The New Grammar of Living Model (NGL) is an attempt to forecast the future of the world economy, countries and economic blocs based upon an integrated, interdependent and holistic structured model premised upon the triad of the important planks, namely, social, economic and climate / environment. The NGL incorporates a wide spectrum of causal critical factors which are intertwined with cross impacts.
The NLG will be capturing cross sectional time series data in its most complex version. The model will be capable of combining the noise and signals of the factors across several disciples into weighted rank scores for any point of time, and also projections over a period of time. Based upon these, the model will be to forecast the position obtaining at future points of time, say, 2025, 2050. The prediction will be both in term of the country as a whole encapsulating all the critical factors, and as well as for each of he factors.
Each of the factors will be assigned appropriate weights based upon its criticality, whether its a necessary and sufficient condition. Weights will be flexible so as to vary across countries based upon their relative criticality with respect to local relevant situations obtaining. Once there is sufficient data, factor analysis will help in arriving at appropriate weights.
The scores of various countries can be normalized by dividing by cumulative weights in order reduce all countries, large or small, rich or poor, to a standardized score which irons out all biases due to size.
The NLG will be capturing cross sectional time series data in its most complex version. The model will be capable of combining the noise and signals of the factors across several disciples into weighted rank scores for any point of time, and also projections over a period of time. Based upon these, the model will be to forecast the position obtaining at future points of time, say, 2025, 2050. The prediction will be both in term of the country as a whole encapsulating all the critical factors, and as well as for each of he factors.
Each of the factors will be assigned appropriate weights based upon its criticality, whether its a necessary and sufficient condition. Weights will be flexible so as to vary across countries based upon their relative criticality with respect to local relevant situations obtaining. Once there is sufficient data, factor analysis will help in arriving at appropriate weights.
The scores of various countries can be normalized by dividing by cumulative weights in order reduce all countries, large or small, rich or poor, to a standardized score which irons out all biases due to size.
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