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Detection of Lead-Lag Relationships Using Both Time Domain and Time-Frequency Domain; An Application to Wealth-To-Income Ratio

Luxembourg School of Finance, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
Economies 2019, 7(2), 28; https://doi.org/10.3390/economies7020028
Received: 5 December 2018 / Revised: 26 January 2019 / Accepted: 15 February 2019 / Published: 1 April 2019
The objective of this paper is the joint application of two different methodological concepts for the detection of lead-lag relationships in economic time-series in order to investigate their consistency and their potential complementarity. The first methodology, a time domain analysis based on vector error correction model, provides evidence about the existence of long-run equilibrium of the time-series and the short-run lead-lag behaviors. The second methodology, a time-frequency concept based on the phase difference of the cross-wavelet coherence, analyzes the lead-lag relationships across various timescales and reveals the altering of leadership over time. The two methods are applied to time-series of wealth-to-income ratio of four developed countries over the period 1970–2010 and analyze the lead-lag relationships of the countries in the long-run and in the short-run. The results show that the two methods are consistent in their major long-run findings, however, they reveal different aspects regarding the short-run dynamics of the lead-lag relationships. Furthermore, the results suggest the complementarity of the two methodologies in the context of a complete framework for the analysis of the lead-lag relationships in non-stationary economic time-series. View Full-Text
Keywords: lead-lag relationships; vector error correction model; wavelet coherence; wealth-to-income ratio lead-lag relationships; vector error correction model; wavelet coherence; wealth-to-income ratio
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Skoura, A. Detection of Lead-Lag Relationships Using Both Time Domain and Time-Frequency Domain; An Application to Wealth-To-Income Ratio. Economies 2019, 7, 28.

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