.png){kind=icon}
.png){kind=icon}
Return
Persistence-robust surplus-lag Granger causality testing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jeconom.2012.01.023.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Previous literature has introduced causality tests with conventional limiting distributions in I(0)/I(1) vector autoregressive (VAR) models with unknown integration orders, based on an additional surplus lag in the specification of the estimated equation, which is not included in the tests. By extending this surplus lag approach to an infinite order VARX framework, we show that it can provide a highly persistence-robust Granger causality test that accommodates i.a stationary, nonstationary, local-to-unity, long-memory, and certain (unmodelled) structural break processes in the forcing variables within the context of a single chi(2) null limiting distribution. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Granger causality
VAR
Long-memory
Structural breaks
Forward rate unbiasedness
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.