Evidence for chaotic dependence between US inflation and commodity prices

Kyrtsou, Catherine; Labys, Walter C.

doi:10.1016/j.jmacro.2005.10.019

Cited by 160 publications

(117 citation statements)

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“…Therefore we turn to nonlinear tests. Using Hiemstra and Jones (1994), Diks and Panchenko (2006), and Kyrtsou and Labys (2006) symmetric test, we observe a stronger predictive power from EMU to EPU than from EPU to EMU. (2006) test, we find no evidence of positive predictive power from EPU to EMU.…”

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confidence: 82%

“…Hence, we use both the Hiemstra-Jones (1994) and Diks-Panchenko (2006) nonlinear causality tests. In addition, we also employ the Kyrtsou and Labys (2006) symmetric and asymmetric nonlinear approach. We also use Sato et al (2007) time varying causality as well as Balcilar et al (2010) sub-sample bootstrap rolling window causality to account for time variation in the relationship between the series.…”

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confidence: 99%

“…The next nonlinear Granger causality we consider is that developed by Kyrtsou and Labys (2006). Our parameter prior selection is presented in Table 6.…”

Section: [Insert Table 5]mentioning

confidence: 99%

“…Therefore, we perform parameter stability tests on the estimated full sample VAR. Subsequently, we test for causality using nonlinear methods of Hiemstra and Jones (1994), Diks and Panchenko (2006), and Kyrtsou and Labys (2006), Sato et al (2007) timevarying causality and Balcilar et al (2010) sub-sample rolling window bootstrap causality tests. This paper makes contribution to the existing literature.…”

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confidence: 99%

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Causality Between Us Economic Policy and Equity Market Uncertainties: Evidence from Linear and Nonlinear Tests

Ajmi

Aye

Balcılar

et al. 2015

Journal of Applied Economics

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This paper examines the causal relationship between economic policy uncertainty (EPU) and equity market uncertainty (EMU) in the US using linear and nonlinear Granger causality tests. We use daily data on the newly developed indexes by Baker et al. (2013aBaker et al. ( ) covering 1985Baker et al. ( :01:01 to 2013 Results from the linear causality tests indicate strong bidirectional causality. We test for parameters stability, and find strong evidence of short run parameter instability, thus invalidating any conclusion from the full sample linear estimations. Therefore we turn to nonlinear tests. Using Hiemstra and Jones (1994), Diks and Panchenko (2006), and Kyrtsou and Labys (2006) symmetric test, we observe a stronger predictive power from EMU to EPU than from EPU to EMU. (2006) test, we find no evidence of positive predictive power from EPU to EMU. However, we find strong evidence of positive predictive power from EMU to EPU and only weak evidence of negative EPU causing EMU. Performing the causality test using the Sato et al. (2007) time-varying method, we find that the causality between EPU and EMU is not constant over time but rather time-varying. Hence, we implement a sub-sample bootstrap rolling window causality tests to fully account for the existence of structural breaks. Using the intensity plots of the p-values from this, we find evidence that EPU can help predict the movements in EMU only around 1993, 2004 and, 2006. However, we find strong evidence that EMU can help predict the movements in EPU throughout the sample period barring around 1998, 2003 and 2005. Further, the analysis of total effects based on the bootstrap sum of coefficients suggests a positive and stronger causal effect from EMU to EPU but smaller and insignificant causality from EPU to EMU. The implications of these findings for both investors and policy makers are provided.

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confidence: 82%

Section: [Insert Table 3]mentioning

confidence: 99%

“…The next nonlinear Granger causality we consider is that developed by Kyrtsou and Labys (2006). Our parameter prior selection is presented in Table 6.…”

Section: [Insert Table 5]mentioning

confidence: 99%

mentioning

confidence: 99%

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Causality Between Us Economic Policy and Equity Market Uncertainties: Evidence from Linear and Nonlinear Tests

Ajmi

Aye

Balcılar

et al. 2015

Journal of Applied Economics

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show abstract

“…Obecnie ma zastosowanie w programowaniu, mikrobiologii, inżynierii (szczególnie robotyce), finansach, meteorologii, filozofii, polityce, badaniach demograficznych i psychologii (por. [17], [21], [22], [23], [36]). Ta dziedzina badań dotyczy głównie nieliniowych systemów występujących w przyrodzie, szczególnie w naszym najbliższym środowisku.…”

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