Regression Approach for GDP Prediction Using Multiple Features From Macro-Economic Data

Abstract

Gross domestic product is the main measure used predominantly for assessing the wealth and growth of a country. Previous works used the amount of CO2 emitted by a country in predicting the gross domestic product growth of that quarter. Though it is a valid indicator, there are many other features that can be considered while calculating the gross domestic product of a country. In this paper, an approach to predict gross domestic product utilizing many features is introduced. Macroeconomic data like unemployment rate, gold rate, foreign exchange rate, and other important data to plot the graph are used for linear regression, employing dimensionality reduction to analyze and extract only the important features and thereby increasing the effectiveness of the proposed GDP prediction. Since data has been published in different time intervals, preprocessing like interpolation, reshaping, and dimensionality reduction using PCA are carried out to make the proposed GDP prediction model more precise and accurate, and the maximum accuracy of 95% is obtained.