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双循环
本文基于高维贝叶斯动态因子模型借助大数据技术构建了中国的货币市场金融状况指数，刻画了我国2001年1月至2013年12月货币市场的风险压力状况。我们进一步利用区制分析、相关性分析和ARIMA（4，1，3）模型来对金融状况指数进行短期预测和长期预测，实证结果均证实2014年我国的货币市场运行状况具有明显的不稳定性，市场面临大概率下行风险。
<<This paper builds financial conditions index for China based on large Bayesian dynamic factor model with big data techniques.The financial conditions index accurately depicts risk stress conditions in the money market between January，2001 and December，2013.We further use regime analysis，correlation analysis and ARIMA（4，1，3）model to predict the financial conditions index in the short-run and in the long-run.The results show that China’s money market operation conditions are obvious instable during 2014，the market faces a highly probability of downside risk.
<<| Keywords： | Financial Conditions IndexInstability |
