The co-movement of monetary policy and its time-varying nature: A DCCA approach

Rohit, Abhishek; Mitra, Subrata Kumar

doi:10.1016/j.physa.2017.11.071

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…He ( 2016 ) used DCCA method to find that there was a long-term cross-correlation between pollutants and meteorology at a 10-year time scale, and it was more obvious in rural areas. DCCA method has been widely used in some other fields (Chen et al, 2018a ; Liang et al, 2017 ; Rohit & Mitra, 2018 ; Yuan & Fu, 2014 ), and its scientificity and applicability have been proved by many scholars.…”

Section: Introductionmentioning

confidence: 99%

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

Bao

Liu

et al. 2023

Environ Monit Assess

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The air pollution in China currently is characterized by high fine particulate matter (PM 2.5 ) and ozone (O 3 ) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM 2.5 and O 3 are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM 2.5 and O 3 . Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM 2.5 and O 3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM 2.5 decreased while O 3 increased in most cities due to the effect of COVID-19, and the increase in O 3 is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM 2.5 -O 3 DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM 2.5 -O 3 VM-DCCA exponents in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM 2.5 and O 3 are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM 2.5 -O 3 DHP coordinated control strategies.

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Section: Introductionmentioning

confidence: 99%

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

Bao

Liu

et al. 2023

Environ Monit Assess

View full text Add to dashboard Cite

show abstract

“…He et al (2016) used DCCA method to test the cross-correlation between pollutants and meteorological factors in urban and rural areas and found that there was a long-term cross-correlation between pollutants and meteorology at a 10-year time scale, and it was more obvious in rural areas. Besides, DCCA method has been widely used in finance (Rohit et al, 2018), medical treatment (Chen et al, 2018a), biology (Liang et al, 2017), climate (Yuan et al, 2014) and other fields, and its scientificity and applicability have been proved by many scholars.…”

Section: Introductionmentioning

confidence: 99%

Response of cross-correlations between high concentrations of PM 2.5 and O 3 at different time scales to the COVID-19: two trends in BTH and PRD

Bao

Liu

et al. 2022

Preprint

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The air pollution in China currently is characterized by high concentrations of fine particulate matter (PM2.5) and ozone (O3). Compared with single high pollution events, these double high pollution (DHP) events (both PM2.5 and O3 are above the National Ambient Air Quality Standards (NAAQS) ) pose a greater threat to public health and environment. However, the studies on the temporal evolution and spatial differences of PM2.5-O3 DHP events is not comprehensive. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM2.5 and O3 deeply and thus provide theoretical support for the formulation of regional coordinated control strategies. In this paper, a novel method detrended cross-correlation analysis based on maximum time series of variable time scales (VM-DCCA) is established to compare the cross-correlation between high concentrations of PM2.5 and O3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD) at different time scales. As a result, through DCCA, there is a long-term persistent behavior about the cross-correlation between PM2.5 and O3. Firstly, compared with non COVID-19 period, the PM2.5-O3 DCCA exponents decrease by 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period. Further, through VM-DCCA, the VM-DCCA exponents in PRD weaken rapidly with the increase of time scales, and the decline range are about 23.53% and 22.90% at 28-hour time scale during the non COVID-19 period and COVID-19 period respectively. BTH is completely different. Without significant tendency, its VM-DCCA exponents is always higher than that in PRD at different time scales, which also suggests that the coordinated control of PM2.5 and O3 in BTH is more difficult than that in PRD. Finally, we consider the above results are manifestation of the self-organized 2 criticality (SOC) theory of atmospheric system. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed.

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