https://scholars.lib.ntu.edu.tw/handle/123456789/635579
標題: | Granger causality tests based on reduced variable information | 作者: | Tseng, Neng Fang YING-CHAO HUNG Nakano, Junji |
關鍵字: | Gaussian white noise | modified Wald test | multivariate delta method | reduced information set | subprocess | Vector autoregression | 公開日期: | 1-一月-2023 | 來源出版物: | Journal of Time Series Analysis | 摘要: | Granger causality is a classical and important technique for measuring predictability from one group of time series to another by incorporating information of the variables described by a full vector autoregressive (VAR) process. However, in some applications economic forecasts need to be made based on information provided merely by a portion of variates (e.g., removal of a listed stock due to halting, suspension or delisting). This requires a new formulation of forecast based on an embedded subprocess of VAR, whose theoretical properties are often difficult to obtain. To avoid the issue of identifying the VAR subprocess, we propose a computation-based approach so that sophisticated predictions can be made by utilizing a reduced variable information set estimated from sampled data. Such estimated information set allows us to develop a suitable statistical hypothesis testing procedure for characterizing all designated Granger causal relationships, as well as a useful graphical tool for presenting the causal structure over the prediction horizon. Finally, simulated data and a real example from the stock markets are used to illustrate the proposed method. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/635579 | ISSN: | 01439782 | DOI: | 10.1111/jtsa.12720 |
顯示於: | 工業工程學研究所 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。