%0 Journal Article %1 noauthororeditor %A Staufer, Jonathan %A Brockmann, Patricia %D 2018 %J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) %K Data Development Economic Freedom Human Index Learning Machine Mining Networks Neural World of the %N 4 %P 15 %R 10.5121/ijscai.2018.7402 %T Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysis of the Relation Between Economic Freedom and Quality of Life %U http://aircconline.com/ijscai/V7N4/7418ijscai02.pdf %V 7 %X The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic oriented economic policy, an objective recommendation for creating an economic policy that improves people’s everyday lives might be derived by the analysis results. It was found that these more advanced algorithms achieve a considerably stronger correlation between both indices than pure statistical means yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven economic policy.

@article{noauthororeditor, abstract = {The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic oriented economic policy, an objective recommendation for creating an economic policy that improves people’s everyday lives might be derived by the analysis results. It was found that these more advanced algorithms achieve a considerably stronger correlation between both indices than pure statistical means yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven economic policy. }, added-at = {2022-05-12T11:36:22.000+0200}, author = {Staufer, Jonathan and Brockmann, Patricia}, biburl = {https://www.bibsonomy.org/bibtex/283140d742a79314c2dc3562faff3525c/leninsha}, doi = {10.5121/ijscai.2018.7402}, interhash = {f3eb46374bcf7ce777bcdae05eaec1df}, intrahash = {83140d742a79314c2dc3562faff3525c}, issn = {23191015}, journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)}, keywords = {Data Development Economic Freedom Human Index Learning Machine Mining Networks Neural World of the}, language = {English}, month = {November}, number = 4, pages = 15, timestamp = {2022-05-12T11:36:22.000+0200}, title = {Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysis of the Relation Between Economic Freedom and Quality of Life }, url = {http://aircconline.com/ijscai/V7N4/7418ijscai02.pdf}, volume = 7, year = 2018 }