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Forecasting wages with local linear forests

Author

Listed:
  • Lenza, Michele
  • Marchini, Claudia

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

Suggested Citation

  • Lenza, Michele & Marchini, Claudia, 2026. "Forecasting wages with local linear forests," Working Paper Series 3291, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20263291
    Note: 411196
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General

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