A Machine Learning Approach to Predicting Gross Domestic Product

There is an ever-increasing understanding of the benefits of using Machine Learning in the prediction of economic performance when compared to more traditional statistical methods. Machine Learning models can provide more accurate and timely forecasts of economic performance, which are very valuable for economists and policy advisors when making important decisions related to the future economic outlook. Gross Domestic Product (GDP) is a main indicator used in the evaluation of the performance of an economy. Traditionally, GDP was forecast using data available on a quarterly basis, such as imports, exports and government spending. Delays and issues with inaccuracy can arise with this quarterly data however, and the use of Machine Learning models has facilitated the evaluation of a wider range of economic indicators, available with more timely data, for predicting GDP. This research project creates Machine Learning models using Multilayer Perceptron - Artificial Neural Networks and Random Forests for predicting GDP. These methods have previously been successfully used for economic prediction models and this research project will build on the previous work in this area by applying the models to economic performance variables that impact GDP, for example Consumer Confidence and Business Confidence, Household Spending, Unemployment Rates, Interest Rates and Exchange Rates. The models built in this research project are evaluated in terms of accuracy and other statistical measures, and the results are promising in terms of the predicting GDP in Ireland and other countries