中国GDP增长率概率分布的预测分析——基于分位数因子模型  

Forecast Analysis of Probability Distribution of China s GDP Growth Rate Based on Quantile Factor Models

作　　者：肖强 曹红红 XIAO Qiang;CAO Hong-hong

机构地区：[1]兰州财经大学统计与数据科学学院、甘肃经济发展数量分析研究中心 [2]兰州财经大学统计与数据科学学院

出　　处：《中央财经大学学报》2024年第4期92-103,共12页Journal of Central University of Finance & Economics

基　　金：国家自然科学基金项目“分位数动态因子模型的构建及其应用”(项目编号:72163019);甘肃省重点研发项目“面向社区的社会治理大数据技术研究与应用”(项目编号:22YF7FA167);甘肃省青年博士基金项目“基于分位数动态因子模型的通货膨胀与经济增长关联性分析”(项目编号:2023QB-071)。

摘　　要：研究GDP增长率的概率分布,可以掌握经济增长的可能范围和经济发展趋势的不确定性,有助于决策者评估经济增长的风险和挑战,制定有效的经济政策。本文基于分位数因子模型(Quantile Factor Models,QFM),从高维宏观经济变量中提取分位数因子,拟合以分位数因子为条件的GDP增长率概率分布。实证结果表明:第一,分位数因子可为经济预测提供额外解释信息,提高模型的预测精度;第二,样本期间条件概率密度拟合结果表明QFM对GDP增长率的短期预测效果较好;第三,对比以分位数因子为条件和以实际GDP增长率为条件的两种概率分布,分位数因子为条件的分布在经济受到冲击时不确定性增大。本文对GDP增长率分布预测的研究与传统的均值预测相比,能提供比较全面和精确的经济预测信息,为经济不确定性和下行风险研究提供新思路,为经济波动机制的深入理解提供支持。This study delves into the probability distribution of GDP growth rate through the utilization of quantile factor models,which facilitate the evaluation of a broad spectrum of economic growth scenarios and uncertainties.By extracting quantile factors from macroeconomic variables,the models establish the probability distribution of GDP growth rate.The results indicate that the inclusion of quantile factors improves the ability of economic forecasts to explain outcomes and also enhances their accuracy.Furthermore,short-term forecasts,grounded in conditional probability density fitting,exhibit notably improved precision.Through an examination of the GDP growth rate probability distribution using quantile factor models,this study underscores the advantages of conditional probability density forecasts in offering more precise insights into economic volatility and downside risks when compared to conventional mean forecasts.These discoveries carry substantial implications for policymaking and contribute significantly to a deeper comprehension of the mechanisms underlying economic volatility and uncertainty.

关 键 词：GDP增长率 概率密度 预测 分位数因子模型 

分 类 号：F201[经济管理—国民经济] O212[理学—概率论与数理统计]