中国宏观经济变量预测的实证研究——基于混合频率数据BVAR模型被引量：1

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摘要使用基于混合频率数据的BVAR,详细推导了其状态空间形式、贝叶斯推断和样本外预测,并对中国主要宏观经济变量进行预测,与单一频率标准VAR模型相比,由于混合频率BVAR模型挖掘更多数据信息,预测能力大有提高。

作者袁靖孙爱玲

机构地区厦门大学经济学院山东工商学院统计学院烟台职业学院经济管理系

出处《山东工商学院学报》 2014年第3期1-6,共6页 Journal of Shandong Technology and Business University

基金国家社会科学基金项目(12CTJ018) 教育部人文社会科学研究项目(12YJC910013) 国家博士后科学基金项目(2013M531544) 全国统计科学研究计划项目(2012LY143)

关键词混合频率 BVAR模型贝叶斯推断

参考文献10

1Mariano R S, Y Murasawa. A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series[ J ]. Journal of Applied Econometrics, 2003,18 (4) :427 - 443.
2Amoba B, F Diebold, C Scotti. Real -Time Measurement of Busines.s Conditions [ J ]. Journal of Business Economics & Statis- tics,2009, 27(4) :417 -427.
3Giannone D, L Reichlin,D Small. Nowcasting: The Real- Time Informational Content of Macroeconomic Data[ J]. Journal of Monetary Economics ,2008, 55:665 - 676.
4Ghysels E, A Sinko, R Valkanov. The MIDAS Regressions : Further Results and New Directions [ J ]. Econometric Reviews, 2007, 26(1) :53 -90.
5Bai J,E Ghysels, Wright. State Space Models and MIDAS Regressions[ M ]. Baltimore:Johns Hopkins University, 2011.
6Kuzin V, M Mareellino, C Schumacher. MIDAS versus Mixed -Frequency VAR: Nowcasting GDP in the Euro Area[ J ]. Inter- national Journal of Forecasting,2011,27:529 - 542.
7Eraker B, C Chiu, A Foerster, T Kim, H Seoane. Bayesian Mixed Frequency VAR's [ M ]. Madison:University of Wisconsin, 2011.
8Doan T, R Litterman, C A Sims. Forecasting and Conditional Projections Using Realistic Prior Distributions [ J ]. Econometric Reviews, 1984,3 (4), 1 - 100.
9Martha Banbura,Domenico Giannone,Lucrezia Reichlin. Now- Casting and the Real -Time Data Flow[ M]. Brussel: Univer- site Libre de Bruxelles,2012.
10Del Negro M, F Schorfheide. Bayesian Macroeconometrics [M]. Oxford : Oxford University,2011.