International Journal of Forecasting最新文献

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Is it possible to predict electoral abstention on the individual level? A preregistered test on forecasting the effects of abolishing compulsory voting in Belgium 是否有可能在个人层面上预测选举弃权？一项预先登记的测试，旨在预测比利时废除强制投票的影响

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-06-26 DOI: 10.1016/j.ijforecast.2025.05.002

Dieter Stiers, Marc Hooghe

There is a vast literature on determinants of electoral turnout that allows us to forecast which groups of the population will turn out to vote and which will not. Here we report on a rather unique forecasting experiment at the individual level. In June 2024, elections were held in Belgium with compulsory voting. In October 2024, another election was held, but this time without compulsory voting. Simultaneously, a panel survey was conducted, spanning from April to November 2024. The information in the first two waves of the panel were used to forecast the likelihood of individual respondents turning out again in October, which we preregistered. The forecasting models were indeed successful in predicting who would turn out to vote, but they tended to give relatively elevated turnout likelihood scores to non-voters. The prediction models tended to underestimate the effect of political interest in explaining actual electoral turnout.

关于投票率的决定因素有大量的文献，这些文献使我们能够预测哪些群体会投票，哪些群体不会投票。在这里，我们报告了一个在个人层面上相当独特的预测实验。2024年6月，比利时举行了强制性选举。2024年10月，又举行了一次选举，但这次没有强制投票。同时，从2024年4月到11月进行了一项小组调查。小组前两波的信息被用来预测个人受访者在10月份再次出现的可能性，这是我们预先登记的。预测模型在预测谁会投票方面确实很成功，但它们往往会给没有投票的人提供相对较高的投票可能性分数。预测模型往往低估了政治利益对解释实际投票率的影响。

引用次数: 0

Kairosis: A method for dynamical probability forecast aggregation informed by Bayesian change-point detection Kairosis：一种基于贝叶斯变点检测的动态概率预测聚合方法

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-06-23 DOI: 10.1016/j.ijforecast.2025.03.001

Zane Hassoun, Niall MacKay, Ben Powell

We present a new method, ‘kairosis’, for aggregating probability forecasts made over a time period of a single outcome determined at the end of that period. Informed by work on Bayesian change-point detection, we begin by constructing for each time during the period a posterior probability that the forecasts before and after this time are distributed differently. The resulting posterior probability mass function is integrated to give a cumulative mass function, which is used to create a weighted median forecast. The effect is to construct an aggregate in which the most heavily weighted forecasts are those which have been made since the probable most recent change in the forecasts’ distribution. Kairosis outperforms standard methods, and is especially suitable for geopolitical forecasting tournaments because it is observed to be robust across disparate questions and forecaster distributions.

我们提出了一种新的方法，“kairosis”，用于在该时期结束时确定的单一结果的一段时间内进行汇总概率预测。根据贝叶斯变化点检测的工作，我们首先为这段时间内的每个时间构建一个后验概率，该概率表明该时间前后的预测分布不同。将得到的后验概率质量函数集成为一个累积质量函数，该函数用于创建加权中位数预测。其效果是构建一个汇总，其中权重最大的预测是自预测分布最近可能发生变化以来做出的预测。Kairosis优于标准方法，特别适用于地缘政治预测比赛，因为它在不同的问题和预测者分布中都具有鲁棒性。

引用次数: 0

Forecasting and policy when “we simply do not know” “我们根本不知道”时的预测和政策

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-05-30 DOI: 10.1016/j.ijforecast.2025.04.004

Alan Kirman , Angus Armstrong , William Hynes

This paper takes the Court of the Bank of England’s Terms of Reference for the Bernanke Review seriously. We explore the underlying issue of radical uncertainty and what this means for forecasting and monetary policy-making. The only logical way to proceed is to embrace Bernanke’s suggestion that we engage ‘alternative modelling frameworks’. What might these be? We need a combination of different types of models, some with closed equilibrium solutions and others that rely on simulations that can provide different insights into what is happening in the economy. The old saying that ‘it takes a model to beat a model’ is just that. We now know that Agent Based Models can perform at least as well as equilibrium models, even on the latter’s own narrow criteria, despite the fraction of resources used in their development. If the Bank is to serve its mission of ‘promoting the good of the people of the UK’ it must start by accepting reality and not limiting itself to a single model framework as if it will somehow deliver ‘the truth’ if only it had more resources.

本文对英国央行法院对伯南克评估的职权范围进行了认真研究。我们探讨了根本不确定性的潜在问题，以及这对预测和货币政策制定意味着什么。唯一合乎逻辑的方法是接受伯南克的建议，即我们采用“替代建模框架”。这些可能是什么呢？我们需要将不同类型的模型结合起来，其中一些具有封闭的均衡解决方案，而另一些则依赖于能够对经济中正在发生的事情提供不同见解的模拟。俗话说“以模制模”就是这样。我们现在知道，基于Agent的模型至少可以和均衡模型一样出色，即使是在后者自己的狭窄标准上，尽管在它们的开发中使用了一小部分资源。如果英国央行要履行其“促进英国人民福祉”的使命，它必须从接受现实开始，而不是把自己局限于一个单一的模式框架，就好像只要它有更多的资源，它就会以某种方式传递“真相”。

引用次数: 0

Combining forecasts under structural breaks using Graphical LASSO 使用图形套索组合结构断裂下的预测

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-05-28 DOI: 10.1016/j.ijforecast.2025.04.003

Tae-Hwy Lee , Ekaterina Seregina

In this paper we develop a novel method of combining many forecasts based on Graphical LASSO. We represent forecast errors from different forecasters as a network of interacting entities and generalize network inference in the presence of common factor structure and structural breaks. First, we note that forecasters often use common information and hence make common errors, which makes the forecast errors exhibit common factor structures. We separate common forecast errors from the idiosyncratic errors and exploit sparsity of the precision matrix of the latter. Second, since the network of experts changes over time as a response to unstable environments, we propose Regime-Dependent Factor Graphical LASSO (RD-FGL) that allows factor loadings and idiosyncratic precision matrix to be regime-dependent. The empirical applications to forecasting macroeconomic series using the data of the European Central Bank’s Survey of Professional Forecasters and Federal Reserve Economic Data monthly database demonstrate superior performance of a combined forecast using RD-FGL.

本文提出了一种基于图形LASSO的多种预测组合的新方法。我们将来自不同预测者的预测误差表示为相互作用实体的网络，并在存在共同因素结构和结构断裂的情况下推广网络推理。首先，我们注意到预测者经常使用共同的信息，从而产生共同的错误，这使得预测误差表现出共同的因素结构。我们将共同预测误差与特质误差分开，并利用后者的精度矩阵的稀疏性。其次，由于专家网络作为对不稳定环境的响应而随着时间的推移而变化，我们提出了制度依赖因子图形LASSO (RD-FGL)，它允许因子负载和特异性精度矩阵依赖于制度。利用欧洲中央银行专业预测人员调查和美联储经济数据月度数据库的数据对宏观经济系列进行预测的实证应用表明，使用RD-FGL进行组合预测具有优越的性能。

引用次数: 0

Anticipating humanitarian emergencies with a high risk of conflict-induced displacement 预见人道主义紧急情况，冲突引发的流离失所风险很高

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-05-22 DOI: 10.1016/j.ijforecast.2025.04.006

Nicolas Rost , Michele Ronco

This exploratory study assesses the risk of future onset of large-scale, conflict-related internal displacement in countries facing humanitarian emergencies. We train a variety of machine learning models on near-real-time data, which we compare against a simple baseline model, to assess the risk, one and three months into the future, of whether at least 1,000 people per month will flee their homes due to conflict. Measures of past displacement, conflict, risk of humanitarian crises, humanitarian access, the severity of humanitarian crises, and free elections improve forecasting performance. Limitations include the fact that displacement onsets are rare and hard to predict, and limited data availability and quality. Still, the best random forest model flagged 24 of 26 cases of displacement onset three months into the future and identified a high-risk group of country-months with a 33 times higher probability of displacement onset than a low-risk group. Providing such monthly forecasts to humanitarian practitioners could help them prepare better for new displacement or even mitigate the human suffering caused by conflict.

本探索性研究评估了面临人道主义紧急情况的国家未来发生大规模与冲突有关的国内流离失所的风险。我们在接近实时的数据上训练了各种机器学习模型，我们将其与简单的基线模型进行比较，以评估未来一个月和三个月的风险，是否每月至少有1000人因冲突而逃离家园。过去的流离失所、冲突、人道主义危机风险、人道主义准入、人道主义危机的严重程度以及自由选举等指标提高了预测效果。局限性包括：位移的发生非常罕见且难以预测，数据的可用性和质量也有限。尽管如此，最好的随机森林模型在未来三个月内标记了26个流离失所案例中的24个，并确定了一个高风险组，其流离失所发生的可能性比低风险组高33倍。向人道主义工作者提供这种月度预报可以帮助他们更好地为新的流离失所做好准备，甚至可以减轻冲突造成的人类痛苦。

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引用次数: 0

A functional mixture prediction model for dynamically forecasting cumulative intraday returns of crude oil futures 动态预测原油期货日内累计收益的函数混合预测模型

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-05-20 DOI: 10.1016/j.ijforecast.2025.04.001

Deqing Wang , Zhihao Lu , Zhenhua Liu , Shoucong Xue , Mengxia Guo , Yiwen Hou

Since the high-frequency crude oil futures price data from intraday trading sessions exhibit continuous functional characteristics, we propose a functional mixture prediction (FMP) model for real-time forecasting of crude oil cumulative intraday returns (CIDR). The core idea of FMP is dynamic forecasting after adaptive classification. Specifically, we develop an adaptive functional clustering algorithm to identify the distinct patterns of CIDR curves and establish a probabilistic discriminant model to estimate their cluster membership probabilities. The mixture prediction of a new partially observed CIDR is obtained by weighting its predicted trajectory in each cluster with its estimated membership probabilities. Moreover, we design an adaptive information updating mechanism to further improve the accuracy of intraday forecasts. Empirical results from applying FMP to forecast the CIDR of China’s crude oil futures show that the proposed FMP not only outperforms several competing forecasters but also provides additional insights into CIDR analysis by revealing distinct patterns in daily CIDR curves of similar variability and typical temporal trends. Furthermore, we provide evidence that FMP can achieve greater gains for traders with different risk preferences based on our designed intraday trading strategies.

鉴于高频原油期货盘中交易价格数据具有连续的功能特征，本文提出了一种功能混合预测（FMP）模型，用于实时预测原油累积盘中收益（CIDR）。FMP的核心思想是自适应分类后的动态预测。具体来说，我们开发了一种自适应功能聚类算法来识别CIDR曲线的不同模式，并建立了一个概率判别模型来估计它们的聚类隶属概率。对一个新的部分观测CIDR进行混合预测，将其预测轨迹与其估计的隶属概率加权。此外，我们设计了自适应信息更新机制，进一步提高了日内预报的准确性。应用FMP预测中国原油期货CIDR的实证结果表明，所提出的FMP不仅优于几种竞争预测方法，而且通过揭示相似变率和典型时间趋势的日常CIDR曲线的不同模式，为CIDR分析提供了额外的见解。此外，我们提供的证据表明，基于我们设计的日内交易策略，FMP可以为具有不同风险偏好的交易者实现更大的收益。

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引用次数: 0

Reactions to the Bernanke Review from Bank of England watchers 英国央行观察人士对伯南克评论的反应

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-05-19 DOI: 10.1016/j.ijforecast.2025.03.006

David Aikman , Richard Barwell

We summarize reactions to the Bernanke Review from the Bank of England watchers community – a diverse group of academics, market economists, and business analysts who closely monitor and analyze the actions of the Bank of England. Key themes include the Review’s recommendations to retire the “fan chart”, increase the use of scenario analysis, and de-emphasize the central forecast conditioned on the market yield curve, as well as its critique of the Bank’s forecasting infrastructure. There is also extensive discussion of areas left unaddressed by the Review, including whether the Monetary Policy Committee should publish its preferred policy rate path, adopt a Fed-style dot plot, or give Bank staff ownership of the forecast.

我们总结了英国央行观察人士对《伯南克评论》的反应，他们是一个由学者、市场经济学家和商业分析师组成的多元化团体，密切关注和分析英国央行的行动。关键主题包括《评估》建议取消“扇形图”，增加情景分析的使用，不再强调以市场收益率曲线为条件的中心预测，以及对世行预测基础设施的批评。会议还就《评估报告》未涉及的领域进行了广泛讨论，包括货币政策委员会（Monetary Policy Committee）是否应公布其偏好的政策利率路径、采用美联储式的点阵图，还是让世行员工拥有预测的所有权。

引用次数: 0

Forecasting UK consumer price inflation with RaGNAR: Random generalised network autoregressive processes 用RaGNAR预测英国消费者价格通胀：随机广义网络自回归过程

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-05-17 DOI: 10.1016/j.ijforecast.2025.04.005

Guy P. Nason, Henry Antonio Palasciano

This article forecasts consumer price index (CPI) inflation in the United Kingdom using random generalised network autoregressive (RaGNAR) processes. More specifically, we fit generalised network autoregressive (GNAR) processes to a large set of random networks generated according to the Erdős–Rényi–Gilbert model and select the best-performing networks each month to compute out-of-sample forecasts. RaGNAR significantly outperforms traditional benchmark models across all horizons. Remarkably, RaGNAR also delivers materially more accurate predictions than the Bank of England’s four to six month inflation rate forecasts published in their quarterly Monetary Policy Reports. Our results are remarkable not only for their accuracy, but also because of their speed, efficiency, and simplicity compared to the Bank’s current forecasting processes. RaGNAR’s performance improvements manifest in terms of both their root mean squared error and mean absolute percentage error, which measure different, but crucial, aspects of the methods’ performance. GNAR processes demonstrably predict future changes to CPI inflation more accurately and quickly than the benchmark models, especially at medium- to long-term forecast horizons, which is of great importance to policymakers charged with setting interest rates. We find that the most robust forecasts are those which combine the predictions from multiple GNAR processes via the use of various model averaging techniques. By analysing the structure of the best-performing graphs, we are also able to identify the key components that influence inflation rates during different periods.

本文使用随机广义网络自回归（RaGNAR）过程预测英国消费者价格指数（CPI）通胀。更具体地说，我们将广义网络自回归（GNAR）过程拟合到根据Erdős-Rényi-Gilbert模型生成的大量随机网络中，并每月选择表现最佳的网络来计算样本外预测。RaGNAR在所有领域的表现都明显优于传统的基准模型。值得注意的是，RaGNAR的预测也比英国央行（Bank of England）在其季度货币政策报告（Monetary Policy Reports）中发布的4至6个月通胀率预测准确得多。与世界银行目前的预测流程相比，我们的结果不仅因为其准确性，而且因为其速度、效率和简单性而引人注目。RaGNAR的性能改进体现在它们的均方根误差和平均绝对百分比误差上，它们衡量了方法性能的不同但至关重要的方面。GNAR过程显然比基准模型更准确、更快速地预测CPI通胀的未来变化，特别是在中长期预测范围内，这对负责设定利率的政策制定者非常重要。我们发现，最可靠的预测是那些通过使用各种模型平均技术将多个GNAR过程的预测结合起来的预测。通过分析表现最好的图表的结构，我们还能够确定在不同时期影响通货膨胀率的关键因素。

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引用次数: 0

Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition 在M6预测竞赛结果中揭示参与和一致性的影响

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-05-14 DOI: 10.1016/j.ijforecast.2025.04.002

Anastasios Kaltsounis, Evangelos Theodorou, Evangelos Spiliotis, Vassilios Assimakopoulos

The M6 competition evaluated investment performance over a period of one year, contributing to the efficient market hypothesis debate. This paper provides further insights into the outcomes of the competition by unraveling the effect that team engagement and performance consistency had on the final results. First, we identify three different types of engagement and investigate their relationship with portfolio efficiency, also making useful observations about the learning effect implied by a re-submission process. Then, we analyze the monthly performance of the teams and determine whether it aligned with their global performance or was affected significantly by extreme instances. Our results suggest that consistency is more important than engagement for making profitable investments. Nevertheless, we identify many cases where both regular portfolio updates and luck provided an advantage.

M6竞赛评估了一年内的投资表现，促进了有效市场假说的辩论。本文通过揭示团队参与和绩效一致性对最终结果的影响，提供了对比赛结果的进一步见解。首先，我们确定了三种不同类型的参与，并调查了它们与投资组合效率的关系，同时也对重新提交过程所隐含的学习效应进行了有用的观察。然后，我们分析团队的月度绩效，并确定它是否与他们的全球绩效一致，还是受到极端情况的显著影响。我们的研究结果表明，在进行有利可图的投资时，一致性比参与度更重要。然而，我们发现在许多情况下，定期的投资组合更新和运气都提供了优势。

引用次数: 0

Citizen forecasting in a mixed electoral system 混合选举制度下的公民预测

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-04-30 DOI: 10.1016/j.ijforecast.2025.03.007

Arndt Leininger , Andreas E. Murr , Lukas Stötzer , Mark A. Kayser

Existing studies show that aggregating citizens’ expectations about who will win can predict election outcomes in a majoritarian system. But can so-called citizen forecasting also successfully predict outcomes in mixed-member systems, where constituency results are less important? The existing evidence is mixed and limited in scope. We conducted, therefore, a citizen forecast of the 2021 German federal election by administering an original survey asking citizens who they thought would win in their constituency, what share of the vote each candidate would win in their constituency, and what share of the vote each party would win nationally. Citizens predicted constituency winners and vote shares more accurately than several benchmarks. However, our citizen forecast was based on a non-representative sample from an online-access panel. We conclude that citizen forecasting provides a simple and inexpensive way to predict the various relevant outcomes in mixed-member elections.

现有的研究表明，汇总公民对谁将获胜的预期可以预测多数主义制度下的选举结果。但是，所谓的“公民预测”是否也能成功地预测选区结果不那么重要的混合成员制度的结果呢？现有的证据是混杂的，而且范围有限。因此，我们对2021年德国联邦选举进行了公民预测，通过管理一项原始调查，询问公民他们认为谁将在他们的选区获胜，每个候选人将在他们的选区赢得多少选票，以及每个政党将在全国赢得多少选票。公民预测选区获胜者和投票份额比几个基准更准确。然而，我们的公民预测是基于来自在线访问面板的非代表性样本。我们的结论是，公民预测提供了一种简单而廉价的方法来预测混合成员选举的各种相关结果。

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