In this article we estimate the “Imports of Goods” in European countries in the period 2010-2019 for 28 countries. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS. Our results show that “Imports of Goods” is negatively associated with “Private Consumption Expenditure at Current Prices”, “Consumption of Fixed Capital”, and “Gross Domestic Product” and positively associated with “Harmonised Consumer Price Index” and “Gross Operating Surplus: Total Economy”. Finally, we compare a set of predictive models based on different machine learning techniques using RapidMiner, and we find that “Gradient Boosted Trees”, “Random Forest”, and “Decision Tree” are more efficient than “Deep Learning”, “Generalized Linear Model” and “Support Vector Machine”, in the sense of error minimization, to forecast the degree of “Imports of Goods”.
Estimation and Machine Learning Prediction of Imports of Goods in European Countries in the Period 2010-2019
Alberto Costantiello
;Lucio Laureti
;
2021-01-01
Abstract
In this article we estimate the “Imports of Goods” in European countries in the period 2010-2019 for 28 countries. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS. Our results show that “Imports of Goods” is negatively associated with “Private Consumption Expenditure at Current Prices”, “Consumption of Fixed Capital”, and “Gross Domestic Product” and positively associated with “Harmonised Consumer Price Index” and “Gross Operating Surplus: Total Economy”. Finally, we compare a set of predictive models based on different machine learning techniques using RapidMiner, and we find that “Gradient Boosted Trees”, “Random Forest”, and “Decision Tree” are more efficient than “Deep Learning”, “Generalized Linear Model” and “Support Vector Machine”, in the sense of error minimization, to forecast the degree of “Imports of Goods”.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
