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                <text>Due to the direct impact of manufacturing firms’ performance to economic stability, there is the need for special attention to examine factors that affect the manufacturing firms’ profitability (Akbas &amp; Karadinan 2012). According to Opondo (2004), most studies has examined the determinants of profitability of firms considering variables such as capital structure, cash liquidity, firm size, and financial leverage in other countries but however, much has not been done in the Ghanaian context. This study assesses the determinant of financial performance of listed manufacturing firms in Ghana. The study used quantitative research approach and explanatory research design. Purposive sampling technique was used to select eight manufacturing companies out of the total population of thirteen listed manufacturing firms. The study used only secondary source of data (audited financial statements) for five year period, from 2011 to 2015. Also, correlation analysis was used to assess the collinearity between the dependent variables thus Return on Equity (ROE) and Return on Asset (ROA) and the independent variables such as financial leverage, cash liquidity, firm size, inflation and exchange rate, while the regression analysis indicates the effect of the independent variables on the dependent variables.&#13;
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                <text>Due to the direct impact of manufacturing firms’ performance to economic stability, there is the need for special attention to examine factors that affect the manufacturing firms’ profitability (Akbas &amp; Karadinan 2012).&#13;
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                <text>In this paper, we assessed volatility of Ghana’s inflation rates for 2000 to 2018 using the auto-regressive conditionally heteroskedasticity (ARCH), generalized ARCH (GARCH), and the exponential GARCH (EGARCH) models. The inflation data were obtained from the Ghana Statistical Service (GSS). The proposed model should be able to provide projections of inflation volatility from 2019 and beyond. The results showed that higher order models are required to properly explain Ghana’s inflation volatility and the EGARCH(12, 1) is the best fitting model for the data. The EGARCH(12, 1) model is robust to model and forecast volatility of inflation rates. Also, the results suggest that we are forecasting increasing volatility and there is increasing trend in general prices of goods and services for 2018 and beyond. The forecasts figures revealed that Ghana’s economy is likely to be unstable in 2018 and 2019. This study therefore recommends that policy makers and industry players need to put in place stringent monetary and fiscal policies that would put the anticipated increase in inflation under control. The models were implemented using R software.</text>
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                <text>Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome</text>
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                <text>In this study, we jointly modeled longitudinal CD4 count data and survival outcome (time-to-first occurrence of composite outcome of death, cardiac tamponade or constriction) in other to investigate the effects of Mycobacterium indicus pranii immunotherapy and the CD4 count measurements on the hazard of the composite outcome among patients with HIV and tuberculous (TB) pericarditis. In this joint modeling framework, the models for longitudinal and the survival data are linked by an association structure. The association structure represents the hazard of the event for 1-unit increase in the longitudinal measurement. Models fitting and parameter estimation were carried out using R version 4.2.3. The association structure that represents the strength of the association between the hazard for an event at time point j and the area under the longitudinal trajectory up to the same time j provides the best fit. We found …</text>
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