Trig D W, Leach D H. Exponential smoothing with an adaptive response rate[J]. Operational Research Quarterly,1968,18(1):53-59.
[84]Wakeling I.N., H.J.H. Macfie H.J.H., A robust PLS procedure[J], Journal of Chemometrics,1992,4:189-198.
[85]Walczak B., Massart D.L., Robust principal components regression as a detection tool for outliers[J], Chemometrics and Intelligent Laboratory Systems,'1995,27:41-54.
[86]Waters, W.G.II.:Empirical Studies of Infrastructure Investment and Economic Activity: Evidence from Canada[M]. World Conference on Transport Research,2004, Istanbul
[87]Weber A., Uber den standort der industrien[M], Tubingen.1909 English translation by C. Friedrich Alfred Weber's Theory of Location of Industries, University of Chicago Press,1929
[88]蔡防,都阳,中国地区经济增长的趋同于差异——对西部开发战略的启示,经济研究,2000(10)：30-37
[89]蔡剑红,朱道林.多空间尺度普通住宅用地的合理地价研究[J].中国房地产,2012,2：38-42
[90]曹建军,刘永娟,李金莲.江苏省区域经济差异的多尺度研究[J].地域研究与开发,2010,29(5)：55-59
[91]曹宗龙,陈松林.基于GIS的经济与产业重心空间演变及动态分析——以福州市为例[J].亚热带水土保持,2011,23(2)：22-32
[92]陈小瑜.基于多尺度空间聚类的经济区域划分研究[J].重庆师范大学学报(自然科学版),2011,28(5)：81-92
[93]陈秀山,徐瑛.中国区域差距影响因素的实证研究[J].中国社会科学,2004,4(5)：119-129
[94]戴鞍钢,交通与经济的互为制约——以近代中国西部省份为例[J],中国延安干部学院学报,2010,2(14)：69-74
[95]董艳,于涛,李淑俊,区域经济与教育投入的空间计量分析[J],石家庄学院学报,2010,12(6)：91-94
[96]董先安,浅释中国地区收入差距：1952-2002,经济研究,2004(9)：48-59
[97]管卫华,林振山,顾朝林.中国区域经济发展差异及其原因的多尺度分析[J].经济研究,2006,7：117-125
[98]海成,李健,杨艳,中国公路交通与经济发展关系的实证研究[J],长安大学学报(社会科学版)2007,9(2)：8-13
[99]何志斌,等.荒漠植被植物种多样性对空间尺度的依赖[J].生态学报,2004,24(6)：1146-1149
[100]简美锋,万智恩.石家庄市人口重心与经济重心的演变轨迹对比研究[J].经济视角,2011,2：4-7
[101]姜磊,季民河,基于空间异质性的中国能源消费强度研究——资源禀赋、产业结构、技术进步和市场调节机制的视角[J],产业经济研究,2011,53：61-70
[102]兰国玉,朱华,曹敏.西双版纳热带雨林树种多样性的尺度效应[J].西北植物学报,2012,32(7)：1454-1458
[103]李蓉,李宇,西部地区交通区域划分问题的研究[J],华东交通大学学报,2006,23(2)：40-43
[104]李志,周生路,张红富,姚鑫,吴巍,基于GWR模型的南京市住宅地价影响因素及其边际价格作用研究[J],中国土地科学,2009,23(10)：20-25
[105]刘牧鑫,蒋伟,外商直接投资与区域经济增长：基于地理加权回归模型的研究[J],统计应用研究,2009,24(12),62-65
[106]刘旭华,王劲峰,孟斌.中国区域经济时空动态不平衡发展分析[J].地理研究,2004,23(4)：530-540
[107]卢二坡,黄炳艺,基于稳健MM估计的统计数据质量评估方法[J],统计研究,2010,27(12)：16-22
[108]卢布,等.不同尺度农业预测差异及其处理[J].中国农业资源与区划,2005,26(6)：54-56
[109]卢俊宇,等.基于时空尺度的中国省级区域能源消费碳排放公平性分析[J].自然资源学报,2012,27(12)：2006-2017
[110]吕萍,甄辉,基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J],经济地理,2010,30(3)：472-478
[111]龙莹,空间异质性与区域房地产价格波动的差异——基于地理加权回归的实证研究[J],中央财经大学学报,2010,11：80-85
[112]齐绍洲,罗威,中国地区经济增长与能源消费强度差异分析,经济研究,2007(7)：74-81
[113]齐绍洲,云波,李锴,中国经济增长与能源消费强度差异的收敛性及机理分析,经济研究,2009(4)：56-64
[114]齐艳红.不同空间尺度下城市土地利用绩效评价指标体系的构建[J].国土与自然资源研究,2012,4：30-31
[115]宋帮英,苏方林,我国省域碳排放量与经济发展的GWR实证研究[J],财经科学,2010,265：41-49
[116]苏方林,基于地理加权回归模型的县域经济发展的空间因素分析——以辽宁省县域为例[J],学术论坛,2005,172：81-84
[117]苏方林,省域R&D知识溢出的GWR实证分析[J],数量经济技术经济研究,2007,2：145-153
[118]孙孝文,和谐交通体系构建研究[D],武汉理工大学博士论文,2007
[119]汤庆园,徐伟,艾福利,基于地理加权回归的上海市房价空间分异及其影响因子研究[J],经济地理,2012,32(2)：52-58
[120]汤浒,交通与市场规模的关系研究[D],北京交通大学硕士论文,2009
[121]王彬,王宜强.改革开放以来福建省经济重心格局演变及其空间差异[J],地理研究,2011,30(10)：1882-1890
[122]王斌会,稳健主成分分析方法研究及其在经济管理中的应用[J],统计研究,2007,24(8)：72-76
[123]王成,蒋福霞,王利平.统筹城乡视域下农户土地利用意识多尺度认知研究[J].云南师范大学学报(哲学社会科学版),2012,44(2)：60-66
[124]王武科,李同升,刘笑明.不同尺度下农业创新技术空间扩散的实证研究——以中国果业协会果业技术扩散为例[J].人文地理,2009,1：76-80
[125]文嫮,金雪琴.基于不同区域尺度的中国信息制造业技术创新能力差异研究[J].社会科学辑刊,2010,5：103-108.
[126]魏后凯,刘楷.我国地区差异变动趋势分析与预测[J].中国工业经济,1994,4：28-36
[127]吴玉鸣,空间计量经济模型在省域研发与创新中的应用研究[J],数量经济技术经济研究,2006,5：74-85
[128]吴玉鸣,李建霞,省域经济增长与电力消费的局域空间计量经济分析[J],地理科学,2009,29(1)：30-35
[129]谢静,张阳生,雷昉,黄卓,经济转型期陕北公路交通与经济发展的关联分析[J],人文地理,2010(5)：103-107
[130]徐建华,等.中国区域经济差异的时空尺度分析[J].地理研究,2005,1：55-58
[131]许乃星,蒲之艳,张静晶,高智睿,公路交通与经济发展适应性评价研究[J],交通运输工程与信息学报,2011,9(3)：79-86
[132]徐现祥,李郇,中国城市经济增长的趋同分析,经济研究2004(5)：40-48
[133]杨开忠.中国区域经济差异变动研究[J].经济研究,1991,2：28-33
[134]杨世琦,杨正礼,高旺盛.不同尺度下区域农业系统协调度的评价[J].西北农林科技大学学报(自然科学版)2008,36(5)：64-72
[135]杨宇,刘毅,齐元静.基于不同区域尺度的中国经济发展阶段判断[J].经济问题探索,2012,12：1-6
[136]杨为民.地区收入差距实证研究[J].经济研究,1992,1：70-74.
[137]曾刚,林兰.不同空间尺度的技术扩散影响因子研究[J].科学学与科学技术管理,2006,2：22-26
[138]张晶,封志明,杨艳昭.现阶段中国不同尺度的粮食减产类型分析[J].资源科学,2006,28(6)：28-32
[139]张培峰.不同空间尺度的经济发展与城市化的相关分析[J].资源环境与发展,2007,3：22-26
[140]张耀军,任正委,基于地理加权回归的山区人口分布影响因素实证研究——以贵州省毕节地区为例[J],人口与资源环境,2012,36(4)：53-63
[141]周杰文.基于空间尺度的湖北省经济差异分析[J].商场现代化,2011,8：37-38
[142]周杰文.中部地区经济差异的多尺度分析[J].广西社会科学,2011,10：57-60
[143]周杰文,张璐.中部地区经济差异的空间尺度效应分析[J].地理与地理信息科学,2011,27(1)：49-52
[144]周文兴, 林新朗,中国住房价格与城市化水平的关系研究——动态面板和空间计量的实证分析[J],重庆大学学报(社会科学版),2012,18(5)：1-7
[145]周志龙,考虑区域经济特征的中国综合经济交通运输结果变化实证分析[D],北京交通大学硕士论文,2011
[146]周玉翠,齐清文,冯灿飞,近10年来中国省级经济差异动态变化特征,地理研究,2002(21)：781-790