Adaptive Trees: a new approach to economic forecasting

Nicolas Woloszko

No 1593, OECD Economics Department Working Papers from OECD Publishing

Abstract: The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the continuous change in the distribution of economic variables). The forecast methodology aims at addressing these challenges. The algorithm is said to be “adaptive” insofar as it adapts to the quantity of structural change it detects in the economy by giving more weight to more recent observations. The performance of the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six major economies (USA, UK, Germany, France, Japan, Italy). The performance of Adaptive Trees is on average broadly similar to forecasts obtained from the OECD’s Indicator Model and generally performs better than a simple AR(1) benchmark model as well as Random Forests and Gradient Boosted Trees.

Keywords: business cycles; concept drift; feature engineering; forecasting; GDP growth; interpretable AI; machine learning; short-term forecasts; structural change (search for similar items in EconPapers)
JEL-codes: C01 C18 C23 C45 C53 C63 E37 (search for similar items in EconPapers)
Date: 2020-01-16
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:oec:ecoaaa:1593-en

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