本文選題：流通產(chǎn)業(yè) + 景氣預(yù)警　； 參考：《浙江工商大學(xué)》2017年碩士論文

【摘要】：在國(guó)民經(jīng)濟(jì)中,流通產(chǎn)業(yè)占據(jù)著戰(zhàn)略地位,它串聯(lián)著經(jīng)濟(jì)體中的多個(gè)行業(yè),一方面它為生產(chǎn)部門帶來了需求信息、提供轉(zhuǎn)移生產(chǎn)成果的去路,另一方面也為消費(fèi)部門供應(yīng)著商品與服務(wù)。隨著經(jīng)濟(jì)模式持續(xù)變更,流通產(chǎn)業(yè)也發(fā)生著諸多變化,本文正是基于景氣指數(shù)對(duì)其周期波動(dòng)特征進(jìn)行了探析,并構(gòu)建出預(yù)警模型估計(jì)其發(fā)展方向與幅度。首先,本文確認(rèn)了流通產(chǎn)業(yè)景氣分析的基準(zhǔn)指標(biāo)——流通產(chǎn)業(yè)增加值增速,并從流通產(chǎn)業(yè)的產(chǎn)業(yè)整體發(fā)展情況、產(chǎn)業(yè)自支持度、宏觀支持度三個(gè)方面構(gòu)建了我國(guó)流通產(chǎn)業(yè)景氣波動(dòng)監(jiān)測(cè)預(yù)警指標(biāo)體系。其次,針對(duì)基準(zhǔn)指標(biāo),本文通過EEMD時(shí)頻分解法將其分解成了七個(gè)固有頻率不同的分量,從而初步分辨出我國(guó)流通產(chǎn)業(yè)基準(zhǔn)周期的結(jié)構(gòu)及特征。結(jié)果顯示,(1)從整個(gè)研究期限來看,短期因素的沖擊對(duì)流通產(chǎn)業(yè)的影響較大,根據(jù)短期波動(dòng)的特征,可以將流通產(chǎn)業(yè)劃分為"低幅高頻-高幅低頻-低幅高頻"三個(gè)階段;(2)從臨近年份來看,中長(zhǎng)期因素的負(fù)面效應(yīng)是導(dǎo)致近期流通產(chǎn)業(yè)存在滑落傾向的主因。再次,在經(jīng)過數(shù)據(jù)預(yù)處理,取得指標(biāo)體系中各指標(biāo)的循環(huán)因子后,本文通過時(shí)差相關(guān)法、峰谷法與系統(tǒng)聚類法辨別出各指標(biāo)與基準(zhǔn)指標(biāo)循環(huán)因子相比的先行、一致與滯后性,其中,系統(tǒng)聚類法對(duì)指標(biāo)間距離的測(cè)量采用的切比雪夫距離,對(duì)類間距的測(cè)量采用的ward法;在此基礎(chǔ)上,本文分別對(duì)這三類指標(biāo)編制了景氣合成指數(shù),并用所有指標(biāo)計(jì)算出綜合景氣指數(shù)。結(jié)合景氣指數(shù)的走勢(shì)與EEMD信號(hào)分解結(jié)果可知:(1)在研究期限內(nèi),我國(guó)流通產(chǎn)業(yè)經(jīng)歷了五個(gè)周期,其中第一周期長(zhǎng)度最長(zhǎng),第二個(gè)周期震蕩最劇烈,后三個(gè)周期正位于金融危機(jī)過后的調(diào)整期,波動(dòng)相對(duì)平穩(wěn);(2)根據(jù)先行指數(shù)的走勢(shì),可以定性預(yù)計(jì)下一季度流通產(chǎn)業(yè)將加速下滑,并且這在很大程度上歸因于流通產(chǎn)業(yè)固定資產(chǎn)投資增速的下滑以及貨物運(yùn)輸行業(yè)的發(fā)展不濟(jì)。最后,本文通過"μ-σ法"將流通產(chǎn)業(yè)綜合景氣指數(shù)劃分為冷熱不同的五個(gè)警情等級(jí)。然后將綜合景氣指數(shù)作為輸出變量,經(jīng)時(shí)間平移后的先行與一致指標(biāo)作為輸入變量,基于適用于連續(xù)型變量回歸的BP-ANN、CART決策樹、CHAID決策樹與SVR建立了流通產(chǎn)業(yè)監(jiān)測(cè)預(yù)警模型,同時(shí)針對(duì)在小樣本問題中存在缺陷的前三類模型通過boosting算法提升其預(yù)測(cè)精度。經(jīng)過比較,本文選擇了訓(xùn)練和泛化能力均最為杰出的多項(xiàng)式核函數(shù)SVR模型對(duì)下一季度流通產(chǎn)業(yè)的景氣程度進(jìn)行了預(yù)測(cè)。結(jié)果表明,流通產(chǎn)業(yè)景氣水平確實(shí)將加速下滑,估計(jì)值為98.8634,根據(jù)警情劃分標(biāo)準(zhǔn),它已處在跌破"正常"線的邊緣。為此,本文提出了幾項(xiàng)建議:合理化財(cái)富分配以促進(jìn)消費(fèi);在工業(yè)化生產(chǎn)當(dāng)中融入新技術(shù)以滿足需求;創(chuàng)新商業(yè)模式、融合新舊市場(chǎng),以挖掘潛在需求;投資交通基礎(chǔ)設(shè)施,并促進(jìn)物流倉(cāng)儲(chǔ)信息化發(fā)展。
[Abstract]:In the national economy, the circulation industry occupies a strategic position. It is connected with many industries in the economy. On the one hand, it brings demand information to the production sector and provides a way to transfer production results. On the other hand, it also supplies goods and services to the consumer sector. With the continuous change of economic model, many changes have taken place in the circulation industry. This paper analyzes the characteristics of its cycle fluctuation based on the boom index, and constructs an early warning model to estimate its development direction and amplitude. First of all, this paper confirms the basic index of circulation industry boom analysis, that is, the increase of value added of circulation industry, and from the overall development of circulation industry, the degree of industry self-support. Three aspects of macro-support to build the circulation industry in China's economic fluctuations monitoring and early warning index system. Secondly, aiming at the benchmark index, this paper decomposes it into seven components with different natural frequencies by EEMD time-frequency decomposition method, and thus preliminarily distinguishes the structure and characteristics of the benchmark cycle of China's circulation industry. The results show that the impact of short-term factors on the circulation industry is significant from the point of view of the whole study period. According to the characteristics of short-term fluctuations, The circulation industry can be divided into three stages: low amplitude high amplitude low frequency low amplitude high frequency and low amplitude high frequency. The negative effect of medium and long term factors is the main reason for the decline of circulation industry in the near future. Thirdly, after the data preprocessing, the cyclic factors of each index in the index system are obtained, and through the time difference correlation method, the peak and valley method and the system clustering method, we identify the first, consistent and lag between each index and the benchmark index cycle factor. Among them, the Chebyshev distance and the ward method are used to measure the distance between the indexes by the systematic clustering method. Using all the indexes to calculate the comprehensive climate index. Combined with the trend of the boom index and the decomposition of the EEMD signal, we can see that within the study period, China's circulation industry experienced five cycles, the longest of which was the first cycle, and the most violent was the second cycle. The latter three cycles are now in the adjustment period after the financial crisis. The volatility is relatively stable. According to the trend of the leading index, we can qualitatively predict that the circulation industry will accelerate its decline in the next quarter. And this is largely due to the decline in fixed asset investment growth in the circulation industry and the poor development of the cargo transport industry. Finally, the comprehensive prosperity index of circulation industry is divided into five different alert grades by "渭-蟽 method". Then the Synthetical climate index is taken as the output variable and the leading and consistent index after time translation is taken as the input variable. Based on the BP-ANNNCART decision tree which is suitable for the continuous variable regression, the chaid decision tree and SVR are used to establish the monitoring and warning model of circulation industry. At the same time, the boosting algorithm is used to improve the prediction accuracy of the first three kinds of models with defects in the small sample problem. Through comparison, this paper chooses the polynomial kernel function SVR model, which is the most outstanding training and generalization ability, to predict the prosperity of circulation industry in the next quarter. The results show that the prosperity level of circulation industry will really accelerate down, the estimated value is 98.8634. According to the criterion of police situation classification, it is on the verge of falling below the "normal" line. To that end, this paper makes several recommendations: rationalizing the distribution of wealth to promote consumption; incorporating new technologies into industrial production to meet demand; innovating business models and integrating new and old markets to tap potential needs; and investing in transport infrastructure. And promote logistics warehousing information development.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F724

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