Forecasting Dutch inflation using machine learning methods
Published: 05 February 2025
This paper examines the performance of machine learning models in forecasting Dutch inflation over the period 2010 to 2023, leveraging a large dataset and a range of machine learning techniques. The findings indicate that certain machine learning models outperform simple benchmarks, particularly in forecasting core inflation and services inflation. However, these models face challenges in consistently outperforming the primary inflation forecast of De Nederlandsche Bank for headline inflation, though they show promise in improving the forecast for non-energy industrial goods inflation. Models employing path averages rather than direct forecasting achieve greater accuracy, while the inclusion of non-linearities, factors, or targeted predictors provides minimal or no improvement in forecasting performance. Overall, Ridge regression has the best forecasting performance in our study.
Keywords: Inflation forecasting; Big data; Machine learning; Random Forest; Ridge regression
JEL codes C22; C53; C55; E17; E31
Working paper no. 828
828 - Forecasting Dutch inflation using machine learning methods
Research highlights:
- This paper examines the usefulness of machine learning (ML) models for forecasting Dutch HICP inflation.
- We find that some ML models can outperform simple benchmarks, particularly for core and services inflation.
- The forecast accuracy of ML models is mostly lower than that of DNB’s official inflation forecast.
- Some ML model, particularly Ridge regressions, generate forecasts for non-energy industrial goods inflation that are superior to DNB’s forecast.
- We find that path average forecasts tend to be more accurate than direct forecasts.
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