Asymmetric autoregressive models: statistical aspects and a financial application under COVID-19 pandemic

Liu, Yonghui; Mao, Chaoxuan; Leiva, Víctor; Liu, Shuangzhe; Neto, Waldemiro A. Silva

doi:10.1080/02664763.2021.1913103

Cited by 17 publications

(22 citation statements)

References 48 publications

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“…The convergence of the MCMC algorithm was monitored using the trace-plots of the generated Gibbs samples. In the simulation process to generate the samples of the joint posterior distribution of interest, we considered initially 200,000 Gibbs samples discarded to eliminate the effect of the initial values in the iterative process and taking a final sample of size 1,000 to get the Monte Carlo estimates for each parameter (taking every 100 th sample of a total of 100,000 simulated samples) [46,47,48,49,50].…”

Section: Resultsmentioning

confidence: 99%

Climate Change Data: Use of an Autoregressive (AR) Model in Presence of Change Points under a Bayesian Approach

Achcar

Barili

2023

IJECC

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In this study, we introduce a statistical model applied to climate change data consisting of an autoregressive times series (AR) model which represents a type of random process. A Bayesian approach using MCMC (Markov Chain Monte Carlo) methods is considered to get the inferences of interest. The main goal of the study is to have a model to get good predictions for mean temperature and also good to identify the time of possible change-points that might be present in the time series which could indicate the possible beginning of a change in climate. Applications of the proposed model are considered using annual average temperatures in some locations obtained over a period of time ranging from the end of 1800’s to a popular Bayesian discrimination criterion using MCMC methods.In addition to a good fit of the proposed model for the data, the model also was used to detect the times of climate changes in the different climate stations using CUSUM methodology.

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Section: Resultsmentioning

confidence: 99%

Climate Change Data: Use of an Autoregressive (AR) Model in Presence of Change Points under a Bayesian Approach

Achcar

Barili

2023

IJECC

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“…Despite the fact that some countries already knew that they had COVID-19 infected cases in their territory since the beginning of February 2020, it was in mid-March 2020 when the governments made the first decisions to control the spread of the virus. With full knowledge of what was happening in Asia and Europe, some South American countries minimized the impact that COVID-19 would have on the economy [2,3] and on people's health. Other countries were a little more cautious and took strong measures from mid-March 2020, such as closing borders and confining citizens.…”

Section: Discussionmentioning

confidence: 99%

“…The COVID-19 pandemic has deteriorated the usual dynamics that rule the world [1]. Its impact on health and the worldwide economy is evident [2,3]. The number of people affected by the COVID-19 disease, the significant number of human lives that have been lost, and the consequences of the containment measures are urgent concerns that should be analyzed.…”

Section: Introductionmentioning

confidence: 99%

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data

Martín-Barreiro

Ramirez-Figueroa

Cabezas

et al. 2021

Sensors

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In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.

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“…The use of global and local influence methods [58], as well as leverage, to diagnose atypical observations are needed. These methods are an appropriate step to employ in all statistical/econometric modeling.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Modeling COVID-19 Cases Statistically and Evaluating Their Effect on the Economy of Countries

et al. 2021

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COVID-19 infections have plagued the world and led to deaths with a heavy pneumonia manifestation. The main objective of this investigation is to evaluate the performance of certain economies during the crisis derived from the COVID-19 pandemic. The gross domestic product (GDP) and global health security index (GHSI) of the countries belonging–or not–to the Organization for Economic Cooperation and Development (OECD) are considered. In this paper, statistical models are formulated to study this performance. The models’ specifications include, as the response variable, the GDP variation/growth percentage in 2020, and as the covariates: the COVID-19 disease rate from its start in March 2020 until 31 December 2020; the GHSI of 2019; the countries’ risk by default spreads from July 2019 to May 2020; belongingness or not to the OECD; and the GDP per capita in 2020. We test the heteroscedasticity phenomenon present in the modeling. The variable “COVID-19 cases per million inhabitants” is statistically significant, showing its impact on each country’s economy through the GDP variation. Therefore, we report that COVID-19 cases affect domestic economies, but that OECD membership and other risk factors are also relevant.

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Asymmetric autoregressive models: statistical aspects and a financial application under COVID-19 pandemic

Cited by 17 publications

References 48 publications

Climate Change Data: Use of an Autoregressive (AR) Model in Presence of Change Points under a Bayesian Approach

Climate Change Data: Use of an Autoregressive (AR) Model in Presence of Change Points under a Bayesian Approach

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data

Modeling COVID-19 Cases Statistically and Evaluating Their Effect on the Economy of Countries

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