Understanding the Support of Savings to Income: A Multivariate Adaptive Regression Splines Analysis
The understanding of the complex socio-economic phenomena requires a deep insight of the dynamics through several correlated variables. Our aim is to demonstrate how some relevant macroeconomic variables could affect the evolution of the economic development path. This research arises in a broader analysis on the role of the wealth of households held in specific forms unexploited and potentially useful if properly integrated in productive cycles. At the basis of these assumptions, however, there are complex dynamics on the formation and composition of wealth, which does not include business capital and savings in the bank channel. We want to demonstrate the utility of aggregate savings for the composition and the very existence of wealth, which can be stimulated through policy instruments. We need to ask how to prove empirically that, in modern economies, encouraging savings and the accumulation of private wealth represent, under certain conditions, a not fully considered support to economic development. In this phase we test if statistical techniques inspired by Artificial Intelligence can be better exploited respect to classic approaches, given the quality of the data available. We use multivariate adaptive regression splines model, in a comparison with a multivariate model, to examine the relationship of aggregate savings, and then other related variables, on GDP in the US for the period 1970-2012.
Keywordsartificial intelligence data mining multivariate adaptive regression splines model aggregate savings economic growth and development
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