Analysis of economic growth fluctuations based on EEMD and causal decomposition

Mao, Xuegeng; Yang, Albert C.; Peng, Chung Kang; Shang, Pengjian

doi:10.1016/j.physa.2020.124661

Cited by 19 publications

(11 citation statements)

References 52 publications

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“…Sankaran et al [ 119 ], Rana and Sharma [ 120 , 121 ], and Wang and Ngene [ 122 ] suggested to overcome this problem by using the Wald or modified Wald (MWald) tests, but Hayashi et al [ 123 ] and Lemonte [ 124 ] demonstrated that, in small samples when used empirically to search for unimportant parameters, the Wald test procedure could be misleading. In furtherance of Zhang et al [ 125 ], Mao et al [ 126 ], Adebayo [ 97 ], and Chan et al [ 127 ], we used variance decomposition instead of the Wald test to explore the strengths of inter-variables causal interactions and to reveal potential causality impacts. The method was applied for nine consecutive periods from July 2020 till March 2021.…”

Section: Methodsmentioning

confidence: 99%

Impacts of COVID-19 on Trade and Economic Aspects of Food Security: Evidence from 45 Developing Countries

Erokhin

Tianming

2020

IJERPH

246

173

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The stability of food supply chains is crucial to the food security of people around the world. Since the beginning of 2020, this stability has been undergoing one of the most vigorous pressure tests ever due to the COVID-19 outbreak. From a mere health issue, the pandemic has turned into an economic threat to food security globally in the forms of lockdowns, economic decline, food trade restrictions, and rising food inflation. It is safe to assume that the novel health crisis has badly struck the least developed and developing economies, where people are particularly vulnerable to hunger and malnutrition. However, due to the recency of the COVID-19 problem, the impacts of macroeconomic fluctuations on food insecurity have remained scantily explored. In this study, the authors attempted to bridge this gap by revealing interactions between the food security status of people and the dynamics of COVID-19 cases, food trade, food inflation, and currency volatilities. The study was performed in the cases of 45 developing economies distributed to three groups by the level of income. The consecutive application of the autoregressive distributed lag method, Yamamoto’s causality test, and variance decomposition analysis allowed the authors to find the food insecurity effects of COVID-19 to be more perceptible in upper-middle-income economies than in the least developed countries. In the latter, food security risks attributed to the emergence of the health crisis were mainly related to economic access to adequate food supply (food inflation), whereas in higher-income developing economies, availability-sided food security risks (food trade restrictions and currency depreciation) were more prevalent. The approach presented in this paper contributes to the establishment of a methodology framework that may equip decision-makers with up-to-date estimations of health crisis effects on economic parameters of food availability and access to staples in food-insecure communities.

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Section: Methodsmentioning

confidence: 99%

Impacts of COVID-19 on Trade and Economic Aspects of Food Security: Evidence from 45 Developing Countries

Erokhin

Tianming

2020

IJERPH

246

173

View full text Add to dashboard Cite

show abstract

“…These results might be stem from the phenomenon that we have not considered or the limitation of causal decomposition itself. Some previous studies also exhibited less obvious and unclear direction of the causality or sometimes contradictory results in interpreting the causal decomposition results [30] , [31] . The energy strength of IMFs and their causal strength varies from different factors, and the causality results might differ in severe fluctuation among different frequency domains.…”

Section: Empirical Analysismentioning

confidence: 93%

“…On the other hand, causal decomposition has the advantage of reflecting real-world data and phenomena based on instantaneous phase dependency between cause and effect, that is, oscillatory stochastic and deterministic mechanisms [7] . Several studies applied this technique to the change rate for the GDP time series between major on a different time scale [30] and the investigation of Malaria epidemics [31] .…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the association between Nevada and California, the causal strength become clearer for the long-term trend. These patterns show they are different among other states, since the causal relationship for each IMF is known to differ [30] , which mirrors the fact that the epidemic transmission pattern is fluctuating and changeable over the varying periods. The IMFs having the higher average causal strength are highlighted in boldface.…”

Section: Empirical Analysismentioning

confidence: 99%

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Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

Cho¹,

Kim²,

Kim³

2022

Physica A: Statistical Mechanics and its Applications

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“…In order to improve the practicability of EMD, Wu et al [2] proposed the EEMD theory, which superimposes Gaussian white noise onto the original signal, and uses the characteristics of white noise frequency distribution uniformity and mean value zero to adaptively distribute the original signal to the corresponding scale, By averaging the modal components obtained by multiple decomposition, this method can effectively suppress the modal aliasing phenomenon and improve the accuracy of each modal component. Because of the strong applicability of EEMD method, it is widely used in economics [3], energy analysis [4], signal detection [5], fault diagnosis [6] and other fields.…”

Section: Introductionmentioning

confidence: 99%

An adaptive optimization EEMD method and its application in bearing fault detection

Liu¹,

Chen²,

Mao³

2023

Preprint

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Aiming at the optimization of two important parameters (white noise amplitude coefficient and set average number) in the set empirical mode decomposition (EEMD), an adaptive EEMD parameter optimization method is proposed. First of all, this paper extracts the corresponding amplitude of the high-frequency component of the signal through the energy value of the first eigenmode function, uses the relative mean square error to determine the corresponding amplitude of the low-frequency component of the signal, and establishes the optimal amplitude evaluation criteria based on the corresponding amplitude of the two; At the same time, in order to improve the calculation efficiency and reduce the influence of white noise, the energy value of the first modal component is used to determine the optimal average number of sets; Then, the effectiveness of the method in this paper is verified by simulation experiments; Finally, this method is applied to the extraction of bearing inner ring fault vibration signal. The results show that compared with the traditional EEMD method, this method can adaptively determine the noise amplitude and the set average number, and can more effectively identify the periodic fault components of the vibration signal.

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Analysis of economic growth fluctuations based on EEMD and causal decomposition

Cited by 19 publications

References 52 publications

Impacts of COVID-19 on Trade and Economic Aspects of Food Security: Evidence from 45 Developing Countries

Impacts of COVID-19 on Trade and Economic Aspects of Food Security: Evidence from 45 Developing Countries

Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

An adaptive optimization EEMD method and its application in bearing fault detection

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