Author Description

We Develop A Framework To Nowcast (and Forecast) Economic Variables With Machine Learning Techniques. We Explain How Machine Learning Methods Can Address Common Shortcomings Of Traditional Ols-based Models And Use Several Machine Learning Models To Predict Real Output Growth With Lower Forecast Errors Than Traditional Models. By Combining Multiple Machine Learning Models Into Ensembles, We Lower Forecast Errors Even Further. We Also Identify Measures Of Variable Importance To Help Improve The Transparency Of Machine Learning-based Forecasts. Applying The Framework To Turkey Reduces Forecast Errors By At Least 30 Percent Relative To Traditional Models. The Framework Also Better Predicts Economic Volatility, Suggesting That Machine Learning Techniques Could Be An Important Part Of The Macro Forecasting Toolkit Of Many Countries. Marijn Bolhuis, Brett Rayner.