The benefits of forecasting inflation with machine learning: New evidence

IF 2.3 3区经济学 Q2 ECONOMICS

Journal of Applied Econometrics Pub Date : 2024-08-08 DOI:10.1002/jae.3088

A. Naghi, Eoghan O'Neill, Martina Danielova Zaharieva

引用次数: 0

Abstract

Medeiros et al. (2021) (Journal of Business & Economic Statistics, 39:1, 98–119) find that random forest (RF) outperforms US inflation forecasting benchmarks. We replicate the main results in Medeiros et al. (2021) and (1) considerably expand the set of machine learning methods, (2) analyse the predictive ability of both the initial and extended sets of methods on Canadian and UK data, (3) add results on coverage rates and widths of prediction intervals and (4) extend the sample from January 2016 to October 2022. Our narrow replication confirms the main findings of the original paper. However, the wider replication results suggest that other methods are competitive with RF and often more accurate. In addition, RF produces disappointing results during the coronavirus pandemic and subsequent high inflation of 2020–2022, whereas a stochastic volatility model and some gradient boosting methods produce more accurate forecasts.

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用机器学习预测通货膨胀的好处：新证据

Medeiros 等人（2021 年）（《商业与经济统计期刊》，39:1, 98-119）发现随机森林（RF）优于美国通货膨胀预测基准。我们复制了 Medeiros 等人（2021 年）的主要结果，并（1）大幅扩展了机器学习方法集，（2）分析了初始方法集和扩展方法集对加拿大和英国数据的预测能力，（3）增加了预测区间的覆盖率和宽度结果，（4）将样本从 2016 年 1 月扩展到 2022 年 10 月。我们的狭义复制证实了原论文的主要结论。然而，更广泛的复制结果表明，其他方法也能与 RF 相媲美，而且往往更准确。此外，在冠状病毒大流行和随后的 2020-2022 年高通胀期间，RF 得出的结果令人失望，而随机波动率模型和一些梯度提升方法则得出了更准确的预测。

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

Journal of Applied Econometrics Multiple-

CiteScore

3.70

自引率

4.80%

发文量

期刊介绍： The Journal of Applied Econometrics is an international journal published bi-monthly, plus 1 additional issue (total 7 issues). It aims to publish articles of high quality dealing with the application of existing as well as new econometric techniques to a wide variety of problems in economics and related subjects, covering topics in measurement, estimation, testing, forecasting, and policy analysis. The emphasis is on the careful and rigorous application of econometric techniques and the appropriate interpretation of the results. The economic content of the articles is stressed. A special feature of the Journal is its emphasis on the replicability of results by other researchers. To achieve this aim, authors are expected to make available a complete set of the data used as well as any specialised computer programs employed through a readily accessible medium, preferably in a machine-readable form. The use of microcomputers in applied research and transferability of data is emphasised. The Journal also features occasional sections of short papers re-evaluating previously published papers. The intention of the Journal of Applied Econometrics is to provide an outlet for innovative, quantitative research in economics which cuts across areas of specialisation, involves transferable techniques, and is easily replicable by other researchers. Contributions that introduce statistical methods that are applicable to a variety of economic problems are actively encouraged. The Journal also aims to publish review and survey articles that make recent developments in the field of theoretical and applied econometrics more readily accessible to applied economists in general.