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What is Machine Learning for GDP Growth Forecasts about?

This working paper investigates the effectiveness of machine learning methods, particularly Gradient-Boosted Regression Trees, Random Forests, and Multi-Layer Perceptron models, in forecasting Swedish quarterly GDP growth using Economic Tendency Survey data. The study finds that machine learning models generally outperform traditional linear indicator models in terms of forecast accuracy. The paper provides a comprehensive analysis of various forecasting models and their performance metrics, contributing to

Author

chen2694

Language

EN