今天论文代写机构Fanessay小编整理了一篇economic Essay代写范文--Spatial statistics of regional economic differences，本篇文章阐述的内容关于区域经济差异的空间统计。对于区域经济差异的空间统计对区域发展有重要的作用，在现阶段，是需要采用多项经济指标来进行分析的，包括了因子与频率分析等，只有这样综合分析后，才能更加了解区域经济的实际情况，然后按照对结构属性去进行评估，从而确定有效的管理与发展的战略。
Regional economic differences of spatial statistics has important role to the regional development, at the application stage, needs to be integrated by a number of economic indicators to analyze it, including: index factor and frequency analysis and spatial autocorrelation analysis, etc., only after a comprehensive analysis was carried out on the regional economy, to understand the regional economic situation, evaluate the structure properties and in accordance with the requirements, and determine the effective management and development strategy. From the perspective of large regional distribution, China's coastal regional economy is relatively developed, while the northwest inland region's economic development is relatively backward. Based on the measurement index, spatial statistics should be done well to meet the requirements of regional management. In this study, the spatial characteristics of regional economic differences were analyzed on the basis of the measurement standard of regional economic strength.
Regional economic differences are the core of regional economic research. With the deepening of research, regional differences have an important impact on the overall development, and the original single index evaluation system needs to be changed to develop into multiple indexes. In view of the special change of research form, it is necessary to realize the analysis and transformation of space from simple measurement model. Regional economy is a new foothold and breakthrough for China's economic development, and also an important part of the urban-rural integration process. In the development process, it needs to be set and analyzed according to the specific requirements of the form of overall planning. Based on the special requirements of regional economic development, it is necessary to have a deeper understanding of the nature of regional differences and realize effective regulation and management of regional economy of provincial and central governments.
From the perspective of the level of economic development, economic structure, economic development speed and economic benefits will affect the development of spatial regions. The specific requirements of fiscal revenue, GDP density and total retail sales of consumer goods for urban and rural residents should be analyzed according to industrial proportion and outward form of economic development. Effective data such as per capita industrial value and industrial added value are the key to measure economic indicators. It is necessary to improve the credibility of the data, ensure the integrity of the data, and then realize the effective use of the data.
Factor analysis refers to the integration of the original multiple indicators into one or several indicators, and takes them as the key information to reflect the index construction. According to the existing data, in the process of data factor analysis, it is necessary to properly analyze variables, and do a good job in feature analysis according to the specific requirements of factors. The analysis showed that the KMO value between 15 indicators was 0.742, indicating that there was a high correlation between the variables, which was suitable for factor analysis. Four common factor for characteristic root is greater than 1, the cumulative variance contribution as follows: 85.444%, shows that the four factors include most of the information, its changes can represent the basic 15 the change of the original variables. By using fourth power rotation of factor loading system is analyzed, according to the indicators suggest that the differences between different load size, the main factors and factors together. According to the regression method, the comprehensive situation of regional economic development can be analyzed, and the regional economic strength score can be obtained considering the requirement of frequency characteristics. If the frequency distribution belongs to the normal range, the proportion of the regions with low scores is larger. As regional development presents obvious differences in different principal factors, economic development indicators can be evaluated on the basis of the characterization of principal factors, and statistical and evaluation can be conducted according to the specific requirements of differences.
Spatial autocorrelation itself is a spatial statistical method, which refers to the correlation of the same observation in different Spaces. Due to the influence of geographical distribution factors on the whole, there will be an obvious trend of continuity. Taking into account the specific requirements of spatial statistics, the index should be evaluated from the measurement space and analyzed according to the requirements of global response coefficient. In order to analyze the attribute relationship, it is necessary to set the local index on the basis of the local index. Global indicators play an important role in verifying the spatial patterns of different regions. Local indicators are applied to the spatial patterns of the whole study region. Local indicators are the values related to a geographical phenomenon or a geographical phenomenon attribute value of a regional unit. At present, many scholars pay more attention to global indicators, mainly Moran's global index and LocalMoran's local index:
According to the specific requirements of the value range, the coefficients should be evaluated in the process of setting different intervals. If there is a negative correlation between regional coefficients, the coefficient range is less than 0, and it should be specific to the target region of economic development. If the economic development level is above the spatial location, the similarity attribute value is higher.
Moran's represents the weight matrix of space, which is set by the proximity standard or distance value, and needs to be calculated according to the specific requirements of the proximity standard value. In addition, for the weight matrix of distance space, the spatial weight matrix of distance is assumed to be the interaction of space, depending on the distance between regions. It can be innovatively designed according to the distance index. K value represents adjacent matrix. In general, the simple matrix is unbalanced due to the general threshold distance. If the area is larger, the adjacent coefficient is less. The space econometric model adopted in practice is space lag model and space error model, which is affected by different factors and directly affects the estimation and experience of the model. As the standard econometric technique has a narrow range of application and low feasibility, it can be evaluated by the spatial two-segment least square method. The fitting degree of the model has a great influence on the linear model.
LNGDP is the logarithmic form of regional GDP, which is interpreted as a variable. Coastal regions have a relatively small population, a relatively developed economy, and a relatively high per capita GDP. Is affected by the economic development level and space form, to explain the model if the per capita indicators, negative influence is big, can adopt total indicators, with GDP as explanatory variables, specific include: the economic structure, industrial structure, between the two, of the form of the substitution model could be used in the analysis phase to evaluate it, by comparing the effect of the model and significant, in the final stages to analyze the selected SGDP index. According to the specific requirements of existing ownership structure indicators, the proportion is measured by the added value of the economy of non-public ownership and evaluated according to the requirements of economic structure variables. Because the structure index is special, the proportion is measured by the added value of non-public economy, which can meet the basic requirements of model setting. In the process of urbanization development, based on the existing model construction, the impact of investment on the economy should be re-examined and expressed with the ratio of fixed asset investment to GDP. In addition, under the influence of government factors, it is generally believed that government consumption expenditure has a negative impact on economic growth, while government investment expenditure has a positive impact on economic growth. Therefore, it is necessary to compare the selected data and play the role of the system in economic development.
Based on the specific requirements of regional space development, it is necessary to analyze the difference proportion in the subsequent development stage and rationalize its application according to the statistics and key points. The characteristics of regional economic spatial differences will be analyzed as follows.
In order to eliminate the influence of adverse factors, the data should be standardized in advance. Factor analysis of all economic indicators should be carried out according to SPSS17.0, and principal components should be selected in combination with the analysis method. Through effective analysis, it can be seen that variable differences exist in different project indicators, indicating that different variable intervals have high correlation. The statistician will evaluate the standard value given. If the KMO value is greater than 0.8, factor analysis can be performed. According to bartlett test value requirement, in order to avoid setting unreasonable phenomenon, we must reject the hypothesis and conduct effective analysis on the original variables. If necessary, three main factors should be proposed and the other differences should be compared. If the extracted principal factors are appropriate, the load matrix can be obtained by using the maximum variance method. One is the GDP judgment. For the specific requirements of non-agricultural output value, in the subsequent stage of setting, the GDP and per capita income are combined and the spatial statistical value is assumed according to the requirements of the main factors of economic strength. Second is the value of industrial production, which itself belongs to the added value, is the main factor of economic vitality.
The factor score was calculated according to the existing regression method, which was saved as a new variable in the data file, and the standard was set for the variance of each principal factor after rotation. Taking into account the data changes of principal factor score and weight sum, regional economic development indicators should be measured in advance. If the index is relatively high, regional economic development level is relatively high; otherwise, it is relatively low. From the perspective of current regional development level, western regions are the main regions with relatively low level of economic development, mainly influenced by factors such as science and technology and geographical location. Considering regional transportation system and the key and difficult points of development work, it is necessary to pay attention to relatively independent regional development and do a good job in statistics.
According to the specific requirements of regional economic space development form, the variable information needs to be analyzed timely in the subsequent practice process, and it should be analyzed according to the requirements of measurement management and statistical design. The measurement indexes of regional economic space will be analyzed as follows.
Explanatory variables are mainly fiscal revenue, which refers to the income obtained from the participation of national finance in the distribution of social products, and is the key to the realization of relevant functions of the country. This indicator can be used to measure the government's role in economic development. Taking the X6 representative value as an example, it is necessary to compare the fiscal revenue of county-level administrative units and the impact of formal fiscal revenue on the economy.
Under the condition of market economy, the government provides public goods and services for social development. In order to meet the common needs of the society, attention should be paid to the form of financial funds, and specific management should be done to meet the overall requirements of the financial system. Theoretically, there are certain requirements for stable economic growth, and the expenditure values of different administrative units need to be highlighted, indicating the impact of fiscal expenditure on economic development.
Investment is a direct factor of economic development. Urban fixed asset investment, as an important part, can be indirectly analyzed through influencing investment.
In view of the particularity of the setting of administrative units, residents' storage directly affects the balance of savings and investment. In the selected stage, it is necessary to analyze the storage level, find alternative variables, and take them as an important indicator of economic development.
The structure is represented by the proportion of different industrial sectors in different regions, mainly by mutual influence, or their integration. In the process of regional development, there is internal connection between industrial structure and overall form, and structural upgrading and optimization play an important role in promoting regional economic development, which can be taken as a substitute variable of industrial structure on the basis of non-agricultural proportion.
Regional economic development is a process in which multiple factors work together. In view of the particularity of regional development, it is necessary to compare different forms. The spatial econometric model can be used to compare regional economic development based on the theoretical and practical demonstration at home and abroad. Based on the specific requirements of the analysis form, the spatial statistical analysis index can be used to evaluate it. Taking the spatial statistical analysis value as an example, the Moran index can be used as the test standard to analyze whether there is any correlation. When necessary, the spatial econometric model can be established to evaluate and test the spatial econometric model.
Moran can effectively evaluate regional economic development level and various coefficients, and can choose spatial weight coefficient to compare adjacent coefficient. Considering the particularity of the inspection area, it needs to be evaluated in the global form according to the requirement of spatial correlation test index. Since spatial development has spatial autocorrelation, economic development level shows a tendency of accumulation in similar regional space, and economic development level is adjacent to other economic development areas. Therefore, the existing indicators are fundamentally different. If the statistical data is analyzed, it can be compared according to the error and model requirements. There are many commonly used test criteria, including natural logarithmic function value, likelihood ratio, information criterion, etc., the higher the value, the lower the ratio.
The economic development level in eastern China is relatively high, and the spatial agglomeration distribution pattern is prominent. The sustained development of zhejiang and jiangsu is relatively fast, and Shanghai has a strong economic and technological link. Guangdong has relatively strong economic strength, but its neighboring regions have relatively low development speed and large economic contrast. Considering the special requirements of the opening-up policy, statistical work should be done according to the strength of economic ties.
In view of the particularity of the index of regional economic difference, it is necessary to do the spatial measurement statistics well on the basis of the index system in the process of regional development. Considering the special requirements of regional economic development, it is necessary to discuss the spatial effect and evaluate the model according to the spatial effect data display effect. The existence of spatial effect leads to the correlation of errors in the model, which does not meet the basic assumptions. Therefore, after the spatial autocorrelation factors of the error term are systematically considered in the model, it is possible to correctly estimate the geographical "spillover effect" in the process of regional economic growth by using these improved spatial models.