Change detection and the causal impact of the yield curve

Shuping Shi, Peter C.B. Phillips*, Stan Hurn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

143 Citations (Scopus)
134 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)966-987
Number of pages22
JournalJournal of Time Series Analysis
Issue number6
Early online date9 Sept 2018
Publication statusPublished - Nov 2018


  • Causality
  • forward recursion
  • hypothesis testing
  • real economic activity
  • recursive evolving test
  • rolling window
  • yield curve


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