International Journal of Forecasting最新文献

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Beyond the numbers: The role of people and processes in central bank forecasting 数字之外：人和流程在央行预测中的作用

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-11-19 DOI: 10.1016/j.ijforecast.2025.11.001

Nikolaos Kourentzes , Robert Fildes

We complement the previous discussions of Bernanke’s review of the Bank of England’s forecasting activities and highlight directions for future research that are relevant to central banks and the wider forecasting community. Decisions in central banks, such as monetary policy ones, are hardly algorithmic and are often influenced by policy and current soft contextual information, introducing challenges into evaluating and specifying forecasts. The use of alternatives to standard econometric models is highlighted in the Bernanke report and other commentaries in this series. These methodological alternatives require both more research, to be validly applied and evaluated, and a cultural shift for those with forecasting responsibilities in central banks. Critically, uncertainty estimates in central bank forecasts are hardly purely model-based. How this is done and how to best communicate it to stakeholders and counterparties are fertile areas for research with potentially important implications for market participants. Finally, while academic research often focuses on large, well-funded central banks, there is a significant opportunity to help smaller, less-resourced institutions.

我们补充了伯南克之前对英国央行预测活动的评论，并强调了与央行和更广泛的预测界相关的未来研究方向。中央银行的决策，如货币政策决策，几乎不受算法影响，往往受到政策和当前软背景信息的影响，给评估和具体预测带来了挑战。伯南克的报告和本系列的其他评论都强调了标准计量经济学模型的替代方法。这些替代方法既需要更多的研究，才能得到有效的应用和评估，也需要央行负责预测的人员进行文化转变。关键是，央行预测中的不确定性估计几乎完全不是基于模型的。如何做到这一点，以及如何最好地与利益相关者和交易对手沟通，是研究的肥沃领域，可能对市场参与者产生重要影响。最后，虽然学术研究的重点往往是资金充足的大型央行，但帮助规模较小、资源不足的机构也有很大的机会。

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引用次数: 0

Editorial and introduction to the special section on the Bernanke’s review of the Bank of England’s forecasting activities 伯南克对英国央行预测活动的评论特别部分的社论和引言

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-11-18 DOI: 10.1016/j.ijforecast.2025.11.002

Pierre Pinson

引用次数: 0

Forecasting for monetary policy 货币政策预测

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-10-09 DOI: 10.1016/j.ijforecast.2025.05.003

Laura Coroneo

This paper discusses three key themes in forecasting for monetary policy highlighted in the Bernanke (2024) review: the challenges in economic forecasting, the conditional nature of central bank forecasts, and the importance of forecast evaluation. In addition, a formal evaluation of the Bank of England’s inflation forecasts indicates that, despite the large forecast errors in recent years, they were still accurate relative to common benchmarks.

本文讨论了伯南克（2024）评论中强调的货币政策预测的三个关键主题：经济预测中的挑战、央行预测的条件性质以及预测评估的重要性。此外，对英国央行（Bank of England）通胀预测的正式评估表明，尽管近年来预测误差很大，但相对于普通基准而言，它们仍然是准确的。

引用次数: 0

Could the Bank of England have avoided mis-forecasting UK inflation during 2021–24? 英国央行（Bank of England）本可以避免错误预测2021 - 2024年英国通胀吗？

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-10-08 DOI: 10.1016/j.ijforecast.2025.07.001

Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry

The Bank of England badly mis-forecast UK annual consumer price inflation as it rose rapidly from 2021, prompting a review by Ben Bernanke. This raised many important issues, but other crucial problems were not addressed, as we discuss. Unpredictable shocks explain some of the bank’s forecast failures, but tardy reactions also mattered. We show that successive large and increasing same-sign one-step-ahead forecast errors contain the information to estimate broken trends, applied to forecasting the UK’s inflation over 2021–24. Compared with Bank of England projections, substantial gains in forecast performance can be made by rapidly detecting trend breaks and updating forecasting models when they occur.

英国央行（Bank of England）严重错误地预测了英国消费者价格指数（cpi）从2021年开始迅速上升，促使本•伯南克（Ben Bernanke）重新审视。这提出了许多重要的问题，但其他关键问题没有得到解决，正如我们讨论的那样。不可预测的冲击解释了该行预测失败的部分原因，但反应迟缓也很重要。我们表明，连续的大且不断增加的同号一步预测误差包含了估计断裂趋势的信息，应用于预测英国2021-24年的通货膨胀。与英国央行（Bank of England）的预测相比，通过快速发现趋势突变并在突变发生时更新预测模型，预测业绩可以大幅提升。

引用次数: 0

Benchmarking M6 competitors: An analysis of financial metrics and discussion of incentives M6竞争对手的基准：财务指标的分析和激励的讨论

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-08-29 DOI: 10.1016/j.ijforecast.2025.03.008

Matthew J. Schneider , Rufus Rankin , Prabir Burman , Alexander Aue

The M6 Competition assessed the performance of competitors using a ranked probability score and an information ratio. While these metrics do well at picking the winners in the competition, crucial questions remain for investors with longer-term incentives. To address these questions, we compare the competitors’ performance with a number of conventional (long-only) and alternative indices using industry-relevant metrics. We apply factor models to measure the competitors’ value-adds above industry-standard benchmarks and find that competitors with more extreme performance are less dependent on the benchmarks. We further introduce two new strategies by picking the competitors with the best (Superstars) and worst (Superlosers) recent performance and show that it is challenging to identify skill amongst investment managers. Finally, we discuss the incentives of winning the competition compared with professional investors, where investors wish to maximize fees over an extended period of time, and provide suggestions for future competition improvements.

M6竞赛使用排序概率得分和信息比率来评估竞争者的表现。虽然这些指标能很好地挑选出竞争中的赢家，但对于有长期动机的投资者来说，仍存在一些关键问题。为了解决这些问题，我们将竞争对手的表现与一些传统（只做多）和使用行业相关指标的替代指数进行了比较。我们运用因子模型来衡量竞争对手在行业标准基准之上的附加价值，发现表现越极端的竞争对手对基准的依赖程度越低。我们进一步介绍了两种新的策略，通过挑选最近表现最好（超级明星）和最差（超级失败者）的竞争对手，并表明在投资经理中识别技能是具有挑战性的。最后，我们讨论了与专业投资者相比，赢得竞争的动机，投资者希望在较长一段时间内最大化费用，并为未来的竞争改进提供建议。

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引用次数: 0

Adaptive forecasting in dynamic markets: An evaluation of AutoTS within the M6 competition 动态市场中的自适应预测：AutoTS在M6竞争中的评价

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-08-28 DOI: 10.1016/j.ijforecast.2025.08.004

Colin Catlin

In contemporary forecasting, the challenges of navigating the intricacies of erratic human-induced patterns combine with the challenges of navigating the overwhelming number of methods and models available to manage these data. The M6 Competition, which emphasized repeated, real-time monthly forecasting of stock markets, featured many of these difficulties. Here, AutoTS, an open-source Python package designed specifically for probabilistic time series predictions, is evaluated within the context of this competition. AutoTS includes an extensive repertoire of models, augmented by robust data preprocessing utilities, and employs genetic algorithms to fine-tune model parameters, contingent upon user-delineated evaluation metrics. This study describes the deployment of AutoTS in the M6 Competition, which won the investment decision challenge, and outlines the model selection pipeline and the process of converting forecasts into decisions which produced this result. Although a single definitive model remains elusive, these findings underscore the potential value of methodologies that are dynamic and largely autonomous.

在当代的预测中，驾驭不稳定的人为模式的复杂性的挑战与驾驭大量可用的方法和模型来管理这些数据的挑战相结合。强调重复、实时的月度股市预测的M6竞赛，就有许多这样的困难。在这里，AutoTS，一个专门为概率时间序列预测设计的开源Python包，将在本次竞赛的背景下进行评估。AutoTS包括广泛的模型库，通过强大的数据预处理工具增强，并采用遗传算法微调模型参数，根据用户描述的评估指标。本研究描述了AutoTS在M6竞赛中的部署，该竞赛赢得了投资决策挑战，并概述了模型选择管道和将预测转化为产生此结果的决策的过程。虽然单一的确定模型仍然难以捉摸，但这些发现强调了动态和很大程度上自主的方法的潜在价值。

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引用次数: 0

Introduction to the M6 forecasting competition Special Issue M6预报比赛特刊简介

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-08-27 DOI: 10.1016/j.ijforecast.2025.07.006

Spyros Makridakis, Fotios Petropoulos, Evangelos Spiliotis, Norman R. Swanson

引用次数: 0

Optimal text-based time-series indices 最佳的基于文本的时间序列索引

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-08-09 DOI: 10.1016/j.ijforecast.2025.07.003

David Ardia , Keven Bluteau

We propose an approach to construct text-based time-series indices in an optimal way—typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. Our methodology relies on binary selection matrices that, applied to the vocabulary of tokens, select the relevant texts in the corpus. Various widely known text-based indices, such as the Economic Policy Uncertainty (EPU) index, can be formulated in terms of selection matrices. We design a genetic algorithm with domain-specific knowledge featuring tailor-made crossover and mutation operations to perform the complex optimization. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices that forecast inflation at various horizons.

我们提出了一种以最优方式构建基于文本的时间序列指数的方法-通常，指数最大化同期关系或相对于目标变量（如通货膨胀）的预测性能。我们的方法依赖于二进制选择矩阵，应用于标记的词汇表，选择语料库中的相关文本。各种众所周知的基于文本的指数，如经济政策不确定性（EPU）指数，都可以根据选择矩阵来制定。我们设计了一种具有特定领域知识的遗传算法，该算法具有定制的交叉和突变操作来执行复杂的优化。我们以《华尔街日报》（Wall Street Journal）的大量新闻文章为例，通过优化基于文本的指数来说明我们的方法，这些指数可以预测不同时期的通货膨胀。

引用次数: 0

When to be discrete: The importance of time formulation in the modeling of extreme events in finance 何时离散：时间公式在金融极端事件建模中的重要性

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-07-22 DOI: 10.1016/j.ijforecast.2025.06.001

Katarzyna Bień-Barkowska , Rodrigo Herrera

We propose a novel extension of the score-driven peaks-over-threshold (SPOT) model within a discrete-time framework. This adaptation is motivated by the fact that financial returns and, consequently, extreme events are typically observed at discrete time intervals. Our primary objective is to assess whether this discrete-time SPOT model provides a more precise representation and superior fit for tail risk forecasting. The study reveals several important findings. First, we demonstrate that continuous-time approaches can result in inaccurate value-at-risk and expected-shortfall forecasts. By contrast, discrete-time models provide a more accurate description of the dynamics of extreme losses. Empirical evidence supports the superiority of discrete-duration models, outperforming various continuous-time SPOT specifications and GARCH models. Overall, our study has substantial implications for the modeling and forecasting of extreme financial events, offering a more accurate and efficient approach than traditional approaches.

我们提出了在离散时间框架内分数驱动的峰值超过阈值（SPOT）模型的新扩展。这种适应的动机是这样一个事实，即金融回报和极端事件通常是在离散的时间间隔内观察到的。我们的主要目标是评估这种离散时间SPOT模型是否为尾部风险预测提供了更精确的表示和更好的拟合。这项研究揭示了几个重要的发现。首先，我们证明了连续时间方法可能导致不准确的风险价值和预期不足预测。相比之下，离散时间模型提供了对极端损失动力学更准确的描述。经验证据支持离散持续时间模型的优越性，优于各种连续时间SPOT规范和GARCH模型。总的来说，我们的研究对极端金融事件的建模和预测具有重要意义，提供了一种比传统方法更准确和有效的方法。

引用次数: 0

Deep switching state space model for nonlinear time series forecasting with regime switching 带状态切换的非线性时间序列预测的深度切换状态空间模型

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-06-30 DOI: 10.1016/j.ijforecast.2025.05.001

Xiuqin Xu, Hanqiu Peng, Ying Chen

Modern time series data often display complex nonlinear dependencies along with irregular regime-switching behaviors. These features present technical challenges in modeling, inference, and providing insightful understanding of the underlying stochastic phenomena. To tackle these challenges, we introduce the novel Deep Switching State Space Model (DS

^{3}

M). In DS

^{3}

M, the architecture employs discrete latent variables to represent regimes and continuous latent variables to account for random driving factors. By melding a Recurrent Neural Network (RNN) with a nonlinear Switching State Space Model (SSSM), we manage to capture the nonlinear dependencies and irregular regime-switching behaviors, governed by a Markov chain and parameterized using multilayer perceptrons. We validate the DS

^{3}

M through short- and long-term forecasting on a wide array of simulated and real-world datasets, spanning sectors such as healthcare, economics, traffic, meteorology, and energy. Our results reveal that DS

^{3}

M outperforms several state-of-the-art models in terms of forecasting accuracy, while providing meaningful regime identifications.

现代时间序列数据往往表现出复杂的非线性依赖关系和不规则的状态切换行为。这些特征在建模、推理和提供对潜在随机现象的深刻理解方面提出了技术挑战。为了解决这些挑战，我们引入了新的深度交换状态空间模型（DS3M）。在DS3M中，体系结构使用离散潜在变量来表示制度，使用连续潜在变量来解释随机驱动因素。通过将递归神经网络（RNN）与非线性切换状态空间模型（SSSM）融合，我们设法捕获非线性依赖关系和不规则状态切换行为，由马尔可夫链控制，并使用多层感知器参数化。我们通过对大量模拟和真实数据集的短期和长期预测来验证DS3M，这些数据集涵盖医疗保健、经济、交通、气象和能源等行业。我们的研究结果表明，DS3M在预测精度方面优于几个最先进的模型，同时提供有意义的状态识别。

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引用次数: 0

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