Forecasting Dutch inflation using machine learning methods

Robert-Paul Berben, Rajni Rasiawan and Jasper de Winter

Working Papers from DNB

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C22 C53 C55 E17 E31 (search for similar items in EconPapers)
Date: 2025-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:dnb:dnbwpp:828

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