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The structural Theta method and its predictive performance in the M4-Competition 结构Theta方法及其在m4竞赛中的预测性能
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-10 DOI: 10.1016/j.ijforecast.2024.08.003
Giacomo Sbrana , Andrea Silvestrini
{"title":"The structural Theta method and its predictive performance in the M4-Competition","authors":"Giacomo Sbrana ,&nbsp;Andrea Silvestrini","doi":"10.1016/j.ijforecast.2024.08.003","DOIUrl":"10.1016/j.ijforecast.2024.08.003","url":null,"abstract":"<div><div>The Theta method is a well-established prediction benchmark widely used in forecast competitions. This method has received significant attention since it was introduced more than 20 years ago, with several authors proposing variants to improve its performance. This paper considers multiple sources of error versions for Theta, belonging to the family of structural time series models. It investigates its out-of-sample forecast performance using the extensive M4-Competition dataset, which includes 100,000 time series. We compare the proposed structural Theta model against several benchmarks, including all variants of the Theta method. The results demonstrate its remarkable predictive abilities as it outperforms all its variants and competitors, emerging as a solid benchmark for use in forecast competitions.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 940-952"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Trust the experts? The performance of inflation expectations, 1960–2023 相信专家？通胀预期的表现，1960-2023
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-10-18 DOI: 10.1016/j.ijforecast.2024.06.006
Tyler Goodspeed
{"title":"Trust the experts? The performance of inflation expectations, 1960–2023","authors":"Tyler Goodspeed","doi":"10.1016/j.ijforecast.2024.06.006","DOIUrl":"10.1016/j.ijforecast.2024.06.006","url":null,"abstract":"<div><div>Using the oldest continuous surveys of U.S. inflation expectations, I find that when inflation is low, the average professional forecaster is generally rational (unbiased, with serially uncorrelated errors) and efficient (exploits available information), and inflation moves one-for-one with their expectations, whereas the average consumer is biased, underreacts to inflation, and inflation moves independently of their expectations. In contrast, when inflation is high the average consumer is generally unbiased, rational, and efficient, and inflation moves one-for-one with their expectations, while the opposite is true of professionals. Neither the median consumer nor median professional is fully rational and efficient, with the consumer underreacting when inflation is low and overreacting when inflation is high. However, inflation moves one-for-one with the median consumer forecast when inflation is high. Results are consistent with an inflation process characterized by two regimes—a low-inflation regime in which consumers are inattentive, and a high-inflation regime in which consumers are highly attentive and inflation moves with their expectations.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 863-876"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fan charts 2.0: Flexible forecast distributions with expert judgement 扇形图2.0：灵活的预测分布和专家判断
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-20 DOI: 10.1016/j.ijforecast.2024.11.009
Andrej Sokol
{"title":"Fan charts 2.0: Flexible forecast distributions with expert judgement","authors":"Andrej Sokol","doi":"10.1016/j.ijforecast.2024.11.009","DOIUrl":"10.1016/j.ijforecast.2024.11.009","url":null,"abstract":"<div><div>I propose a new model, conditional quantile regression (CQR), that generates density forecasts consistent with a specific view of the future evolution of some of the explanatory variables. This addresses a shortcoming of existing quantile regression-based models in settings that require forecasts to be conditional on technical assumptions, such as most forecasting processes within policy institutions. Through an application to house price inflation in the euro area, I show that CQR provides a viable alternative to conditional density forecasting with Bayesian VARs, with added flexibility and further insights that do not come at the cost of forecasting performance.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1148-1164"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Predicting value at risk for cryptocurrencies with generalized random forests 用广义随机森林预测加密货币的风险值
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2025-01-24 DOI: 10.1016/j.ijforecast.2024.12.002
Rebekka Buse , Konstantin Görgen , Melanie Schienle
{"title":"Predicting value at risk for cryptocurrencies with generalized random forests","authors":"Rebekka Buse ,&nbsp;Konstantin Görgen ,&nbsp;Melanie Schienle","doi":"10.1016/j.ijforecast.2024.12.002","DOIUrl":"10.1016/j.ijforecast.2024.12.002","url":null,"abstract":"<div><div>We study the prediction of value at risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that generalized random forests (GRF) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type models, and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns, and clearly superior in the cryptocurrency setup.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1199-1222"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Econometric forecasting using ubiquitous news text: Text-enhanced factor model 使用无处不在的新闻文本的计量经济预测：文本增强因子模型
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-05 DOI: 10.1016/j.ijforecast.2024.11.001
Beomseok Seo
{"title":"Econometric forecasting using ubiquitous news text: Text-enhanced factor model","authors":"Beomseok Seo","doi":"10.1016/j.ijforecast.2024.11.001","DOIUrl":"10.1016/j.ijforecast.2024.11.001","url":null,"abstract":"<div><div>News text is gaining increasing attention as a novel source for econometric forecasting. This paper revisits how narrative information is incorporated into econometric forecasting by effectively quantifying sector-specific textual information without requiring training data. We propose <em>Theme Frequency Indices</em> (TFIs), which utilize domain-specific subject-predicate patterns to measure public perception about the economy. TFIs for 15 sectors, including production, inflation, employment, capital investment, stock and house prices, and others, were examined and integrated into the <em>Text-enhanced Factor Model</em> (TFM), using latent factor structures. Empirical analysis based on over 18 million news articles from Korea reveals that TFM improves the accuracy of near-term GDP forecasts, demonstrating that simple text-mining techniques combined with domain knowledge can effectively leverage qualitative information in the news without costly training. The proposed method is applicable to a wide range of subjects for utilizing narrative information on the economy, offering a rapid and cost-effective approach.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1055-1072"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The decrease in confidence with forecast extremity 随着预测的极值，信心的下降
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2025-01-16 DOI: 10.1016/j.ijforecast.2024.07.004
Doron Sonsino , Yefim Roth
{"title":"The decrease in confidence with forecast extremity","authors":"Doron Sonsino ,&nbsp;Yefim Roth","doi":"10.1016/j.ijforecast.2024.07.004","DOIUrl":"10.1016/j.ijforecast.2024.07.004","url":null,"abstract":"<div><div>A large panel of chief financial officers’ forecasts of the S&amp;P 500 annual returns and four experiments suggest that forecast confidence decreases as the forecasts diverge from zero, in the positive or negative direction. This decreased confidence is reflected in longer forecast intervals, larger perceived volatility estimates, and weaker belief in the accuracy of the predictions. <span><span>De Bondt</span></span>’s (<span><span>1993</span></span>) forecast hedging intensifies with the extremity of the forecasts, but the decrease in confidence is sustained when the intervals are symmetrized. Imposing cumulative prospect theory preferences on the CFOs, permutation tests show that the decreased confidence delays the response to optimistic expectations and alleviates miscalibration, although the optimistic CFOs still discount the VIX by more than 50%. The paper thus reveals a self-corrective mechanism that partially, but far from fully, offsets the overconfidence hazards.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 877-893"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Testing for equal predictive accuracy with strong dependence 具有强依赖性的相等预测精度的测试
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-05 DOI: 10.1016/j.ijforecast.2024.11.003
Laura Coroneo , Fabrizio Iacone
{"title":"Testing for equal predictive accuracy with strong dependence","authors":"Laura Coroneo ,&nbsp;Fabrizio Iacone","doi":"10.1016/j.ijforecast.2024.11.003","DOIUrl":"10.1016/j.ijforecast.2024.11.003","url":null,"abstract":"<div><div>We analyse the properties of the Diebold and Mariano (1995) test in the presence of autocorrelation in the loss differential. We show that the power of the Diebold and Mariano (1995) test decreases as the dependence increases, making it more difficult to obtain statistically significant evidence of superior predictive ability against less accurate benchmarks. We also find that, after a certain threshold, the test has no power, and the correct null hypothesis is spuriously rejected. These results caution us to seriously consider the loss differential’s dependence properties before applying the Diebold and Mariano (1995) test.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1073-1092"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust recalibration of aggregate probability forecasts using meta-beliefs 利用元信念对总概率预测进行稳健的再校准
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-04-01 Epub Date: 2024-10-30 DOI: 10.1016/j.ijforecast.2024.09.005
Cem Peker , Tom Wilkening
{"title":"Robust recalibration of aggregate probability forecasts using meta-beliefs","authors":"Cem Peker ,&nbsp;Tom Wilkening","doi":"10.1016/j.ijforecast.2024.09.005","DOIUrl":"10.1016/j.ijforecast.2024.09.005","url":null,"abstract":"<div><div>Previous work suggests that aggregate probabilistic forecasts on a binary event are often conservative. Extremizing transformations that adjust the aggregate forecast away from the uninformed prior of 0.5 can improve calibration in many settings. However, such transformations may be problematic in decision problems where forecasters share a biased prior. In these problems, extremizing transformations can introduce further miscalibration. We develop a two-step algorithm where we first estimate the prior using each forecaster’s belief about the average forecast of others. We then transform away from this estimated prior in each forecasting problem. Our algorithm works in single-question forecasting problems and does not require past data. Evidence from experimental prediction tasks suggests that the resulting average probability forecast is robust to biased priors and improves calibration.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 613-630"},"PeriodicalIF":6.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping 改进人口外预测：模型辅助和判断自举的互补效应
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-04-01 Epub Date: 2024-09-17 DOI: 10.1016/j.ijforecast.2024.07.002
Mathew D. Hardy , Sam Zhang , Jessica Hullman , Jake M. Hofman , Daniel G. Goldstein
{"title":"Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping","authors":"Mathew D. Hardy ,&nbsp;Sam Zhang ,&nbsp;Jessica Hullman ,&nbsp;Jake M. Hofman ,&nbsp;Daniel G. Goldstein","doi":"10.1016/j.ijforecast.2024.07.002","DOIUrl":"10.1016/j.ijforecast.2024.07.002","url":null,"abstract":"<div><div>We propose and test a method for out-of-population prediction termed model-assisted judgmental bootstrapping, which leverages a predictive model from one domain combined with expert judgment to generate training data and subsequently a predictive model for a new domain. In a preregistered experiment (<span><math><mi>N</mi></math></span>=1440), we assessed the predictive accuracy of this method in increasingly challenging environments. We also analyzed the individual contributions of two techniques that underlie the method: model-assisted estimation and judgmental bootstrapping. Our findings revealed that both techniques significantly improved predictive accuracy. Furthermore, their impacts were complementary: model-assisted estimation provided the largest accuracy gains in the least demanding environment, while judgmental bootstrapping did so in the most challenging environment. Our results suggest that model-assisted judgmental bootstrapping is a promising technique for creating predictive models in domains in which outcome data are not available.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 689-701"},"PeriodicalIF":6.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Efficiency of poll-based multi-period forecasting systems for German state elections 基于民调的德国州选举多期预测系统的效率
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2025-04-01 Epub Date: 2024-05-18 DOI: 10.1016/j.ijforecast.2024.04.008
Markus Fritsch , Harry Haupt , Joachim Schnurbus
{"title":"Efficiency of poll-based multi-period forecasting systems for German state elections","authors":"Markus Fritsch ,&nbsp;Harry Haupt ,&nbsp;Joachim Schnurbus","doi":"10.1016/j.ijforecast.2024.04.008","DOIUrl":"10.1016/j.ijforecast.2024.04.008","url":null,"abstract":"<div><div>Election polls are frequently employed to reflect voter sentiment with respect to a particular election (or fixed-event). Despite their widespread use as forecasts and inputs for predictive algorithms, there is substantial uncertainty regarding their efficiency. This uncertainty is amplified by judgment in the form of pollsters applying unpublished weighting schemes to ensure the representativeness of the sampled voters for the underlying population. Efficient forecasting systems incorporate past information instantly, which renders a given fixed-event unpredictable based on past information. This results in all sequential adjustments of the fixed-event forecasts across adjacent time periods (or forecast revisions) being martingale differences. This paper illustrates the theoretical conditions related to weak efficiency of fixed-event forecasting systems based on traditional least squares loss and asymmetrically weighted least absolute deviations (or quantile) loss. Weak efficiency of poll-based multi-period forecasting systems for all German federal state elections since the year 2000 is investigated. The inefficiency of almost all considered forecasting systems is documented and alternative explanations for the findings are discussed.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 670-688"},"PeriodicalIF":6.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141135428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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