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The recent failure of commonly accepted, inductive, econometric models to provide insights into real, macroeconomic phenomenon during economic crises has provoked a debate concerning contemporary econometric methodology. Based on the foundations laid by Haavelmo, and Hollis and Nell, an assessment of Edward J. Nell’s (1998) “unifying methodological framework” (UMF) is offered. Nell’s UMF places socioeconomic institutions and interdependencies, and technological realities as basis of analysis. Using “conceptual analysis” and “fieldwork” Nell presents an alternative to generally accepted, mainstream, econometric methodology. The purpose of this paper is to look at some examples of the way, and this can help develop useful theory and improve macroeconometric model building. Applying Nell’s UMF to unemployment, inflation, and production reveals a methodological advance that promises more realistic insights into macroeconomic phenomena than is offered by contemporary, mainstream, econometric models.
Climate Pollution due to the Carbon Emission (CO2) from the different fossil fuels is
considered as a great and important international challenge to many researchers.
In this paper we are providing a solution to forecast the poison CO2 gas emerged from energy consumption. Four inputs data were considered the
global oil, natural gas, coal, and primary energy consumption to build our
system. In this paper, we used the Artificial Neural Network (ANN) as successful and powerful
tool in handling a time series modeling problem. The proposed ANN model was
used to train and test the yearly CO2 Emission. The data were
trained from year 1982 to 2000, and tested for the year 2003 to 2010. From the
results obtained we can see that ANN performance was Excellent and proved its
efficiency as a useful tool in solving the climate pollution problems.