组合模型下陕西科技人才需求数量预测  

Forecasting the Aem and Quantity of Science and Technology Talents in Shaanxi Province under the Combination Model

作　　者：罗静[1] 屈静雯 杨睿娟[1] LUO Jing;QU Jingwen;YANG Ruijuan(School of Economics and Management,Xi’an Shiyou University,Xi’an 710065,China)

机构地区：[1]西安石油大学经济管理学院,西安710065

出　　处：《科技和产业》2023年第5期19-24,共6页Science Technology and Industry

基　　金：陕西省创新能力支撑计划项目(2020KRM064);陕西省高等教育教学改革项目(17BY044)。

摘　　要：基于陕西省科技人才发展现状,运用统计年鉴动态数据,结合Granger因果关系检验和逐步回归分析构建时间序列-BP神经网络组合预测模型,预测2021—2025年陕西省科技人才需求数量。研究表明:与灰色GM(1,1)模型相比,时间序列模型对规上工业企业工业总产值的预测精度更高;陕西省规上工业企业总产值能够有效预测陕西省科技人才需求数量;陕西省科技人才需求数量将呈指数趋势显著上升,到2025年达26.7万人。以此从关注陕西省重点产业、完善陕西省科技人才管理方案和充分利用陕西省科技人才3个方面提出相关建议,充分激发科技人才创新潜能。Based on the development status of scientific and technological talents in Shaanxi Province,using the dynamic data of statistical yearbook,combined with Granger causality test and stepstep regression analysis,the prediction model of time series and BP neural network is established to predict the demand for scientific and technological talents in Shaanxi Province from 2021 to 2025.The results show that compared with the grey GM(1,1)model,the time series model can predict the total industrial output value more accurately.The total output value of regulated industrial enterprises in Shaanxi province can effectively predict the demand for scientific and technological talents in Shaanxi Province.The demand for scientific and technological talents in Shaanxi province will increase exponentially and reach 267000 by 2025.In order to fully stimulate the innovation potential of scientific and technological talents,suggestions are put forward from three aspects:paying attention to key industries in Shaanxi Province,improving the management plan of scientific and technological talents in Shaanxi Province and making full use of them.

关 键 词：科技人才 GRANGER检验 GM(1 1)模型 BP神经网络模型 

分 类 号：C93[经济管理—管理学]