Publication Type
Journal Article
Version
submittedVersion
Publication Date
11-2018
Abstract
Causal relationships in econometrics are typically based on the concept of predictability and are established by testing Granger causality. Such relationships are susceptible to change, especially during times of financial turbulence, making the real-time detection of instability an important practical issue. This article develops a test for detecting changes in causal relationships based on a recursive evolving window, which is analogous to a procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity and conditional heteroskedasticity of an unknown form. Bootstrap methods are used to control family-wise size in implementation. Simulation experiments compare the efficacy of the proposed test with two other commonly used tests, the forward recursive and the rolling window tests. The results indicate that the recursive evolving approach offers the best finite sample performance, followed by the rolling window algorithm. The testing strategies are illustrated in an empirical application that explores the causal relationship between the slope of the yield curve and real economic activity in the United States over the period 1980-2015.
Keywords
Causality, forward recursion, hypothesis testing, recursive evolving test, rolling window, yield curve, real economic activity
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Time Series Analysis
Volume
39
Issue
6
First Page
966
Last Page
987
ISSN
0143-9782
Identifier
10.1111/jtsa.12427
Publisher
Wiley: 12 months
Citation
SHI, Shuping; PHILLIPS, Peter C. B.; and HURN, Stan.
Change detection and the causal impact of the yield curve. (2018). Journal of Time Series Analysis. 39, (6), 966-987.
Available at: https://ink.library.smu.edu.sg/soe_research/2349
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1111/jtsa.12427