基于混频回归类模型对中国季度GDP的预报方法研究

被引:42
作者
王维国 [1 ]
于扬 [1 ,2 ]
机构
[1] 东北财经大学
[2] 内蒙古财经大学
关键词
预报; 混频回归联合预测模型; 季度GDP;
D O I
10.13653/j.cnki.jqte.2016.04.008
中图分类号
F224 [经济数学方法]; F124 [经济建设和发展];
学科分类号
0701 ; 070104 ; 0201 ; 020105 ;
摘要
根据混频数据计量经济模型的建模理论和分析技术,本文构建了中国季度GDP 5种不同权重函数的混频数据回归预测模型(MIDAS)和非限制MIDAS模型。结合传统分布滞后模型推导出MIDAS模型的最小二乘识别方法,并在此基础上对中国季度GDP进行短期预报,分析了高频解释变量滞后阶数变化效应及其对低频变量GDP的影响效应。根据6种模型拟合及预测结果,进一步构建混频回归联合预测模型,并考察了混频回归联合预测模型的预测精度及预测效果。研究结果表明:非限制混频数据回归预测模型的预测精度及拟合效果高于5种不同权重MIDAS模型,以BIC为权重构建的混频联合预测模型在对我国季度GDP短期预报时表现最优。
引用
收藏
页码:108 / 125
页数:18
相关论文
共 18 条
[11]  
MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets[J] . C. Emre Alper,Salih Fendoglu,Burak Saltoglu.Economics Letters . 2012 (2)
[12]  
Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*[J] . MassimilianoMarcellino,ChristianSchumacher.Oxford Bulletin of Economics and Statistics . 2010 (4)
[13]  
MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area[J] . Vladimir Kuzin,Massimiliano Marcellino,Christian Schumacher.International Journal of Forecasting . 2010 (2)
[14]  
MIDAS Regressions: Further Results and New Directions[J] . Eric Ghysels,Arthur Sinko,Rossen Valkanov.Econometric Reviews . 2007 (1)
[15]  
Chapter 4 Forecast Combinations[J] . Allan Timmermann.Handbook of Economic Forecasting . 2006
[16]  
Predicting volatility: getting the most out of return data sampled at different frequencies[J] . Eric Ghysels,Pedro Santa-Clara,Rossen Valkanov.Journal of Econometrics . 2005 (1)
[17]   There is a risk-return trade-off after all [J].
Ghysels, E ;
Santa-Clara, P ;
Valkanov, R .
JOURNAL OF FINANCIAL ECONOMICS, 2005, 76 (03) :509-548
[18]   Combination forecasts of output growth in a seven-country data set [J].
Stock, JH ;
Watson, MW .
JOURNAL OF FORECASTING, 2004, 23 (06) :405-430