Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists’ Predictions (original) (raw)
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2019
Timely alternative data sources such as credit card transactions and search query trends have become more readily available in recent years, while sophisticated machine learning (ML) techniques have enabled marked gains in predictive accuracy. These advances offer the benefit of revealing economic news earlier in the estimation cycle, reducing revisions, and improving estimate quality. But which combinations of data and ML techniques give the most accurate prediction of national economic activity? To answer this question, we conduct a prediction horse race using a one-step ahead model validation design to evaluate how each ML algorithm, data set, and variable selection method weighs on predictive accuracy. We test 73,884 model specifications, consider 1,180 variables drawn from both traditional and alternative sources, and predict 188 quarterly revenue and expenditure series for the services sector as published in the Quarterly Service Survey (QSS)—a key data set that accounts for n...
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The Artificial Regression Market
arXiv preprint arXiv:1204.4154, 2012
Abstract: The Artificial Prediction Market is a recent machine learning technique for multi-class classification, inspired from the financial markets. It involves a number of trained market participants that bet on the possible outcomes and are rewarded if they predict correctly. This paper generalizes the scope of the Artificial Prediction Markets to regression, where there are uncountably many possible outcomes and the error is usually the MSE. For that, we introduce the reward kernel that rewards each participant based on its prediction ...