Machine Learning Methods for Multi-Horizon Inflation Forecasting: A Comparative Analysis for Costa Rica

Esteban Sánchez-Gómez ()
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Esteban Sánchez-Gómez: Economic Division, Central Bank of Costa Rica

No 2606, Documentos de Trabajo from Banco Central de Costa Rica

Abstract: This paper evaluates the performance of machine learning (ML) methods for forecasting year-over-year inflation in Costa Rica using monthly data from 2012-2025 and compares their performance against standard benchmarks within a rolling out-of-sample framework. ML techniques are particularly useful for capturing nonlinearities and complex interactions between inflation and a broad set of macroeconomic covariates. The results show that nonlinear ensemble methods such as XGBoost and BART provide the strongest gains at short horizons, while linear shrinkage methods are more competitive at longer horizons. ***Resumen: Este documento evalúa el desempeño de los métodos de aprendizaje automático (ML) para pronosticar la inflación interanual en Costa Rica, utilizando datos mensuales de 2012 a 2025 y comparándolos con estándares de referencia dentro de un esquema de muestra móvil fuera de muestra. Las técnicas de ML son especialmente útiles para captar no linealidades e interacciones complejas entre la inflación y un amplio conjunto de variables macroeconómicas. Los resultados muestran que los métodos de ensamblaje no lineal como XGBoost y BART presentan los mayores beneficios en horizontes cortos, mientras que los métodos de reducción lineal son más competitivos en horizontes largos.

Keywords: Inflation; Macroeconomic Forecasting; Machine Learning; Monetary Policy; Hybrid Models; inflación; Pronósticos; Aprendizaje automático; Política Monetaria; Modelos Híbridos. (search for similar items in EconPapers)
JEL-codes: C33 C53 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2026-08
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Persistent link: https://EconPapers.repec.org/RePEc:apk:doctra:2606

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