Forecasting wages with local linear forests

Michele Lenza and Claudia Marchini

No 3291, Working Paper Series from European Central Bank

Abstract: This study applies a Local Linear Forest (LLF) for wage forecasting in France. The LLF outperforms benchmarks such as Random Walk (RW), Ridge Regressions (RR) and Random Forests (RF). We also show that adding foreign predictors (i.e. measures of real activity, price and wage pressures from Germany and Italy) to French economic variables significantly improves wage predictions in France. The implications of our results are that wage dynamics in France exhibit non-linearity, to a certain extent, and that the better ability of LLFs relative to RFs to fit smooth signals is a valuable feature, especially at times in which wage growth reaches unprecedented levels from an historical perspective. JEL Classification: C45, C55, E37, J30

Keywords: forecasting; local linear forest; negotiated wages (search for similar items in EconPapers)
Date: 2026-10
Note: 411196
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20263291

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