The conceptualisation of the Provisioning of Assistive Resources for Patrons with Visual Impairment within Academic Libraries in Zimbabwe

The introduction of assistive resources within academic libraries in the 20th century positively affected library users (patrons) since they eased information access challenges for patrons living with impairments. However, most academic libraries in developing countries are still grappling with challenges relating to ensuring adequate services to patrons with visual impairment. Given this problem, this paper seeks to provide a better overview and understanding of what provisioning of assistive resources entails. Such an understanding would aid decision-making in academic libraries on the specific resources that should be acquired for patrons with visual impairment. In addition, this paper also went on to conceptualise the term patrons with visual impairment, this is because there has been confusion within most academic libraries on the specific individuals that fall within this category

Can Afghanistan be a victim of the Malthusian population trap? what does the ARIMA approach tell us?

Using annual time series data on total population in Afghanistan from 1960 to 2017, we model and forecast total population over the next 3 decades using the Box – Jenkins ARIMA technique. Diagnostic tests such as the ADF tests show that Afghanistan annual total population is I (1). Based on the AIC, the study presents the ARIMA (1, 1, 2) model as the best model. The diagnostic tests further show that the presented model is stable and that its residuals are I (0). The results of the study reveal that total population in Afghanistan will continue to rise gradually in the next three decades and in 2050 Afghanistan’s total population will be approximately 51 million people. In order to circumvent the chances of being a victim of the Malthusian population trap, 4 policy prescriptions have been suggested for consideration by the government of Afghanistan

Modeling the long-run relationship between inflation and economic growth in Zimbabwe: a bi-variate cointegration (Engle-Granger Two-Step) approach

The debate on the nexus between economic growth and inflation is generally inconclusive and yet inevitably interesting. This study makes a contribution to the existing debate by empirically investigating the relationship between inflation and economic growth in the context of Zimbabwe. Using time series data spanning from 1960 up to 2017, the study employs the Engle – Granger Two Step modeling technique in order to analyze the relationship between inflation and economic growth in Zimbabwe. Our findings indicate that there is a negative and statistically significant relationship between inflation and economic growth both in the short – run and long – run. The speed of adjustment to equilibrium is approximately 62% annually when the variables wander away from their equilibrium values. Amongst other policy prescriptions, the study recommends inflation targeting policy in order to stimulate growth while maintaining price stability in Zimbabwe

Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models

This research uses annual time series data on CO2 emissions in China from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Diagnostic tests indicate that China CO2 emission data is I (2). The study presents the ARIMA (1, 2, 1) model. The diagnostic tests further imply that the presented best model is stable and hence acceptable for predicting carbon dioxide emissions in China. The results of the study reveal that CO2 emissions in China are likely to increase and thereby exposing China to a plethora of climate change related challenges. 4 main policy prescriptions have been put forward for consideration by the Chinese government

Modeling and forecasting inflation in The Gambia: an ARMA approach

This research uses annual time series data on inflation rates in The Gambia from 1962 to 2016, to model and forecast inflation using ARMA models. Diagnostic tests indicate that G is I(0). The study presents the ARMA (1, 0, 0) model [which is nothing but an AR (1) model]. The diagnostic tests further imply that the presented optimal ARMA (1, 0, 0) model is stable and indeed acceptable. The results of the study apparently show that G will be approximately 7.88% by 2020. Policy makers and the business community in The Gambia are expected to take advantage of the anticipated stable inflation rates over the next decade

Modeling and forecasting inflation in The Gambia: an ARMA approach

This research uses annual time series data on inflation rates in The Gambia from 1962 to 2016, to model and forecast inflation using ARMA models. Diagnostic tests indicate that G is I(0). The study presents the ARMA (1, 0, 0) model [which is nothing but an AR (1) model]. The diagnostic tests further imply that the presented optimal ARMA (1, 0, 0) model is stable and indeed acceptable. The results of the study apparently show that G will be approximately 7.88% by 2020. Policy makers and the business community in The Gambia are expected to take advantage of the anticipated stable inflation rates over the next decade

Modeling the long-run relationship between inflation and economic growth in Zimbabwe: a bi-variate cointegration (Engle-Granger Two-Step) approach

The debate on the nexus between economic growth and inflation is generally inconclusive and yet inevitably interesting. This study makes a contribution to the existing debate by empirically investigating the relationship between inflation and economic growth in the context of Zimbabwe. Using time series data spanning from 1960 up to 2017, the study employs the Engle – Granger Two Step modeling technique in order to analyze the relationship between inflation and economic growth in Zimbabwe. Our findings indicate that there is a negative and statistically significant relationship between inflation and economic growth both in the short – run and long – run. The speed of adjustment to equilibrium is approximately 62% annually when the variables wander away from their equilibrium values. Amongst other policy prescriptions, the study recommends inflation targeting policy in order to stimulate growth while maintaining price stability in Zimbabwe

Prediction of total population in Togo using ARIMA models

Using annual time series data on total population in Togo from 1960 to 2017, we model and forecast total population over the next 3 decades using the Box – Jenkins ARIMA technique. Diagnostic tests such as the ADF tests show that Togo annual total population is neither I (1) nor I (2) but for simplicity purposes, the researcher has assumed it is I (2). Based on the AIC, the study presents the ARIMA (3, 2, 0) model as the best model. The diagnostic tests further indicate that the presented model is stable. The results of the study reveal that total population in Togo will continue to rise in the next three decades and in 2050 Togo’s total population will be approximately 14.2 million people. In order to benefit from an increase in total population in Togo, 3 policy recommendations have been suggested for consider by the government of Togo

Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models

This research uses annual time series data on CO2 emissions in China from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Diagnostic tests indicate that China CO2 emission data is I (2). The study presents the ARIMA (1, 2, 1) model. The diagnostic tests further imply that the presented best model is stable and hence acceptable for predicting carbon dioxide emissions in China. The results of the study reveal that CO2 emissions in China are likely to increase and thereby exposing China to a plethora of climate change related challenges. 4 main policy prescriptions have been put forward for consideration by the Chinese government