Forecasting Inflation From Disaggregated Data

IF 1.5 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2025-07-06 DOI:10.1002/asmb.70023

Wilmer Martínez-Rivera, Eliana González-Molano, Edgar Caicedo-Garcia

引用次数: 0

Abstract

We forecast inflation aggregates for the United States, the United Kingdom, and Colombia using forecasts aggregation of disaggregates and forecasts obtained directly from the aggregate. We implement helpful models for many predictors, such as dimension reduction, shrinkage methods, machine learning models, and traditional time-series models (ARIMA and TAR). We evaluate out-sample forecasts for the period before COVID-19 and the period afterward. It was found that the aggregation of forecasts performs as well as the forecast using the aggregate directly. In some cases, there is a reduction in the forecast error from the disaggregate analysis.

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从分类数据预测通胀

我们预测了美国、英国和哥伦比亚的通货膨胀总量，使用的是分类汇总的预测和直接从总量中获得的预测。我们为许多预测器实现了有用的模型，例如降维、收缩方法、机器学习模型和传统的时间序列模型（ARIMA和TAR）。我们评估了COVID-19之前和之后时期的样本外预测。结果表明，集合预测的效果与直接使用集合预测的效果相当。在某些情况下，通过分解分析可以减少预测误差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Stochastic Models in Business and Industry 数学-数学跨学科应用

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.