Predicting gross domestic product to macroeconomic indicators

Abstract

Macroeconomic indicators enable countries to concentrate on goods, services, and other entities that grow their Gross Domestic Product (GDP). Often, identifying these groups of indicators poses a challenge to nations. The study considered a typical data set with two main objectives. First, to predict GDP to macroeconomic indicators by applying four machine learning methods namely, Principal Component Regression (PCR), Ridge Regression (RR), Lasso Regression (LR), and Ordinary Least Squares (OLS). Second, identify the most likely key macroeconomic variables that could affect the growth of GDP. The methods were evaluated using 5-fold cross-validation, and the estimated coefficients associated with the macroeconomic indicators were computed. The results revealed that PCR method with an accuracy of 89% and a mean square error of -7.552007365635066e+21 predicted GDP to macroeconomic indicators accurately, more than other methods. Some macroeconomic indicators did affect GDP positively, while others did not. The major contribution of the study is the use of machine learning regularization methods to predict GDP instead of the traditional statistical methods. It also identified additional macroeconomic variables to compute real GDP.

Keywords

• Machine learning
• Regression techniques
• Coefficient estimates
• Macroeconomic indicators
• Gross domestic product