Study Based On Matching The GDP Data Quality Assessment

GDP as one of the aggregate indicators of the core content of national accounts, the quality of its data has received widespread attention. This paper studies the quality of GDP data from the perspective of data matching, constructs a system of evaluation methods of GDP data quality based on matching, and does an empirical analysis for GDP data quality, which is conducive to all-round understanding of the quality of GDP data and to have a more effective presentation of data quality control methods.From the generalized connotation of the GDP data quality, this article combines the matching of the GDP data with the GDP data quality connotation, and summarizes the specific content based on matching, which are logic matching, method matching and the significance of economic matching. Dividing according to the data type and using the basic assumptions of the corresponding data type, this paper converts the quality of the GDP data, based on matching, into structure, time and space of three-dimensional data quality assessment, and then selects the adaptive statistical models to construct the GDP data quality assessment methodology based on matching.GDP, as an important indicator for the measurement of output, has an approximate proportional relationship with many variables in the economic system, namely structural relationship which is mainly reflected in three aspects-the input-output structure, the structure of the physical quantity, and the internal structure of the economic production. Through using the adaptive models to verify these three aspects and assessing China’s GDP data quality, evaluation results show that China’s GDP data from1981to2010has larger defects in logic matching; its methods are not very sound; GDP data is relatively stable in the economic structure matching. Overall, the GDP data quality is not ideal in structure dimension matching; there is greater room for improvement.GDP data in the time dimension has a significant phase characteristics which are reflected in the correlation of the economic operation and within GDP data-namely GDP and GDP growth. Based on the basic assumptions of asymptotically stability in GDP time series, correlation in the time dimension between the absolute and relative GDP data and so on, this article constructs the deterministic and stochastic time series models for GDP and GDP growth, then uses the error rate of these models to assess the quality of the GDP data in the time dimension. From the empirical analysis results, GDP data is basically matching in the time dimension; corresponding to each of the contents in the time dimension, GDP matches highest in the economic significance, the logic matching followed, but both are above80%, and the method matching is low, at53.33%. From doubtful year points in the time dimension, they mainly embody in the years before1991; with different methods, concerning GDP data, there exist basically suspicious characteristics in the year1990,1991and1992and around.There is a growing gap between the total GDP of different areas and the national GDP in recent years; based on this, from the spatial perspective while analysing of the quality of China’s GDP data, the total amount of GDP data of all provinces cannot be used to explain the quality level of the national GDP data. When building GDP data quality assessment based on space matching, it can depend on three basic assumptions-the low level of distortion of structured data in various regions, the strong association between structured data of regional summary and national structural data, the reflection in the different segments and the combined effects of the regional structural data, at the meantime, uses the canonical correlation to verify these assumptions. This paper evaluates the China’s GDP data from the spatial dimension by constructing the systematic vector autoregressive models and the distributed lag regression model and using the error rate and the comprehensive evaluation methods. The empirical results show that GDP data matching in the spatial dimension is poorer; corresponding to each of the contents, in the spatial dimension GDP matches highest in the logic matching, about76.08%; the economic significance matching followed, only45.16%; the sound methods matching is the lowest, only29%. From doubtful year points in the spatial dimension, they mainly embody in the years between1991and1999; with different methods, concerning GDP data, there exist basically suspicious characteristics in the year1990,1991and1992and around.Based on the structure, time and space the three dimensions of GDP data quality assessment, meanwhile, this paper determined the GDP data quality evaluation results based on the matching from two aspects. On the one hand it takes error rate of the evaluation in the foregoing sections as the foundation, and then uses the analytical hierarchy process (ahp) to identify weight, and the comprehensive evaluation method for the comprehensive error rate of each year which is took as the results of GDP data quality evaluation. On the other hand, to refine GDP data quality high or low comparison, this article uses data transform, taking index calculation to error rate of the GDP data quality based on matching, and we get the result of indexation of GDP data quality evaluation to compare GDP data quality high or low in different years. The analysis of the results of comprehensive evaluation from four aspects are analyzed including overall characteristics, phasic characteristics, the relevance of relevant dimensions, the influence for the comprehensive value of GDP from each dimension. Through the investigation for overall characteristics, it finds that China’s GDP data quality is7.5points or so at an average level in various dimensions and general and gets the lowest point in the structure dimension, and at the same time it varies largely among the dimensions, especially in the structural dimension with the biggest variation. From the feature of stage, although China’s GDP data quality has the characteristics of white noise, it still exists obvious gradual characteristics; this gradual characteristic and national economic accounting reform processes are closely linked. From the analysis of the correlation among the dimensions, China’s GDP data quality is with a low correlation in each dimension, and this indicates that when using models to evaluate China’s GDP data quality, it has a low correlation among dimensions, so it is able to fully consider the influence from each dimension to the overall data quality. From the influence for the comprehensive evaluation of GDP from each dimension, various dimensions have a dynamic characteristic of comprehensive evaluation quality of the GDP data, and the dynamic characteristic enjoys a strong difference before1984, and becomes flat gradually after1987.For GDP data quality problems, this paper analyzes the causes, and proposes appropriate control systems and countermeasures. When analyzing the reasons for which China’s GDP data is not of high quality, this article thinks it should come down to three main causes including institutional deficiency, technical barriers, and social complex changes since1978. For these reasons, improving GDP data quality should be from the GDP data running link, that is, GDP data production, dissemination and evaluation of three main links to establish data quality control system. To make the quality control system for the GDP data run effectively, we need to seize the training for persons with the data management ability, construct the efficient national statistical information network system, and establish the GDP data quality organization system.