Using time-series data of Pakistan from 1990 to 2019, this study explores the asymmetric effects of political instability on clean energy consumption and CO2 emissions. The results from the traditional ARDL model show that political stability lessens environmental damage by reducing CO2 emissions in the long run. However, when we used the nonlinear ARDL approach we found that political instability reduces the consumption of clean energy but also leads to damage environmental quality in long run in Pakistan. While political stability increases the consumption of clean energy but also helps to improve environmental quality in the short run in Pakistan. Thus, macroeconomic policies to promote expansion in clean energy consumption will directly stimulate green economic growth and environmental quality.
Using time-series data of Pakistan from 1990 to 2019, this study explores the asymmetric effects of political instability on clean energy consumption and CO2 emissions. The results from the traditional ARDL model show that political stability lessens environmental damage by reducing CO2 emissions in the long run. However, when we used the nonlinear ARDL approach we found that political instability reduces the consumption of clean energy but also leads to damage environmental quality in long run in Pakistan. While political stability increases the consumption of clean energy but also helps to improve environmental quality in the short run in Pakistan. Thus, macroeconomic policies to promote expansion in clean energy consumption will directly stimulate green economic growth and environmental quality.
The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting.
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