Journal of Forecasting最新文献_第5页

Identifying Drivers of Deviations From Rational Expectations: Using a New Irrational Index for Inflation Forecasts 识别偏离理性预期的驱动因素：使用一种新的非理性通胀预测指数

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-05 DOI: 10.1002/for.70136

Belen Chocobar, Peter Claeys

{"title":"Identifying Drivers of Deviations From Rational Expectations: Using a New Irrational Index for Inflation Forecasts","authors":"Belen Chocobar, Peter Claeys","doi":"10.1002/for.70136","DOIUrl":"https://doi.org/10.1002/for.70136","url":null,"abstract":"<p>Most studies on inflation forecasts have studied behavioral biases, informational frictions, or external shocks in isolation, without considering how these factors jointly drive deviations from rational expectations. We therefore adopt an integrated framework that simultaneously estimates the behavioral, informational, and external determinants of deviations from rationality. To measure the deviations, we develop a novel irrationality index—following Rossi and Sekhposyan's (2016) Fluctuation Rationality Test—to measure experts' time-varying deviations from rationality in US year-ahead inflation forecasts between 2010 and 2022. We then estimate the impact of different behavioral, informational, and external drivers on deviations from rationality using panel models. Our results indicate that external information shocks, interest rate expectations, as well as geopolitical risk all significantly drive the deviations from rationality. These findings highlight the value of a unified approach to expectation formation, moving beyond the isolated analyses that are common to the literature.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2160-2172"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Enhanced Bagging-Based Approach for Forecasting Nonstationary Time Series: Bridging Nonstationarity With a Scaled Logit Transformation 基于bagging的改进非平稳时间序列预测方法：用比例Logit变换架起非平稳的桥梁

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-12 DOI: 10.1002/for.70138

Young Eun Jeon, Yongku Kim, Jung-In Seo

{"title":"Enhanced Bagging-Based Approach for Forecasting Nonstationary Time Series: Bridging Nonstationarity With a Scaled Logit Transformation","authors":"Young Eun Jeon, Yongku Kim, Jung-In Seo","doi":"10.1002/for.70138","DOIUrl":"https://doi.org/10.1002/for.70138","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional block bootstrapping methods, such as the moving block bootstrap, can effectively preserve serial dependence within blocks when the underlying time series is stationary; however, when applied to nonstationary data, these methods often fail to capture evolving dependence structures, which can substantially undermine the performance of bagging predictors. This limitation highlights the need for effective strategies that transform nonstationary time series into stationary counterparts before bootstrapping, thereby enabling the reliable application of block bootstrapping in nonstationary settings. Motivated by this issue, this study develops an enhanced bagging-based approach incorporating a scaled logit transformation and a decomposition technique. In particular, the scaled logit transformation operates without parameter estimation and effectively stabilizes variance for data containing negative values or bounded ranges, such as proportions and rates, unlike a Box–Cox transformation, which relies on parameter estimation and requires positive data. The effectiveness of our method is examined through two illustrative studies: a simulation study and a real data analysis. In the simulation study, its performance is evaluated using various nonstationary time series generated under controlled conditions. For the real data analysis, three nonstationary time series datasets with different frequencies are utilized to substantiate its practical applicability.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2300-2308"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Macroeconomic and Financial Forecasting in the Aftermath of the Covid Shock: Inflation Forecasting With Large Language Models 新冠疫情冲击后的宏观经济和金融预测：基于大型语言模型的通胀预测

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-05 DOI: 10.1002/for.70139

Nikolaos Giannellis, Stephen G. Hall, Georgios P. Kouretas, George S. Tavlas, Yongli Wang

引用次数: 0

A New Proposal for Forecasting Inflation in the Eurozone: A Global Model 预测欧元区通胀的新建议：一个全球模型

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-24 DOI: 10.1002/for.70135

Georgios Angelopoulos, Zacharias Bragoudakis, Dimitrios Dimitriou, Alexandros Tsioutsios

{"title":"A New Proposal for Forecasting Inflation in the Eurozone: A Global Model","authors":"Georgios Angelopoulos, Zacharias Bragoudakis, Dimitrios Dimitriou, Alexandros Tsioutsios","doi":"10.1002/for.70135","DOIUrl":"https://doi.org/10.1002/for.70135","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper evaluates the forecasting performance of the relatively new machine learning Global Unrefined (GlobalUN, hereafter) model with respect to inflation in the Eurozone. In this global pooled neural network framework, we use a quarterly panel dataset covering 20 euro-area countries (2001Q1–2025Q1) together with the EA-20 aggregate, which includes key variables such as HICP, energy prices, food, and others. Thus, the network remains simple yet flexible enough to absorb heterogeneity across countries. Our work's contribution is crucial since monetary policy in the Eurozone hinges on accurate inflation forecasts (i.e., as ECB decisions target expected rather than current inflation). Our findings are crystal clear. The GlobalUN model outperforms all other benchmark models and the advanced machine learning XGBoost model in almost all Eurozone countries and horizons (i.e., the naïve model seems to outperforms in a few cases). These results are useful for policymakers, central banks, and fiscal institutions, as they should take the GlobalUN model into account as part of their arsenal.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2393-2425"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques 预测分类生产者价格：机器学习和计量经济学技术的融合

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-29 DOI: 10.1002/for.70149

Soňa Benecká

引用次数: 0

Forecasting European Union Electronic Trading Systems Phase 4 Spot Prices Using Data-Driven Hybrid Deep Learning Models: Integrating Energy and Market Activity as Controls 使用数据驱动的混合深度学习模型预测欧盟电子交易系统第4阶段现货价格：整合能源和市场活动作为控制

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-29 DOI: 10.1002/for.70152

Noman Arshed, Shajara Ul-Durar, Younes Ben Zaied, Marco De Sisto

{"title":"Forecasting European Union Electronic Trading Systems Phase 4 Spot Prices Using Data-Driven Hybrid Deep Learning Models: Integrating Energy and Market Activity as Controls","authors":"Noman Arshed, Shajara Ul-Durar, Younes Ben Zaied, Marco De Sisto","doi":"10.1002/for.70152","DOIUrl":"https://doi.org/10.1002/for.70152","url":null,"abstract":"<div>\u0000 \u0000 <p>Amid the focus on climate change mitigation, this study explores carbon market forecasting. This study uses a hybrid forecasting framework that integrates empirical model decomposition, bidirectional long short-term memory (BiLSTM) network, and attention mechanism to enhance the predictive performance of carbon spot prices within the European Union (EU) Emissions Trading System (ETS). The model decomposes the nonstationary carbon prices to multiple intrinsic mode functions (IMF) representing each distinct frequency component. The forecasting at IMF level enables learning of temporal dependence and volatility. The final model reconstructs the signals to present overall prediction. The multiple iterations that include a selection of macroeconomic variable led to the final root mean square errors (RMSE) value of 4.59, which shows that the BiLSTM outperforms a conventional long short-term memory (LSTM) setup. This study also improves the model by including exogenous macroeconomic variables and policy shocks to enhance predictive accuracy. Shapley additive explanations (SHAP) analysis also identified the important features and variables. The visualized confidence interval confirms the reliability of the forecasts. The findings of the study highlight the effectiveness of integrating signal decomposition with deep learning and inclusion of exogenous factors. This study offers practical insights for regulators and researchers who are engaged in the emissions market and climate finance.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2502-2524"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Inflation Forecasting Post-COVID-19: Evidence From Germany covid -19后的通胀预测：来自德国的证据

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-07 DOI: 10.1002/for.70142

Tiphaine Wibault

{"title":"Inflation Forecasting Post-COVID-19: Evidence From Germany","authors":"Tiphaine Wibault","doi":"10.1002/for.70142","DOIUrl":"https://doi.org/10.1002/for.70142","url":null,"abstract":"<div>\u0000 \u0000 <p>I compare the forecasting performance of a wide range of univariate and multivariate forecasting models for both headline and core inflation in Germany. The analysis is conducted using a full sample containing quarterly data from 1995 to 2024, which includes the COVID-19 pandemic and subsequent inflation surge, and a shortened sample spanning the years 1995–2019, a period of low and stable inflation. I firstly find evidence to support a recurrent finding in the literature that univariate models are hard to beat but show that this holds particularly for single-indicator models, at times of stable inflation. Furthermore, upon testing 34 indicators for German inflation in both single-indicator and combination (i.e., factor, pooling, and BVAR) models, I find that the performance of multivariate models is improved by (i) including the years 2020–2024 in which inflation was high and volatile and (ii) by <i>combining</i> information contained within the indicators. Specifically, I find that the multivariate models improve upon the univariate benchmark by up to 20% in the full sample, providing evidence for the performance of forecasting models to vary with the economic environment. The analysis also sheds light on which indicators are particularly useful for forecasting inflation in Germany. Additionally, it appears that multivariate models provide a greater forecasting gain over the univariate benchmark for headline, rather than core, inflation. Finally, a short case study of the inflation pickup following the COVID-19 pandemic reveals that the multivariate models, particularly the factor, BVAR, and price expectations single-indicator models, most accurately forecasted inflation at this time.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2238-2265"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Evaluating Forecasts at Multiple Horizons: An Extension of the Diebold–Mariano Approach 多视界预测评估：Diebold-Mariano方法的扩展

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-22 DOI: 10.1002/for.70150

Andrew Grant, Matus Mrazik, Steve Satchell

引用次数: 0

Intraday Functional PCA Forecasting of Cryptocurrency Returns 加密货币收益的日内功能PCA预测

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-07 DOI: 10.1002/for.70127

Joann Jasiak, Cheng Zhong

引用次数: 0

AI-Driven Inflation Forecasting in the Aftermath of COVID-19 2019冠状病毒病后人工智能驱动的通胀预测

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-04-08 DOI: 10.1002/for.70148

Krystian Jaworski

{"title":"AI-Driven Inflation Forecasting in the Aftermath of COVID-19","authors":"Krystian Jaworski","doi":"10.1002/for.70148","DOIUrl":"https://doi.org/10.1002/for.70148","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper develops a hybrid approach to inflation forecasting that combines large language models (LLMs) with traditional econometric methods. We construct a novel indicator of inflation expectations by applying GPT-4o to classify Twitter discussions among economists and financial commentators in the United States, the Eurozone, and Poland. Embedded in an ARIMAX framework, this indicator delivers systematic predictive content and consistently anticipates inflation dynamics up to 1 year ahead. Out-of-sample tests show that AI-augmented forecasts achieve statistically significant accuracy gains over both professional consensus forecasts and benchmark ARIMA models, particularly at medium and long horizons. Robustness checks—including alternative smoothing and lag structures, model variants, prompt formulations, and metadata weighting—confirm the stability of results across regions and specifications. By integrating high-frequency, sentiment-based signals with econometric forecasting, the study demonstrates how generative AI can complement conventional tools, offering central banks, policymakers, and financial market participants an early-warning mechanism during periods of uncertainty and structural change. While the approach is constrained by its reliance on Twitter data and the interpretability limits of LLMs, it highlights the transformative potential of hybrid AI-econometric frameworks for real-time macroeconomic analysis.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2525-2548"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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