Online Program

WITHDRAWN - Generalized Linear Time Series Regression Model and Its Application to Impact of COVID-19 on Women Globally (309604)

Keywords: COVID-19 pandemic; cross-section, deterministic time trend; Generalized linear time series regression; quasi-likelihood estimation

The COVID-19 pandemic is a disease caused by the novel coronavirus and has affected global health, social and economic well-being, with women at the centre. Women are more in the health response to COVID-19, contributing 70% of the health care workforce, exposing them to a greater risk of infection. Also, women are also shouldering much of the burden at home, given school and child care facility. Women are also faced with high risks job and income loss, and face increased risks of violence, exploitation, abuse or harassment during times of crisis and quarantine. The data collected is a data depending on time but is not normally distributed. So, other distribution can be used to fit this data in a time series regression model. Thus, in this research a generalized time series regression model is fitted on data collected on women contribution to COVID-19 pandemic globally with focus on African women. The data is a panel data with countries as cross-sections and time variable. We showed some properties of the model and parameters of the distribution were estimated using maximum likelihood method. We consider this model in two settings; least squares fits of linear specifications of the cross-section component and the parameters of the deterministic time trend and, more generally, quasi-likelihood estimation in a generalized linear time series model.