CAJ | 학술논문

本文首先构建失业综合指数及其测算指标体系，用来客观地反映失业状况；其次通过文献研究、发放问卷调查等分析方法，并在考虑数据可获得性、可靠性等前提下，初步整理出影响失业的相关因素，以2000～2012年各季度对应的数据，对失业综合指数及其影响因素进行多元线性逐步回归分析，从中筛选出影响显著的因素，构建失业综合指数预测模型。同时基于线性相关分析筛选的结果，构建BP神经网络预测模型，同样对失业状况进行预测，并与多元回归预测模型的预测结论进行比较，结果发现后者预测性能高于前者。
본문수선구건실업종합지수급기측산지표체계，용래객관지반영실업상황；기차통과문헌연구、발방문권조사등분석방법，병재고필수거가획득성、가고성등전제하，초보정리출영향실업적상관인소，이2000～2012년각계도대응적수거，대실업종합지수급기영향인소진행다원선성축보회귀분석，종중사선출영향현저적인소，구건실업종합지수예측모형。동시기우선성상관분석사선적결과，구건BP신경망락예측모형，동양대실업상황진행예측，병여다원회귀예측모형적예측결론진행비교，결과발현후자예측성능고우전자。
Firstly , this paper structured the composite index of unemployment (CIU ) , which could reflect the unemployment situa-tion objectively ;secondly , through the way of literature review , questionnaire surveys , and taking into account the availability and relia-bility of the data , the writer sort out the relevant factors which affecting unemployment preliminarily . Finally , based on the data during the years of 2000~2012 , with SPSS17.0 software , multiple linear stepwise regression method have been applied to analysis the relationship between CIU and its influencing factors , and the key factors which affect CIU most significantly were extracted to construct the multiple re-gression forecast model of CIU . Meanwhile ,based on the results of the linear correlation analysis ,and through software of MATLAB R12a , the BP neural network prediction model were constructed too , and comparing with multiple regression prediction model , the research con-clusions showed that the latter prediction model performance better than the former .