本文選題：PMI指數(shù) + 定量數(shù)據(jù)　； 參考：《浙江工商大學(xué)》2017年碩士論文

【摘要】：國際金融危機過后,雖然全球經(jīng)濟逐步回升,但是危機深層次的影響依然存在,再加上政治等非經(jīng)濟因素的影響逐步加深,使得我國的發(fā)展面臨了許多的不確定性和挑戰(zhàn)性。為了科學(xué)、及時的監(jiān)測經(jīng)濟發(fā)展狀態(tài),我國構(gòu)建了采購經(jīng)理——PMI指數(shù)體系,采用非定量的問卷調(diào)查數(shù)據(jù),于每月月初計算并對外公布上一個月的PMI指數(shù)數(shù)值,來綜合的反映宏觀經(jīng)濟發(fā)展態(tài)勢。在此背景下,本文基于前人的研究基礎(chǔ),采用PMI(-1)(滯后一期PMI指數(shù)值)、超額準備金率、法定準備金率、進口金額、出口金額、平均匯率、流通于銀行體系以外的現(xiàn)鈔M0等36個客觀定量數(shù)據(jù)作為PMI指數(shù)體系的可能影響變量,同時,采用Li and Racine(2004)提出了混合數(shù)據(jù)下變量剔除的非參數(shù)方法,來確定與PMI指數(shù)存在相關(guān)關(guān)系的變量,復(fù)制出PMI指數(shù)的決定變量,并進一步,構(gòu)建半?yún)?shù)時變系數(shù)的完全模型、路徑模型,對所篩選出的變量進行了聯(lián)合效應(yīng)、個體效應(yīng)的分析,最后,將模型推廣為預(yù)測模型,對PMI指數(shù)進行了預(yù)測,從而將PMI指數(shù)由描述性統(tǒng)計指數(shù)推向推斷性統(tǒng)計指數(shù),填補了已有文獻在這方面的空白。具體所做工作和得到的結(jié)論如下:首先,通過混合數(shù)據(jù)下變量剔除的非參方法進行變量的相關(guān)性和線性性的選擇,模擬出PMI指數(shù)的決定變量,發(fā)現(xiàn)與PMI指數(shù)存在線性關(guān)系的變量為:PMI(-1)、出口金額(CE)、工業(yè)增加值(GZ);存在非線性關(guān)系的變量為:股票成交金額(GE)、公共財政收入(GR)、公共財政收支差額(GGCE)、稅收收入(SR)、平均匯率(PL)、活期存款利率(HL)。其次,通過半?yún)?shù)時變系數(shù)的完全模型和路徑模型的實證分析,發(fā)現(xiàn)加入6個非線性變量后,它們的聯(lián)合作用會使得模型對于PMI指數(shù)有更加明顯的解釋能力。同時,發(fā)現(xiàn)各個非線性變量對PMI指數(shù)的影響各不相同,對PMI指數(shù)的擬合存在正向影響的非線性變量為:GE、GR、GGCE,沒有存在負向影響的變量,其中SR、PL、HL對PMI指數(shù)擬合結(jié)果的正負向影響不明顯。最后,本文構(gòu)建了變異系數(shù),通過比較,發(fā)現(xiàn)各個非線性變量對PMI指數(shù)波動影響大小依次是:GGCE、GE、HL、PL、GR、SR(剔除后)。最后,通過半?yún)?shù)時變系數(shù)的預(yù)測模型的實證分析,不僅說明了非參變量選擇所篩選出的變量在未來經(jīng)濟運行中依然能夠解釋PMI指數(shù),而且,提供了一個整體預(yù)測效果較好的預(yù)測模型,為企業(yè)、金融機構(gòu)和政府等提供了判斷經(jīng)濟形勢、制定發(fā)展計劃的有力依據(jù)。
[Abstract]:After the international financial crisis, although the global economy is rising gradually, the deep influence of the crisis still exists, and the influence of non-economic factors such as politics is deepening gradually, which makes the development of our country face a lot of uncertainty and challenge. In order to monitor the state of economic development in a scientific and timely manner, China has constructed a purchasing manager PMI index system, which uses non-quantitative questionnaire data to calculate and publish the PMI index value of the previous month at the beginning of each month. To reflect the macroeconomic development situation. In this context, based on the previous research basis, this paper adopts PMI-1N (PMI-1U), the excess reserve ratio, the legal reserve ratio, the import amount, the export amount, the average exchange rate. 36 objective quantitative data, such as cash M0, which are circulating outside the banking system, are regarded as possible influential variables in the PMI-index system. At the same time, a non-parametric method for the elimination of variables under mixed data is proposed by using Li and Racine 2004). To determine the variables related to PMI index, duplicate the determinant variables of PMI index, and further, construct the complete model of semi-parametric time-varying coefficient, path model, and carry on the joint effect to the selected variables. Finally, the model is extended to predict the PMI index, thus the PMI index is pushed from descriptive statistical index to inferential statistical index, which fills the gap in the previous literature. The specific work and conclusions are as follows: firstly, the determinant variables of PMI index are simulated by selecting the correlation and linearity of variables by the non-parametric method of variable elimination under mixed data. It was found that the variables with linear relationship with PMI index were: PMI-1 / 1, export / export value / value added / industrial / industrial value added / GZN, and the nonlinear relationships were as follows: stock transaction value / stock turnover, public finance revenue / expenditure / GGCEC / GGCEA, tax revenue / tax / tax revenue / expenditure balance The exchange rate is high, and the demand deposit rate is high. Secondly, through the empirical analysis of complete model and path model of semi-parametric time-varying coefficient, it is found that the combined action of six nonlinear variables will make the model have a more obvious ability to explain PMI index. At the same time, it is found that the influence of each nonlinear variable on PMI index is different. The nonlinear variable with positive influence on PMI index fitting is the one with no negative effect. The positive and negative effects of SRL PL HL on PMI index fitting results are not obvious. Finally, the coefficient of variation is constructed, and by comparison, it is found that the influence of each nonlinear variable on the fluctuation of PMI index is in turn: 1. Finally, through the empirical analysis of the semi-parametric time-varying coefficient prediction model, it not only shows that the variables selected by the non-parametric variables can still explain the PMI index in the future economic operation, but also, A better forecasting model is provided, which provides a powerful basis for enterprises, financial institutions and governments to judge the economic situation and formulate development plans.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F224;F124

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本文編號：2008190