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Change Detection and the Causal Impact of the Yield Curve

Shuping Shi (), Peter Phillips () and Stan Hurn ()

Journal of Time Series Analysis, 2018, vol. 39, issue 6, 966-987

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.

Date: 2018
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https://doi.org/10.1111/jtsa.12427

Related works:
Working Paper: "Change Detection and the Causal Impact of the Yield Curve (2016) Downloads
Working Paper: Change Detection and the Casual Impact of the Yield Curve (2015) Downloads
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