ARFIMA models generated an enormous amount of interest in the literature about three decades ago. However, this interest vaned after Granger (1999) showed that an ARFIMA process might have stochastic properties that do not mimic the properties of the data at all. The empirical results of our research in which we used exchange rate data for the analysis, show that a variant of an ARFIMA process indeed can beat the ARFIMA, the Random Walk and the ARMA process of the order one in out of sample forecasting. This indirectly indicates that our variant of the ARFIMA process can be considered as the data generating process for the long memory time series.
This paper analyses [1] the relative impact of housing affordability, housing prices and gross domestic product on housing glut, [2] the effects of housing glut on the health of housing market and then [3] suggestion of solutions to mitigate the risks of housing bubble bursting. Results show that housing affordability and housing price exert very mild effect on housing glut contrary to the common belief that these two factors have significant effect on housing glut. In terms of number, our results show that economic growth contributes about 0.15 negative impact on housing glut for every unit increase in economic growth while each unit increase in housing price can increase housing glut as much as 0.0054 unit.
Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are nally making in road to provide cheap and clean energy sources to rural population. As a result, literature on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to ful l this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least square structural equation modeling (PLS-SEM) and arti cial neural network (ANN) have been applied to satisfy both linear and nonlinear regression assumption respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results con rm that Branding, Economic and Altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality and ease of use are also important. The results offer wide ranging theoretical and managerial implications.
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