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This review explores poverty as a lack of capabilities based on existing literature. The capability approach rejects income-based measures of poverty and well-being, and also argues that human capabilities are the best measure of poverty. Scholars who define poverty and its causes have widely discussed and accepted this approach. The literature recognizes many capabilities, including education, employment, and health, are highly associated with poverty. Hence, this review primarily focuses on three key dimensions to explore poverty as a lack of capabilities. The review findings have identified that lack of capabilities such as being less educated, being ill health and being unemployed or poorly employed are highly associated with the individual being likelihood of poor. In addition, this paper contributes to the knowledge of exploring the relationship between poverty and capabilities.

This study analysis forecasting the bitcoin exchange rate against the USD. The dataset selected for this study starts from January 2015 to June 2022. This study's methodology uses autoregressive integrated moving average forecasting (ARIMA). The overall outcomes of this study were gathered from the statistical software Minitab 21.1. The Box Jenkins approaches are also used to predict the best model. To determine the ARIMA model parameter, this study did autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses. According to the Box-Cox transformation method, log transformation was selected. The outcome demonstrates that the seasonal with the regular difference in the Bitcoin exchange rate against the USD is a stationary data series. The forecasting model used in this study is ARIMA (1,1,0) (2,1,1)12. This predicted model is identified through the Mean squared error by comparing the other guessing ARIMA models. After the prediction, 5 Month bitcoin exchange rate against the USD. Investors will be able to estimate the bitcoin exchange rate against the USD with the use of this information, but volatility must also be properly watched. This will aid investors in making better investment decisions and increase profits. In future studies, better consider another exchange rate of BTC and software experts will develop such type of software based on ARIMA models for prediction.

This study is to find the relationship between government expenditure and government tax revenue in Sri Lanka, using time series data from 1990-2017. Public expenditure was used as the dependent variable in the model while tax revenue, inflation, public debt, and population were the independent variables considered. Stationarity of the time series was tested employing Augmented Dickey-Fuller (ADF) unit root. Johansen's maximum likelihood estimation of the parameters of a co-integrating equation was employed to examine long-run relationship between the variables. In addition, Granger causality test was performed to identify the causal relationship between public expenditure and selected independent variables. The results confirmed the existence of long-run relationship between the public expenditure and tax revenue. The presence of a unidirectional causality between government expenditure and tax revenue was reveled through the results. The study outcomes suggest the necessity of constructive policy decisions pertaining to government revenue and expenditure in view of promoting the Sri Lankan economy.