本文關(guān)鍵詞：基于Copula函數(shù)的CPI相關(guān)性分析及模糊預(yù)測(cè)　出處：《寧夏大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： Copula函數(shù) 相關(guān)性度量 模糊回歸 CPI 組合預(yù)測(cè)

【摘要】：CPI是重要的經(jīng)濟(jì)指標(biāo),反映著居民消費(fèi)水平的變動(dòng)情況.國家的宏觀經(jīng)濟(jì)調(diào)控政策的出臺(tái)力度將受到CPI大小的直接影響.PPI是衡量企業(yè)生產(chǎn)成本變化的指標(biāo),也是經(jīng)濟(jì)核算的重要依據(jù).對(duì)這兩個(gè)指標(biāo)的定量分析是當(dāng)今統(tǒng)計(jì)學(xué)研究的熱門問題之一,本文一方面基于Copula函數(shù)研究CPI與PPI間的相關(guān)性度量,這有利于分析經(jīng)濟(jì)過熱或緊縮的發(fā)展趨勢(shì).另一方面,我們提出了一類模糊回歸組合預(yù)測(cè)模型,并將其運(yùn)用于CPI的短期預(yù)測(cè)研究,為制定經(jīng)濟(jì)政策提供可靠的依據(jù)和建議,從而減少因?yàn)檎叩臅r(shí)滯性帶來的影響.一類經(jīng)濟(jì)數(shù)據(jù)呈現(xiàn)出顯著的非線性關(guān)系,我們常采用的Pearson相關(guān)系數(shù)并不能夠正確、客觀地反映非線性性.Copula作為刻畫變量間相關(guān)關(guān)系的工具,在非線性情況下具有顯著的優(yōu)勢(shì).它可以靈活、全面、精細(xì)地刻畫出變量間復(fù)雜的相依性結(jié)構(gòu).本文考慮到CPI與PPI的本質(zhì)非線性相關(guān),基于表現(xiàn)能力更強(qiáng)的Copula來研究CPI與PPI之間的相依性.采用2011年1月至2016年12月全國月度同比CPI與PPI數(shù)據(jù)進(jìn)行相關(guān)性研究,結(jié)果表明Gaussian Copula在刻畫秩相關(guān)性上效果較好,而在尾部相關(guān)性以及與原始數(shù)據(jù)的擬合程度上,GumbelCopula要優(yōu)于其他Copula.通常由于輸入或輸出數(shù)據(jù)的不精確造成了經(jīng)典回歸模型的局限性,模糊回歸利用模糊集理論在刻畫不精確性的優(yōu)勢(shì),有效的解決了這一問題.在預(yù)測(cè)中,單純地使用一種模型難以全面揭示其變化規(guī)律.組合預(yù)測(cè)理論能有效利用單一模型的有用信息提高預(yù)測(cè)準(zhǔn)確度.本文提出了一種模糊回歸組合預(yù)測(cè)方法,并運(yùn)用到我國CPI的預(yù)測(cè)中.該方法能綜合模糊回歸和組合預(yù)測(cè)的優(yōu)點(diǎn),給出更符合實(shí)際的CPI預(yù)測(cè)區(qū)間值.最后,CPI預(yù)測(cè)結(jié)果表明,模糊回歸組合模型的預(yù)測(cè)結(jié)果優(yōu)于各單項(xiàng)預(yù)測(cè)模型,能提高CPI的預(yù)測(cè)精度,適合對(duì)CPI進(jìn)行短期預(yù)測(cè).
[Abstract]:CPI is an important economic indicator. Reflecting the change of consumption level of residents. The national macroeconomic regulation and control policy will be directly affected by the size of CPI. PPI is an index to measure the change of enterprise production cost. The quantitative analysis of these two indexes is one of the hot issues in the statistical research. On the one hand, based on the Copula function, this paper studies the correlation measure between CPI and PPI, which is helpful to analyze the development trend of economic overheating or contraction. On the other hand. We put forward a kind of fuzzy regression combination forecasting model and apply it to the short-term forecast research of CPI, which provides reliable basis and suggestion for making economic policy. In order to reduce the impact of policy delay, a class of economic data show a significant nonlinear relationship, we often use the Pearson correlation coefficient can not be correct. As a tool to describe the correlation between variables, the objective reflection of nonlinearity. Copula has significant advantages in nonlinear cases. It can be flexible and comprehensive. The complex structure of dependence between variables is described in detail. In this paper, the essential nonlinear correlation between CPI and PPI is considered. The dependency between CPI and PPI was studied based on the more expressive Copula. The correlation between CPI and PPI data from January 2011 to December 2016 was used to study the correlation between CPI and PPI. Research. The results show that Gaussian Copula is effective in describing rank correlation, but in tail correlation and fitting degree with original data. GumbelCopula is superior to other Copula. The limitations of classical regression models are usually due to inaccuracy of input or output data. Fuzzy regression utilizes the advantage of fuzzy set theory to depict imprecision and solve this problem effectively. It is difficult to reveal the law of change by using a single model alone. The combination prediction theory can effectively use the useful information of a single model to improve the prediction accuracy. In this paper, a fuzzy regression combined prediction method is proposed. This method can integrate the advantages of fuzzy regression and combination prediction, and give the actual CPI prediction interval value. Finally, the results show that the method can be used to predict CPI. The prediction result of fuzzy regression combination model is better than that of each single prediction model, which can improve the prediction accuracy of CPI, and is suitable for short-term prediction of CPI.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F124

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