本文選題：失業(yè)率預(yù)測(cè) + 大數(shù)據(jù)　； 參考：《系統(tǒng)科學(xué)與數(shù)學(xué)》2017年02期

【摘要】：目前,中國(guó)失業(yè)率統(tǒng)計(jì)存在一定局限,不利于準(zhǔn)確及時(shí)地反映勞動(dòng)市場(chǎng)的就業(yè)變動(dòng),大數(shù)據(jù)技術(shù)的快速發(fā)展為中國(guó)失業(yè)率統(tǒng)計(jì)提供新的發(fā)展視角.基于網(wǎng)絡(luò)搜索數(shù)據(jù),文章從5種常用的預(yù)測(cè)方法中篩選出最優(yōu)的支持向量機(jī)回歸模型,對(duì)中國(guó)季度失業(yè)率進(jìn)行了預(yù)測(cè)研究.研究表明,基于網(wǎng)絡(luò)搜索數(shù)據(jù)預(yù)測(cè)的失業(yè)率能夠比官方數(shù)據(jù)更早地反映失業(yè)趨勢(shì)的變化,預(yù)測(cè)失業(yè)率與修正后的失業(yè)率水平接近,能夠?yàn)檎块T提供中國(guó)失業(yè)狀況的政策預(yù)警.
[Abstract]:At present, there are some limitations in the statistics of China's unemployment rate, which is not conducive to accurately and timely reflecting the employment changes in the labor market. The rapid development of big data's technology provides a new perspective for the development of China's unemployment rate statistics. Based on the network search data, this paper selects the optimal support vector machine regression model from five commonly used forecasting methods, and makes a prediction study on the quarterly unemployment rate in China. The study shows that the unemployment rate predicted on the basis of web search data is able to reflect changes in unemployment trends earlier than the official data, and that the predicted unemployment rate is close to the revised unemployment rate. To provide government departments with a policy warning of unemployment in China.
【作者單位】： 東北財(cái)經(jīng)大學(xué)統(tǒng)計(jì)學(xué)院;東北財(cái)經(jīng)大學(xué)博士后科研流動(dòng)站;
【基金】：國(guó)家社科基金重大項(xiàng)目(2015YZD08);國(guó)家社科基金項(xiàng)目(14CRK019) 國(guó)家自然科學(xué)基金項(xiàng)目(71573034) 遼寧省教育廳項(xiàng)目(LN2016JD020) 中國(guó)博士后科學(xué)基金(2016M601318)資助課題
【分類號(hào)】：F249.2

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