This paper investigates the contribution of futures market information to enhancing the predictive accuracy of macroeconomic forecasts, using data from China. We employ three cat-egories of predictors: monthly macroeconomic factors, daily commodity futures factors, and daily financial futures variables. Principal component analysis is applied to extract key fac-tors from large data sets of monthly macroeconomic indicators and daily commodity futures contracts. To address the challenge of mixed sampling frequencies, these predictors are incor-porated into factor-MIDAS models for both nowcasting and long-term forecasting of critical macroeconomic variables. The empirical results indicate that financial futures data provide modest improvements in forecasting secondary and tertiary GDP, whereas commodity futures factors significantly improve the accuracy of PPI forecasts. Interestingly, for PMI forecast-ing, models relying exclusively on futures market data, without incorporating macroeconomic factors, achieve superior predictive performance. Our findings underscore the significance of futures market information as a valuable input to macroeconomic forecasting.

运用2007年1月- 2012年2月，62个月间的钢铁价格及相关数据, 运用单位根检验、协整分析、VECM 模型,对影响钢铁价格的各因素进行了实证研究。实证结果表明, 影响钢铁价格的主要因素有钢铁产量、进口铁矿石价格、货币供给量、房地产投资额、汽车产量, 这些变量所构成的钢铁价格检验模型精确度较高, 能够较好地解释钢铁价格的变化; 同时, 各个变量和钢铁价格的综合长短期关系要好于长期关系。该模型定量测量了各影响因素对钢铁价格的影响权重及大小, 以期为宏观调控部门影响和调控钢铁价格提供借鉴。