Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns.This study uses monthly data from India...Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns.This study uses monthly data from India for the period from April 1994 to July 2018 to examine the long-run relationship between the stock market and macroeconomic variables.The empirical findings suggest that standard cointegration tests fail to identify any relationship among these variables.However,a transformation that extracts the actual functional relationship between these variables using the alternating conditional expectations algorithm of(J Am Stat Assoc 80:580–598,1985)identifies strong evidence of cointegration and indicates nonlinearity in the long-run relationship.Further,the continuous partial wavelet coherency model identifies strong coherency at a lower frequency for the transformed variables,establishing the fact that the long-run relationship between stock prices and macroeconomic variables in India is nonlinear and time-varying.This evidence has far-reaching implications for understanding the dynamic relationships between the stock market and macroeconomic variables.展开更多
Precipitation and deposition of asphaltene have undesirable effects on the petroleum industry by increasing operational costs due to reduction of well productivity as well as catalyst poisoning.Herein we propose a rel...Precipitation and deposition of asphaltene have undesirable effects on the petroleum industry by increasing operational costs due to reduction of well productivity as well as catalyst poisoning.Herein we propose a reliable model for quantitative estimation of asphaltene precipitation.Scaling equation is the most powerful and popular model for accurate prediction of asphaltene precipitated out of solution in crudes without regard to complex nature of asphaltene.We employed a new mathematical-based approach known as alternating conditional expectation(ACE)technique for combining results of different scaling models in order to increase the accuracy of final estimation.Outputs of three well-known scaling equations,including Rassamdana(RE),Hu(HU),and Ashoori(AS),are input to ACE and the final output is produced through a nonlinear combination of scaling equations.The proposed methodology is capable of significantly increasing the precision of final estimation via a divide-andconquer principle in which ACE functions as the combiner.Results indicate the superiority of the proposed method compared with other individual scaling equation models.展开更多
文摘Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns.This study uses monthly data from India for the period from April 1994 to July 2018 to examine the long-run relationship between the stock market and macroeconomic variables.The empirical findings suggest that standard cointegration tests fail to identify any relationship among these variables.However,a transformation that extracts the actual functional relationship between these variables using the alternating conditional expectations algorithm of(J Am Stat Assoc 80:580–598,1985)identifies strong evidence of cointegration and indicates nonlinearity in the long-run relationship.Further,the continuous partial wavelet coherency model identifies strong coherency at a lower frequency for the transformed variables,establishing the fact that the long-run relationship between stock prices and macroeconomic variables in India is nonlinear and time-varying.This evidence has far-reaching implications for understanding the dynamic relationships between the stock market and macroeconomic variables.
文摘Precipitation and deposition of asphaltene have undesirable effects on the petroleum industry by increasing operational costs due to reduction of well productivity as well as catalyst poisoning.Herein we propose a reliable model for quantitative estimation of asphaltene precipitation.Scaling equation is the most powerful and popular model for accurate prediction of asphaltene precipitated out of solution in crudes without regard to complex nature of asphaltene.We employed a new mathematical-based approach known as alternating conditional expectation(ACE)technique for combining results of different scaling models in order to increase the accuracy of final estimation.Outputs of three well-known scaling equations,including Rassamdana(RE),Hu(HU),and Ashoori(AS),are input to ACE and the final output is produced through a nonlinear combination of scaling equations.The proposed methodology is capable of significantly increasing the precision of final estimation via a divide-andconquer principle in which ACE functions as the combiner.Results indicate the superiority of the proposed method compared with other individual scaling equation models.
