论文标题
双变量固定曲线时间序列的Granger因果关系
Granger causality of bivariate stationary curve time series
论文作者
论文摘要
我们使用Granger因果关系概括的相关性测量方法研究双变量曲线时间序列之间的因果关系。通过此措施，我们可以调查哪个曲线时间序列Granger-CAS对方。反过来，它有助于确定任何两个曲线时间序列的可预测性。通过气候学的例子说明，我们发现海面温度Granger会导致海平面大气压。由财务投资组合管理申请的激励，我们挑出了那些领导或落后于道琼斯工业平均值的股票。鉴于标准普尔500指数与原油价格之间的密切关系，我们确定了领先和滞后变量。
We study causality between bivariate curve time series using the Granger causality generalized measures of correlation. With this measure, we can investigate which curve time series Granger-causes the other; in turn, it helps determine the predictability of any two curve time series. Illustrated by a climatology example, we find that the sea surface temperature Granger-causes the sea-level atmospheric pressure. Motivated by a portfolio management application in finance, we single out those stocks that lead or lag behind Dow-Jones industrial averages. Given a close relationship between S&P 500 index and crude oil price, we determine the leading and lagging variables.
