【摘要】：一般來(lái)說(shuō),第三產(chǎn)業(yè)增加值與GDP的比值,能夠衡量一個(gè)國(guó)家的發(fā)展水平和發(fā)達(dá)程度。據(jù)國(guó)家統(tǒng)計(jì)局統(tǒng)計(jì),2015年,我國(guó)國(guó)內(nèi)生產(chǎn)總值為682635.1億元。其中,第三產(chǎn)業(yè)增加值占GDP比重高達(dá)50.2%,標(biāo)志著中國(guó)經(jīng)濟(jì)正式邁入“服務(wù)化”時(shí)代,意味著中國(guó)經(jīng)濟(jì)由工業(yè)主導(dǎo)向第三產(chǎn)業(yè)主導(dǎo)加快轉(zhuǎn)變,成為新常態(tài)下中國(guó)經(jīng)濟(jì)增長(zhǎng)的新動(dòng)力和新一輪經(jīng)濟(jì)增長(zhǎng)的“火車(chē)頭”。山東省2015年總產(chǎn)值是63002.32億元,三次產(chǎn)業(yè)增加值分別為4979.08、29485.90、28537.35億元,占GDP的比例分別達(dá)到了7.9%、46.8%和45.3%,第二產(chǎn)業(yè)、第三產(chǎn)業(yè)都成了經(jīng)濟(jì)增長(zhǎng)的主要的動(dòng)力。作為重工業(yè)大省,經(jīng)濟(jì)發(fā)展依然面臨許多問(wèn)題,如市場(chǎng)主體的活力尚未充分釋放;產(chǎn)業(yè)結(jié)構(gòu)不夠合理,消費(fèi)增長(zhǎng)動(dòng)力不足。因此我們需要進(jìn)行產(chǎn)業(yè)結(jié)構(gòu)的調(diào)整,加快第三產(chǎn)業(yè)的發(fā)展來(lái)拉動(dòng)經(jīng)濟(jì)的增長(zhǎng),使第三產(chǎn)業(yè)成為山東省經(jīng)濟(jì)增長(zhǎng)的主動(dòng)力。本文主要對(duì)山東省第三產(chǎn)業(yè)增加值進(jìn)行時(shí)間序列建模以及分析第三產(chǎn)業(yè)對(duì)就業(yè)人數(shù)的影響。文章包括五章內(nèi)容,第一章介紹了第三產(chǎn)業(yè)的研究背景及意義,對(duì)現(xiàn)有的關(guān)于第三產(chǎn)業(yè)的研究結(jié)果進(jìn)行了綜述;第二章對(duì)平穩(wěn)時(shí)間序列模型及其建模步驟進(jìn)行了介紹,并對(duì)矢量自回歸模型(VAR模型)的一些概念及模型的估計(jì)方法、模型滯后期的選擇問(wèn)題、模型平穩(wěn)性檢驗(yàn)的原則作了簡(jiǎn)單說(shuō)明;第三章選取了自1978年-2014年山東省第三產(chǎn)業(yè)增加值的數(shù)據(jù),利用Eviews8.0軟件進(jìn)行數(shù)據(jù)分析,對(duì)數(shù)據(jù)預(yù)處理之后得到平穩(wěn)性序列。利用AIC準(zhǔn)則進(jìn)行模型定階,建立ARIMA模型。進(jìn)而對(duì)模型做適應(yīng)性檢驗(yàn)和平穩(wěn)性檢驗(yàn),表明我們的模型擬合效果不錯(cuò)。最后利用建立的模型對(duì)2015和2016年的第三產(chǎn)業(yè)增加值進(jìn)行預(yù)測(cè);第四章建立VAR模型分析第三產(chǎn)業(yè)對(duì)就業(yè)的影響,并依據(jù)分析結(jié)果給出相應(yīng)的建議。第五章對(duì)論文進(jìn)行綜述,并依據(jù)分析結(jié)果對(duì)山東省第三產(chǎn)業(yè)發(fā)展提出意見(jiàn)和建議。
[Abstract]:Generally speaking, the ratio of added value of tertiary industry and GDP can measure the level of development and development of a country. According to the National Bureau of Statistics, China's GDP in 2015 was 68.26351 trillion yuan. Among them, the proportion of the added value of the tertiary industry to GDP is as high as 50.2%, which indicates that China's economy has formally entered the era of "service economy", which means that China's economy has been transformed from industrial leading to tertiary industry leading. To become the new power of China's economic growth and a new round of economic growth "locomotive" under the new normal. The total output value of Shandong Province in 2015 is 6.300232 trillion yuan, and the added value of three industries is 4979.080.29485.90,2.853735 trillion yuan respectively, which accounts for 7.9% 46.8% and 45.3% of GDP, respectively. The secondary industry and the tertiary industry have become the main driving force of economic growth. As a big province of heavy industry, economic development still faces many problems, such as the vitality of the main body of the market has not been fully released, the industrial structure is not reasonable, and the power of consumption growth is insufficient. Therefore, we need to adjust the industrial structure, accelerate the development of the tertiary industry to stimulate economic growth, so that the tertiary industry becomes the main driving force of economic growth in Shandong Province. This paper models the time series of the added value of the tertiary industry in Shandong Province and analyzes the influence of the tertiary industry on the number of employed people. The first chapter introduces the research background and significance of the tertiary industry, summarizes the existing research results on the tertiary industry, and introduces the stationary time series model and its modeling steps in the second chapter. Some concepts of the vector autoregressive model (VAR model), the estimation methods of the model, the problem of model lag selection, and the principle of model stationarity test are briefly explained. The third chapter selects the data of the added value of the tertiary industry in Shandong Province from 1978 to 2014, and uses Eviews8.0 software to analyze the data and get the stationary sequence after the data preprocessing. AIC criterion is used to determine the order of the model and the ARIMA model is established. Then the model is tested for adaptability and smoothness, which shows that our model fits well. At last, we use the established model to predict the added value of the tertiary industry in 2015 and 2016. Chapter four establishes the VAR model to analyze the influence of the tertiary industry on the employment, and gives the corresponding suggestions according to the analysis results. The fifth chapter summarizes the paper and puts forward some suggestions on the development of the tertiary industry in Shandong Province according to the analysis results.
【學(xué)位授予單位】：曲阜師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F127

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