scholarly journals Forecasting COVID-19 Pandemic and Capital Market Efficiency in Africa

2021 ◽  
Vol 5 (1) ◽  
pp. 136-153
Author(s):  
Kolawole Lookman Subair ◽  
Ibrahim Ayoade Adekunle
Keyword(s):  
Capital Market ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Jel Classification ◽  
World Health ◽  
Policy Document ◽  
Market Capitalization ◽  
Development Indicators ◽  
Percentage Decrease

Despite a large and growing list of studies on COVID-19 across space and time and on heterogeneous social, environmental and welfare issues, the empirical relations on the consequences of COVID-19 pandemic and Africa’s market capitalization objectives remain dimly discerned. Even more worrisome is Africa, where the condition for growth and development has not been adequately fulfilled. This structural ambiguity calls for a policy document that is evidence-based to reach conclusions to aid the containment, risk analysis, structures and features of the deadly and fast-spreading disease. This study employed negative binomial and the Poisson regression to establish the contemporaneous influence of COVID-19 pandemic on market capitalization capabilities in Africa. Health data from various reports of the World Health Organization (WHO) is regressed on the all-share index from World Development Indicators (WDI) to establish a clear line of thought. It is found that the growth of confirmed cases and attributable deaths are inversely related to the growth in market capitalization in Africa. The findings from this study show that Africa market capitalization is inversely related to total growth in the number of confirmed cases of COVID-19 and attributable COVID-19 deaths. This leads to the assertion that Africa’s capital market is fast nosediving in the time of COVID-19 due to global uncertainties caused by the pandemic. With no known cure or vaccine procedure discovered yet, the global uncertainty around the novel coronavirus disease will lead to approximately 0.56 percentage decrease in market capitalization in Africa. To this end, emphases must be laid on identifying and including non-traditional sources of financing strictly tied to projects that could encourage institutional investors. It is therefore equally imperative for Africa to form a formidable and integrated capital market among themselves.  Keywords: market capitalization; COVID-19 pandemic, negative binomial Regression, poisson, Regression, Africa JEL Classification: C10, C31, G15, I12

scholarly journals Analyzing Overdispersed Antenatal Care Count Data in Bangladesh: Mixed Poisson Regression with Individual-Level Random Effects

2021 ◽  
Vol 50 (4) ◽  
pp. 78-90
Author(s):  
Zakir Hossain ◽  
Maria
Keyword(s):  
Antenatal Care ◽  
Random Effects ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Wealth Index ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
World Health ◽  
Individual Level

Poisson regression (PR) is commonly used as the base model for analyzing count data with the restrictive equidispersion property. However, overdispersed nature of count data is very common in health sciences. In such cases, PR produces misleading inferences and hence give incorrect interpretations of the results. Mixed Poisson regression with individual--level random effects (MPR_ILRE) is a further improvement for analyzing such data. We compare MPR_ILRE with PR, quasi-Poisson regression (Q_PR) and negative binomial regression (NBR) for modelling overdispersed antenatal care (ANC) count data extracted from the latest Bangladesh Demographic and Health Survey (BDHS) 2014. MPR_ILRE is found to be the best choice because of its minimum Akaike information criterion (AIC) value and the overdispersion exists in data has also been modelled very well. Study findings reveal that on average, women attended less than three ANC visits and only 6.5\% women received the World Health Organization (WHO) recommended eight or more ANC visits for the safe pregnancy and child birth. Administrative division, place of residence, birth order, exposure of media, education, wealth index and body mass index (BMI) have significant impact on adequate ANC attendance of women to reducing pregnancy complications, maternal and child deaths in Bangladesh.

scholarly journals Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java

2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Yopi Ariesia Ulfa ◽  
Agus M Soleh ◽  
Bagus Sartono
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Directorate General ◽  
The Republic ◽  
And Control

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.

PEMODELAN DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat)

2021 ◽  
Vol 10 (3) ◽  
pp. 226-236
Author(s):  
Khusnul Khotimah ◽  
Itasia Dina Sulvianti ◽  
Pika Silvianti
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Kernel Weight ◽  
Negative Binomial Regression Model ◽  
West Java ◽  
Binomial Regression ◽  
Spatial Heterogenity

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

scholarly journals PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON

2013 ◽  
Vol 2 (2) ◽  
pp. 6
Author(s):  
PUTU SUSAN PRADAWATI ◽  
KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Poisson Regression Model ◽  
Response Variable ◽  
Poisson Regression Analysis ◽  
Binomial Regression ◽  
The Mean

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.

scholarly journals Suicide by pesticide poisoning in India: a review of pesticide regulations and their impact on suicide trends

2019 ◽  
Author(s):  
Toby Bonvoisin ◽  
Leah Utyasheva ◽  
Duleeka Knipe ◽  
David Gunnell ◽  
Michael Eddleston
Keyword(s):  
Negative Binomial ◽  
Public Health Problem ◽  
State Level ◽  
Negative Binomial Regression ◽  
World Health ◽  
State Governments ◽  
Suicide Rates ◽  
Level Data ◽  
Agricultural Yield ◽  
Health Organization

Abstract Background Pesticide self-poisoning is a common means of suicide in India. Banning highly hazardous pesticides (HHPs) from agricultural use has been successful in reducing suicides in several Asian countries without affecting agricultural output. Here, we describe national and state-level regulation of HHPs and explore how they might relate to suicide rates across India.Methods Information on pesticide regulation was collated from agriculture departments of the central and state governments. National and state-level data on suicides from 1995 to 2015 were obtained from the National Crime Records Bureau (NCRB). We used joinpoint analysis and negative binomial regression to investigate any effects on trends in suicide rates nationally and in Kerala.Results As of October 2019, 318 pesticides were registered for use in India, of which 18 were extremely (Class Ia) or highly (Class Ib) hazardous according to World Health Organization criteria. Despite many HHPs still being available, several bans have been implemented during the period studied. In our quantitative analyses we focused on the permanent bans in Kerala in 2005 (of endosulfan) and 2011 (of 14 other pesticides); and nationally in 2011 (of endosulfan). NCRB data indicate that pesticides were used in 441,918 reported suicides in India from 1995-2015, 90.3% of which occurred in 11 of the 29 states. There was statistical evidence of lower than expected rates of pesticide suicides (rate ratio [RR] 0.52, 95% CI 0.49-0.54) and total suicides nationally by 2014 (0.90, 0.87-0.93) after the 2011 endosulfan ban. In Kerala, there was a lower than expected pesticide suicide rate (0.45, 0.42-0.49), but no change to the already decreasing trend in total suicides after the 2011 ban of 14 pesticides. The 2005 ban on endosulfan showed a similar effect. Agricultural outputs continued growing following the bans.Discussion Highly hazardous pesticides continue to be used in India and pesticide suicide remains a serious public health problem. However, some pesticide bans do appear to have impacted previous trends in the rates of both pesticide suicides and all suicides. Comprehensive national bans of HHPs could lead to a reduction in suicides across India, in addition to reduced occupational poisoning, with minimal effects on agricultural yield.

scholarly journals Modeling COVID-19 Cases in Nigeria Using Some Selected Count Data Regression Models

2020 ◽  
pp. 64-73
Author(s):  
Samuel Olorunfemi Adams ◽  
Muhammad Ardo Bamanga ◽  
Samuel Olayemi Olanrewaju ◽  
Haruna Umar Yahaya ◽  
Rafiu Olayinka Akano
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Significance Level ◽  
Generalized Poisson ◽  
Binomial Regression ◽  
Selection Test ◽  
Generalized Poisson Regression

COVID-19 is currently threatening countries in the world. Presently in Nigeria, there are about 29,286 confirmed cases, 11,828 discharged and 654 deaths as of 6th July 2020. It is against this background that this study was targeted at modeling daily cases of COVID-19’s deaths in Nigeria using count regression models like; Poisson Regression (PR), Negative Binomial Regression (NBR) and Generalized Poisson Regression (GPR) model. The study aim at fitting an appropriate count Regression model to the confirmed, active and critical cases of COVID-19 in Nigeria after 118 days. The data for the study was extracted from the daily COVID-19 cases update released by the Nigeria Centre for Disease Control (NCDC) online database from February 28th, 2020 – 6th, July 2020. The extracted data were used in the simulation of Poisson, Negative Binomial, and Generalized Poisson Regression model with a program written in STATA version 14 and fitted to the data at a 5% significance level. The best model was selected based on the values of -2logL, AIC, and BIC selection test/criteria. The results obtained from the analysis revealed that the Poisson regression could not capture over-dispersion, so other forms of Poisson Regression models such as the Negative Binomial Regression and Generalized Poisson Regression were used in the estimation. Of the three count Regression models, Generalized Poisson Regression was the best model for fitting daily cumulative confirmed, active and critical COVID-19 cases in Nigeria when overdispersion is present in the predictors because it had the least -2log-Likelihood, AIC, and BIC. It was also discovered that active and critical cases have a positive and significant effect on the number of COVID-19 related deaths in Nigeria.

Penanganan Overdispersi pada Regresi Poisson dengan Regresi Binomial Negatif pada Kasus Kemiskinan di Indonesia

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Lulu Mahdiyah Sandjadirja ◽  
Muhammad Nur Aidi ◽  
Akbar Rizki
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
External Factors ◽  
Poor People ◽  
Negative Binomial Regression Model ◽  
Average Value ◽  
Binomial Regression ◽  
Working Age

Poisson regression can be used to model rare events that consist of count data. Poisson regression application is carried out to find out external factors that affect the number of poor people in Indonesia by the province in 2016. The assumptions that must be met in this analysis are equdispersion. However, in real cases there is often a problem of overdispersion, ie the value of the variance is greater than the average value. High diversity can be caused by outliers. Expenditures on outliers have not been able to deal with the problem of overdispersion in Poisson Regression. One way to overcome this problem is to replace the Poisson distribution assumption with the Negative Binomial distribution. The results of the analysis show that the Negative Binomial Regression model without outliers is better than the Poisson Regression without outliers model indicated by a smaller AIC value. Based on the Negative Binomial Regression model without this outlier the external factors that affect the number of poor people in Indonesia by the province in 2016 are the percentage of households with floor conditions of houses with soil by province, population by province, percentage of unemployment to the total workforce by province and the percentage of the workforce against the working age population.

scholarly journals Determinants of Antenatal Care Visit Utilization of Child-Bearing Mothers in Kaffa, Sheka, and Bench Maji Zones of SNNPR, Southwestern Ethiopia

2019 ◽  
Vol 6 ◽  
pp. 233339281986662 ◽  
Author(s):  
Abiyot Negash Terefe ◽  
Assaye Belay Gelaw
Keyword(s):  
Health Care ◽  
Antenatal Care ◽  
Rural Areas ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Care Program ◽  
Normal Pregnancy ◽  
World Health ◽  
Child Bearing

Background: Antenatal care (ANC) is a preventive obstetric health-care program aimed at optimizing maternal fetal outcome through regular monitoring of pregnancy. Even if World Health Organization recommends a minimum of 4 ANC visits for normal pregnancy, existing evidence from developing countries including Ethiopia indicates there are few women who utilize it due to different reasons. The purpose of this article is to identify determinants significantly influencing the ANC visit utilization of child-bearing mothers in the Kaffa, Sheka, and Bench-Maji zones of Southern Nation Nationalities and Peoples Region, Ethiopia. Methods: A total of 1715 child-bearing mothers were selected. Several count models such as Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial, hurdle Poisson, and hurdle negative binomial regression models were fitted to select the model which best fits the data. The parameters were estimated by maximum likelihood. Measures of goodness of fit were based on the Rootogram. Results: The data were found zeros (8.1%); the variance (3.794), which is less than its mean (3.91). Hurdle Poisson regression model was found to be better fitted with the data given. Variables are selected by backward selection method, through the analysis, zones, residence, age at first pregnancy, source of information, knowledge during danger sin, willingness, time of visit, and satisfaction, which were major predictors of ANC service utilization. The estimated odds that the number of ANC visits those child-bearing mothers made (mothers who lived in urban) are 3.52 times more likely than mothers who lived in rural keeping others variables constant and the like. Conclusion: Based on our findings, a lot of effort needs to be made by health offices to create awareness, maternal health-care programs should be expanded and intensified in rural areas, improve women’s knowledge and awareness about the risk factor of late visit, the necessary investigations and follow-up throughout the antenatal period to promote regular attendance for ANC, and fulfill the client’s satisfaction.

scholarly journals Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 829
Author(s):  
Shuai Sun ◽  
Jun Bi ◽  
Montserrat Guillen ◽  
Ana M. Pérez-Marín
Keyword(s):  
Risk Score ◽  
Poisson Regression ◽  
High Speed ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Near Miss ◽  
Risk Scores ◽  
Data Set ◽  
Binomial Regression ◽  
Driving Risk

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

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