Spills by multinational petroleum corporations have scarred the Niger Delta and as a result, many residents are relocating, hence, the rise of abandoned idled, and neglected infrastructures. The purpose of this study is to understand how neglected infrastructures have shaped the living conditions of people in the Niger Delta communities. Employing a phenomenological case study approach, this paper focuses on the lived experiences of the Niger Delta people of Nigeria to explain the correlation between oil exploration activities and abandoned or neglected infrastructures. Contributing to the theoretical perspective on sustainability, development, and infrastructural issues, this study finds that corruption, poor governance, lack of accountability, laws, finances, resources, and education are also factors promoting the rise of abandoned infrastructures in the Niger Delta. The findings of this study provide crucial insights to government, policymakers, and environmental justice advocates who may consider using the discussions to address socio-economic issues in developing countries.
Objective We explain evolving policy choices made by all 50 states in response to the coronavirus (COVID‐19) pandemic in the United States against the background of each state's political and public health landscape. Method We create an index of eight state preventative measures and explain variation in that index by infection and death rates, vaccination rates, support for President Trump in 2020, and the political party of the governor. We control for population density and the health vulnerability of each state. Results State response was largely driven by three factors: the death rate from COVID‐19, Trump votes in 2020 and Republican control of the governorship. Conclusion Understanding state response to the pandemic requires going beyond a partisan lens and considering the shifting onus of responsibility for taking action to protect against the virus from states to individual citizens in an increasingly politicized sphere of pandemic response.
Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy under student’s t error distribution. The asymmetric effect captured by the parameter of ARMA (1,1) with TGARCH (1,1), APARCH (1,1) and EGARCH (1,1) models shows that negative shocks or bad news create more volatility than positive shocks or good news. The study also provides evidence that student’s t distribution for errors improves forecasting accuracy. With such an error distribution assumption, ARMA (1,1)-IGARCH (1,1) is considered the best for out-of-sample volatility forecasting.
Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy under student’s t error distribution. The asymmetric effect captured by the parameter of ARMA (1,1) with TGARCH (1,1), APARCH (1,1) and EGARCH (1,1) models shows that negative shocks or bad news create more volatility than positive shocks or good news. The study also provides evidence that student’s t distribution for errors improves forecasting accuracy. With such an error distribution assumption, ARMA (1,1)-IGARCH (1,1) is considered the best for out-of-sample volatility forecasting.
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