Forecasting Inflation from Disaggregated Data: The Colombian case

Martinez-Rivera, Wilmer; González-Molano, Eliana Rocío; Caicedo-García, Edgar

Forecasting Inflation from Disaggregated Data: The Colombian case

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Borradores de Economía; No.1251

Date published

2023-10-09

Authors

Martinez-Rivera, Wilmer
González-Molano, Eliana Rocío
Caicedo-García, Edgar

Document language

eng

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Las opiniones contenidas en el presente documento son responsabilidad exclusiva de los autores y no comprometen al Banco de la República ni a su Junta Directiva.

The opinions contained in this document are the sole responsibility of the author and do not commit Banco de la República or its Board of Directors.

Abstract

Based on monthly disaggregated Consumer Price Index (CPI) item series and macroeconomic series, we explore the advantages of forecast inflation from a disaggregated to an aggregated level by aggregating the forecasts. We compare the performance of this approach with the forecast obtained modeling aggregated inflation directly. For the aggregate level, we implement some of the techniques and models, helpful to work with many predictors, such as dimension reduction, shrinkage methods, and machine learning models. Also, we implement traditional time-series models. For the disaggregated data, we use its lags and a set of macroeconomic variables as explanatory variables. Direct and recursive forecast techniques are also explored. The sample period of the analysis is from 2011 to 2022, with forecasting and evaluation out of the sample from 2017. In addition, we evaluate the forecast accuracy during the COVID-19 period. We found a reduction in the forecast error from the disaggregate analysis over the aggregate one.

Description

Con base en índice de precios al consumidor (IPC) a nivel desagregado y de información macroeconómica mensual, nosotros estudiamos las ventajas de pronosticar a nivel desagregado para luego agregar pronósticos y comparar con los pronósticos obtenidos al analizar los índices de precios a nivel agregado. El cálculo de pronósticos esta basado en el ajuste de modelos y técnicas que incluyen modelos de reducción de dimensión, modelos de selección de variables, modelos de Machine Learning así como modelos tradicionales de series de tiempo. Además, se usan las técnicas del cálculo de pronósticos iterativos y directos. El periodo muestral de análisis es 2011 a 2022 cuyo cálculo de pronósticos fuera de muestra se realiza a partir de 2017 hasta 2022. Nosotros encontramos que por medio del enfoque de análisis propuesto se logra reducir el error de pronóstico frente al desempeño analizando los índices agregados.