Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2024

• Afees A. Salisu & Ahamuefula E. Ogbonna & Elie Bouri & Rangan Gupta, 2024, "Climate Risks and Prediction of Sectoral REITs Volatility: International Evidence," Working Papers, University of Pretoria, Department of Economics, number 202434, Aug.
• Elie Bouri & Rangan Gupta & Christian Pierdzioch & Onur Polat, 2024, "Forecasting U.S. Recessions Using Over 150 Years of Data: Stock-Market Moments versus Oil-Market Moments," Working Papers, University of Pretoria, Department of Economics, number 202435, Aug.
• Rangan Gupta & Anandamayee Majumdar & Christian Pierdzioch & Onur Polat, 2024, "Climate Risks and Real Gold Returns over 750 Years," Working Papers, University of Pretoria, Department of Economics, number 202436, Aug.
• Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024, "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers, University of Pretoria, Department of Economics, number 202437, Aug.
• O-Chia Chuang & Rangan Gupta & Christian Pierdzioch & Buliao Shu, 2024, "Financial Uncertainty and Gold Market Volatility: Evidence from a GARCH-MIDAS Approach with Variable Selection," Working Papers, University of Pretoria, Department of Economics, number 202441, Sep.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Elie Bouri & Rangan Gupta, 2024, "Economic Policy Uncertainty and Bank-Level Stock Returns Volatility of the United States: A Mixed-Frequency Perspective," Working Papers, University of Pretoria, Department of Economics, number 202444, Oct.
• Matteo Bonato & Rangan Gupta & Christian Pierdzioch, 2024, "Do Shortages Forecast Aggregate and Sectoral U.S. Stock Market Realized Variance? Evidence from a Century of Data," Working Papers, University of Pretoria, Department of Economics, number 202450, Nov.
• Andrea Kolková, 2024, "Data Analysis in Demand Forecasting: A Case Study of Poetry Book Sales in the European Area," Central European Business Review, Prague University of Economics and Business, volume 2024, issue 5, pages 51-69, DOI: 10.18267/j.cebr.371.
• Qi Shi, 2024, "The Second RP-PCA Factor and Crude Oil Price Predictability," Prague Economic Papers, Prague University of Economics and Business, volume 2024, issue 6, pages 662-690, DOI: 10.18267/j.pep.879.
• Ayaz Zeynalov, 2024, "Impact of Oil Price Shocks on Russian Macroeconomic Performance," Politická ekonomie, Prague University of Economics and Business, volume 2024, issue 4, pages 676-701, DOI: 10.18267/j.polek.1412.
• Oktay Özkan & Babatunde Sunday Eweade & Tomiwa Sunday Adebayo, 2024, "Examining the Effects of Energy Efficiency R&D and Renewable Energy on Environmental Sustainability Amidst Political Risk in France," Politická ekonomie, Prague University of Economics and Business, volume 2024, issue Spec.issu, pages 331-356, DOI: 10.18267/j.polek.1437.
• João Amador & Paulo Barbosa & João Cortes, 2024, "Distance to Export: A Machine Learning Approach with Portuguese Firms," Working Papers, Banco de Portugal, Economics and Research Department, number w202420.
• Randall Romero-Aguilar, 2024, "Una propuesta para medir el ciclo economico," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., volume 21, issue 1, pages 39-58, January-J.
• Luke Hartigan & Tom Rosewall, 2024, "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," RBA Research Discussion Papers, Reserve Bank of Australia, number rdp2024-04, Jul, DOI: 10.47688/rdp2024-04.
• Tenorio, Juan & Perez, Wilder, 2024, "GDP nowcasting with Machine Learning and Unstructured Data," Working Papers, Banco Central de Reserva del Perú, number 2024-003, Apr.
• Fernando Pérez Forero, 2024, "Forecasting Peruvian Monetary Aggregates in a Nonlinear and Uncertain Environment," Working Papers, Banco Central de Reserva del Perú, number 2024-010, Dec.
• Fernando Pérez Forero, 2024, "Exploring the presence of Nonlinearities in the Peruvian Economy - Monetary Policy Implications," Working Papers, Banco Central de Reserva del Perú, number 2024-017, Dec.
• Foteini Kyriazi & Efthymios Xylangouras & Theodoros Papadogonas, 2024, "On the Forecastability of Agricultural Output," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, volume 16, issue 4, pages 443-467, December, DOI: https://doi.org/10.15353/rea.v16i4..
• Saswat Patra & Malay Bhattacharyya, 2024, "Charting the Unknown: First Passage Time Probabilities for Pearson Diffusion Process and Application to Options Risk Management," American Business Review, Pompea College of Business, University of New Haven, volume 27, issue 2, pages 623-639.
• Chalerm Jaitang & Zhaohua Li & Christopher Gan, 2024, "An Empirical Analysis of Private SMEs' Insolvency in Thailand Using Machine Learning," Asian Journal of Applied Economics/ Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, volume 31, issue 2, pages 1-30.
• Georgy Bronitsky & Elena Vakulenko, 2024, "Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 73, pages 78-101.
• Ekaterina Astafyeva & Marina Turuntseva, 2024, "Forecast evaluation improving using the simplest methods of individual forecasts’ combination," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 74, pages 78-103.
• Marina Mikitchuk, 2024, "Forming the benefit-oriented official assistance: Cross-country analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 74, pages 124-143.
• Anton Skrobotov, 2024, "Time series forecasting under structural breaks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 76, pages 120-139.
• Berit Hanna Czock & Cordelia Frings & Fabian Arnold, 2024, "Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany," EWI Working Papers, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), number 2024-4, Jun.
• Morteza Beiranvand & Seyed Saeed Malek Sadati & Seyed Mohammad Javad Razmi, 2024, "Nowcasting Iran's GDP Using Sentiment Analysis of Economic News," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, volume 11, issue 3, pages 135-164.
• Mihaela SIMIONESCU, 2024, "The Role of the European Directive on Renewable Energy Consumption in Reducing Pollution in CEE Countries from the European Union," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 5-21, July.
• Taoxiong Liu & Huolan Cheng, 2024, "Can The Classical Economic Model Improve The Performance Of Deep Learning? A GDP Forecasting Example," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 86-110, July.
• Xianning WANG & Xikai HUANG & Longkun TIAN & Huiyan ZHOU, 2024, "Can the Futures Price of Agricultural Products Predict the Scale of China's Agricultural Production?," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 4, pages 128-143, December.
• Pablo PINCHEIRA-BROWN & Nicolás HARDY, 2024, "More predictable than ever, with the worst MSPE ever," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 4, pages 5-30, December.
• Vlad TEODORESCU & Catalina-Ioana TOADER, 2024, "Using Machine Learning to Model Bankruptcy Risk in Listed Companies," PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ECONOMICS AND SOCIAL SCIENCES, Bucharest University of Economic Studies, Romania, volume 6, issue 1, pages 610-619, August.
• Ignace De Vos & Gerdie Everaert, 2025, "GLS Estimation of Local Projections: Trading Robustness for Efficiency," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium, Ghent University, Faculty of Economics and Business Administration, number 24/1095, Jun.
• Paramita Mukherjee & Dipankor Coondoo & Poulomi Lahiri, 2024, "Forecasting Hourly Spot Prices in Indian Electricity Market," Studies in Microeconomics, , volume 12, issue 3, pages 273-295, December, DOI: 10.1177/23210222221108019.
• Renáta K?e?ková & Daniela ?álková & Radka Procházková & Sergyi Yekimov, 2024, "Macroeconomics And Tourism Demand: Evaluating The Role Of Economic Indicators In The Czech Republic?S Hospitality Industry," Proceedings of Economics and Finance Conferences, International Institute of Social and Economic Sciences, number 14516470, Oct.
• Milen Arro-Cannarsa & Rolf Scheufele, 2024, "Nowcasting GDP: what are the gains from machine learning algorithms?," Working Papers, Swiss National Bank, number 2024-06.
• Jiawen Xu & Pierre Perron, 2024, "Forecasting in the presence of in-sample and out-of-sample breaks," Advanced Studies in Theoretical and Applied Econometrics, Springer, in: Subal C. Kumbhakar & Robin C. Sickles & Hung-Jen Wang, "Advances in Applied Econometrics", DOI: 10.1007/978-3-031-48385-1_20.
• Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2024, "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Annals of Operations Research, Springer, volume 334, issue 1, pages 679-699, March, DOI: 10.1007/s10479-021-04187-w.
• Mehdi Mili & Jean‐Michel Sahut & Frédéric Teulon & Lubica Hikkerova, 2024, "A multidimensional Bayesian model to test the impact of investor sentiment on equity premium," Annals of Operations Research, Springer, volume 334, issue 1, pages 919-939, March, DOI: 10.1007/s10479-023-05165-0.
• Daniel Goller & Sandro Heiniger, 2024, "A general framework to quantify the event importance in multi-event contests," Annals of Operations Research, Springer, volume 341, issue 1, pages 71-93, October, DOI: 10.1007/s10479-023-05540-x.
• Patrick Oliver Schenk & Christoph Kern, 2024, "Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, volume 18, issue 2, pages 131-184, June, DOI: 10.1007/s11943-024-00344-2.
• Andrey Shternshis & Piero Mazzarisi, 2024, "Variance of entropy for testing time-varying regimes with an application to meme stocks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 47, issue 1, pages 215-258, June, DOI: 10.1007/s10203-023-00427-9.
• Ewelina Osowska & Piotr Wójcik, 2024, "Predicting the reaction of financial markets to Federal Open Market Committee post-meeting statements," Digital Finance, Springer, volume 6, issue 1, pages 145-175, March, DOI: 10.1007/s42521-023-00096-8.
• Ewelina Osowska & Piotr Wójcik, 2024, "Correction: Predicting the reaction of financial markets to Federal Open Market Committee post-meeting statements," Digital Finance, Springer, volume 6, issue 1, pages 177-177, March, DOI: 10.1007/s42521-023-00100-1.
• Vu, Patrick, 2024, "Why are replication rates so low?," Journal of Econometrics, Elsevier, volume 245, issue 1, DOI: 10.1016/j.jeconom.2024.105868.
• Chudik, Alexander & Pesaran, M. Hashem & Sharifvaghefi, Mahrad, 2024, "Variable selection in high dimensional linear regressions with parameter instability," Journal of Econometrics, Elsevier, volume 246, issue 1, DOI: 10.1016/j.jeconom.2024.105900.
• Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2024, "Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility," Econometrics and Statistics, Elsevier, volume 32, issue C, pages 34-56, DOI: 10.1016/j.ecosta.2021.08.002.
• Berlin, Mitchell & Byun, Sung Je & D'Erasmo, Pablo & Yu, Edison, 2024, "Measuring climate transition risk at the regional level with an application to community banks," European Economic Review, Elsevier, volume 170, issue C, DOI: 10.1016/j.euroecorev.2024.104834.
• Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2024, "Labour at risk," European Economic Review, Elsevier, volume 170, issue C, DOI: 10.1016/j.euroecorev.2024.104849.
• Montorsi, Carlotta & Fusco, Alessio & Van Kerm, Philippe & Bordas, Stéphane P.A., 2024, "Predicting depression in old age: Combining life course data with machine learning," Economics & Human Biology, Elsevier, volume 52, issue C, DOI: 10.1016/j.ehb.2023.101331.
• Coqueret, Guillaume & Deguest, Romain, 2024, "Unexpected opportunities in misspecified predictive regressions," European Journal of Operational Research, Elsevier, volume 318, issue 2, pages 686-700, DOI: 10.1016/j.ejor.2024.05.044.
• Hernández, Juan R. & Ventosa-Santaulària, Daniel & Valencia, J. Eduardo, 2024, "Global supply chain inflationary pressures and monetary policy in Mexico," Emerging Markets Review, Elsevier, volume 58, issue C, DOI: 10.1016/j.ememar.2023.101089.
• Lo, Gaye-Del & Marcelin, Isaac & Bassène, Théophile & Lo, Assane, 2024, "Connectedness and risk spillovers among sub-Saharan Africa and MENA equity markets," Emerging Markets Review, Elsevier, volume 63, issue C, DOI: 10.1016/j.ememar.2024.101193.
• Branco, Rafael R. & Rubesam, Alexandre & Zevallos, Mauricio, 2024, "Forecasting realized volatility: Does anything beat linear models?," Journal of Empirical Finance, Elsevier, volume 78, issue C, DOI: 10.1016/j.jempfin.2024.101524.
• Watanabe, Toshiaki & Nakajima, Jouchi, 2024, "High-frequency realized stochastic volatility model," Journal of Empirical Finance, Elsevier, volume 79, issue C, DOI: 10.1016/j.jempfin.2024.101559.
• Salisu, Afees A. & Demirer, Riza & Gupta, Rangan, 2024, "Technological shocks and stock market volatility over a century," Journal of Empirical Finance, Elsevier, volume 79, issue C, DOI: 10.1016/j.jempfin.2024.101561.
• Syuhada, Khreshna & Hakim, Arief & Suprijanto, Djoko, 2024, "Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives," Energy Economics, Elsevier, volume 129, issue C, DOI: 10.1016/j.eneco.2023.107261.
• Salisu, Afees A. & Isah, Kazeem & Oloko, Tirimisiyu O., 2024, "Technology shocks and crude oil market connection: The role of climate change," Energy Economics, Elsevier, volume 130, issue C, DOI: 10.1016/j.eneco.2024.107325.
• Phella, Anthoulla & Gabriel, Vasco J. & Martins, Luis F., 2024, "Predicting tail risks and the evolution of temperatures," Energy Economics, Elsevier, volume 131, issue C, DOI: 10.1016/j.eneco.2023.107286.
• Wang, Yushi & Wu, Libo & Zhou, Yang, 2024, "Household's willingness to pay for renewable electricity: A meta-analysis," Energy Economics, Elsevier, volume 131, issue C, DOI: 10.1016/j.eneco.2024.107390.
• Gupta, Rangan & Nielsen, Joshua & Pierdzioch, Christian, 2024, "Stock market bubbles and the realized volatility of oil price returns," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107432.
• Bonaccolto, Giovanni & Caporin, Massimiliano & Iacopini, Matteo, 2024, "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Comment," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107469.
• Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024, "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107466.
• Yang, Jinyu & Dong, Dayong & Liang, Chao & Cao, Yang, 2024, "Monetary policy uncertainty and the price bubbles in energy markets," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107503.
• Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie & Wang, Qunwei, 2024, "Forecasting carbon prices under diversified attention: A dynamic model averaging approach with common factors," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107537.
• Blazsek, Szabolcs & Escribano, Alvaro & Kristof, Erzsebet, 2024, "Global, Arctic, and Antarctic sea ice volume predictions using score-driven threshold climate models," Energy Economics, Elsevier, volume 134, issue C, DOI: 10.1016/j.eneco.2024.107591.
• Billio, Monica & Casarin, Roberto & Costola, Michele & Veggente, Veronica, 2024, "Learning from experts: Energy efficiency in residential buildings," Energy Economics, Elsevier, volume 136, issue C, DOI: 10.1016/j.eneco.2024.107650.
• Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024, "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, volume 136, issue C, DOI: 10.1016/j.eneco.2024.107733.
• Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024, "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, volume 138, issue C, DOI: 10.1016/j.eneco.2024.107851.
• Tian, Guangning & Peng, Yuchao & Du, Huancheng & Meng, Yuhao, 2024, "Forecasting crude oil returns in different degrees of ambiguity: Why machine learn better?," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107867.
• Zhao, Yue & Brooks, Adria E. & Du, Xiaodong, 2024, "Electricity market resilience in the face of Hurricane Harvey: A network-oriented approach," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107879.
• Sánchez-García, Javier & Mattera, Raffaele & Cruz-Rambaud, Salvador & Cerqueti, Roy, 2024, "Measuring financial stability in the presence of energy shocks," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107922.
• Fields, Micah & Lindequist, David, 2024, "Global spillovers of US climate policy risk: Evidence from EU carbon emissions futures," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107931.
• Lipiecki, Arkadiusz & Uniejewski, Bartosz & Weron, Rafał, 2024, "Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107934.
• Yang, Kun & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024, "Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107952.
• Zhao, Yuan & Gong, Xue & Zhang, Weiguo & Xu, Weijun, 2024, "Forecasting carbon futures returns using feature selection and Markov chain with sample distribution," Energy Economics, Elsevier, volume 140, issue C, DOI: 10.1016/j.eneco.2024.107962.
• Kim, Sunjin & Park, Daehyeon & Ryu, Doojin, 2024, "Potential sanctions on the Northeast Asia supergrid: A network theory perspective," Energy, Elsevier, volume 302, issue C, DOI: 10.1016/j.energy.2024.131655.
• Wen, Danyan & Wang, Huihui & Wang, Yudong & Xiao, Jihong, 2024, "Crude oil futures and the short-term price predictability of petroleum products," Energy, Elsevier, volume 307, issue C, DOI: 10.1016/j.energy.2024.132750.
• He, Mengxi & Zhang, Zhikai & Zhang, Yaojie, 2024, "Forecasting crude oil prices with global ocean temperatures," Energy, Elsevier, volume 311, issue C, DOI: 10.1016/j.energy.2024.133341.
• Hong, Yun & Yao, Youfu, 2024, "Can comment letters impact excess perks? Evidence from China," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.102943.
• Zhang, Jiaming & Xiang, Yitian & Zou, Yang & Guo, Songlin, 2024, "Volatility forecasting of Chinese energy market: Which uncertainty have better performance?," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.102952.
• Bouazizi, Tarek & Guesmi, Khaled & Galariotis, Emilios & Vigne, Samuel A., 2024, "Crude oil prices in times of crisis: The role of Covid-19 and historical events," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.102955.
• Teng, Huei-Wen & Kang, Ming-Hsuan & Lee, I-Han & Bai, Le-Chi, 2024, "Bridging accuracy and interpretability: A rescaled cluster-then-predict approach for enhanced credit scoring," International Review of Financial Analysis, Elsevier, volume 91, issue C, DOI: 10.1016/j.irfa.2023.103005.
• Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024, "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103094.
• Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024, "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103102.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2024, "Reflections of public perception of Russia-Ukraine conflict and Metaverse on the financial outlook of Metaverse coins: Fresh evidence from Reddit sentiment analysis," International Review of Financial Analysis, Elsevier, volume 93, issue C, DOI: 10.1016/j.irfa.2024.103215.
• Heger, Julia & Min, Aleksey & Zagst, Rudi, 2024, "Analyzing credit spread changes using explainable artificial intelligence," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103315.
• Huang, Yujun, 2024, "Do ESG ETFs provide downside risk protection during Covid-19? Evidence from forecast combination models," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103320.
• Bouazizi, Tarek & Abid, Ilyes & Guesmi, Khaled & Makrychoriti, Panagiota, 2024, "Evolving energies: Analyzing stability amidst recent challenges in the natural gas market," International Review of Financial Analysis, Elsevier, volume 95, issue PA, DOI: 10.1016/j.irfa.2024.103346.
• Moffo, Ahmadou Mustapha Fonton, 2024, "A machine learning approach in stress testing US bank holding companies," International Review of Financial Analysis, Elsevier, volume 95, issue PC, DOI: 10.1016/j.irfa.2024.103476.
• Ben Hamida, Amal & de Peretti, Christian & Belkacem, Lotfi, 2024, "The link between abnormal numbers and price movements of financial securities: How does Benford’s law predict stock returns?," International Review of Financial Analysis, Elsevier, volume 95, issue PC, DOI: 10.1016/j.irfa.2024.103517.
• Yang, Ni & Fernandez-Perez, Adrian & Indriawan, Ivan, 2024, "Spillover between investor sentiment and volatility: The role of social media," International Review of Financial Analysis, Elsevier, volume 96, issue PA, DOI: 10.1016/j.irfa.2024.103643.
• Zhang, Xiaoyun & Guo, Qiang, 2024, "How useful are energy-related uncertainty for oil price volatility forecasting?," Finance Research Letters, Elsevier, volume 60, issue C, DOI: 10.1016/j.frl.2023.104953.
• Baruník, Jozef & Hanus, Luboš, 2024, "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, volume 61, issue C, DOI: 10.1016/j.frl.2024.105003.
• Liu, Dinggao & Chen, Kaijie & Cai, Yi & Tang, Zhenpeng, 2024, "Interpretable EU ETS Phase 4 prices forecasting based on deep generative data augmentation approach," Finance Research Letters, Elsevier, volume 61, issue C, DOI: 10.1016/j.frl.2024.105038.
• Tang, Wenjin & Bu, Hui & Zuo, Yuan & Wu, Junjie, 2024, "Unlocking the power of the topic content in news headlines: BERTopic for predicting Chinese corporate bond defaults," Finance Research Letters, Elsevier, volume 62, issue PA, DOI: 10.1016/j.frl.2024.105062.
• Kirtac, Kemal & Germano, Guido, 2024, "Sentiment trading with large language models," Finance Research Letters, Elsevier, volume 62, issue PB, DOI: 10.1016/j.frl.2024.105227.
• Li, Wei & Zhang, Junchao & Cao, Xiangye & Han, Wei, 2024, "Is the prediction of precious metal market volatility influenced by internet searches regarding uncertainty?," Finance Research Letters, Elsevier, volume 62, issue PB, DOI: 10.1016/j.frl.2024.105269.
• Ma, Feng & Lyu, Zhichong & Li, Haibo, 2024, "Can ChatGPT predict Chinese equity premiums?," Finance Research Letters, Elsevier, volume 65, issue C, DOI: 10.1016/j.frl.2024.105631.
• Chen, Zhenlong & Liu, Junjie & Hao, Xiaozhen, 2024, "Can the ‘good-bad’ volatility and the leverage effect improve the prediction of cryptocurrency volatility?—Evidence from SHARV-MGJR model," Finance Research Letters, Elsevier, volume 67, issue PA, DOI: 10.1016/j.frl.2024.105757.
• Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Ji, Qiang, 2024, "Energy market uncertainties and exchange rate volatility: A GARCH-MIDAS approach," Finance Research Letters, Elsevier, volume 67, issue PB, DOI: 10.1016/j.frl.2024.105847.
• Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024, "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, volume 67, issue PB, DOI: 10.1016/j.frl.2024.105874.
• Nguyen, Hien Thi & Nguyen, Hoang & Tran, Minh-Ngoc, 2024, "Deep learning enhanced volatility modeling with covariates," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106145.
• Liu, Wei-han & Xu, Xingfu, 2024, "Forecasting crude oil price: A deep forest ensemble approach," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106153.
• Bouri, Elie & Gupta, Rangan & Pierdzioch, Christian & Polat, Onur, 2024, "Forecasting U.S. recessions using over 150 years of data: Stock-market moments versus oil-market moments," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106179.
• Wang, Qi & Zhang, Li, 2024, "Are natural resource volatility curses or blessings for economic performance? Stories of resource-rich regions," Finance Research Letters, Elsevier, volume 69, issue PB, DOI: 10.1016/j.frl.2024.106240.
• Kim, Hyeongwoo & Son, Jisoo, 2024, "What charge-off rates are predictable by macroeconomic latent factors?," Journal of Financial Stability, Elsevier, volume 74, issue C, DOI: 10.1016/j.jfs.2024.101301.
• Biswas, Rita & Loungani, Prakash & Liang, Zhongwen & Michaelides, Michael, 2024, "Linkages between financial and macroeconomic indicators in emerging markets and developing economies," Global Finance Journal, Elsevier, volume 62, issue C, DOI: 10.1016/j.gfj.2024.101007.
• Steinmetz, Julia & Jentsch, Carsten, 2024, "Bootstrap consistency for the Mack bootstrap," Insurance: Mathematics and Economics, Elsevier, volume 115, issue C, pages 83-121, DOI: 10.1016/j.insmatheco.2024.01.001.
• Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024, "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 92, issue C, DOI: 10.1016/j.intfin.2024.101975.
• Huang, Zih-Chun & Sangiorgi, Ivan & Urquhart, Andrew, 2024, "Forecasting Bitcoin volatility using machine learning techniques," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 97, issue C, DOI: 10.1016/j.intfin.2024.102064.
• Alexandridis, Antonios K. & Panopoulou, Ekaterini & Souropanis, Ioannis, 2024, "Forecasting exchange rate volatility: An amalgamation approach," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 97, issue C, DOI: 10.1016/j.intfin.2024.102067.
• Iseringhausen, Martin, 2024, "A time-varying skewness model for Growth-at-Risk," International Journal of Forecasting, Elsevier, volume 40, issue 1, pages 229-246, DOI: 10.1016/j.ijforecast.2023.02.006.
• Segnon, Mawuli & Gupta, Rangan & Wilfling, Bernd, 2024, "Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks," International Journal of Forecasting, Elsevier, volume 40, issue 1, pages 29-43, DOI: 10.1016/j.ijforecast.2022.11.007.
• Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024, "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, volume 40, issue 2, pages 430-456, DOI: 10.1016/j.ijforecast.2023.10.010.
• Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2024, "Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates," International Journal of Forecasting, Elsevier, volume 40, issue 2, pages 626-640, DOI: 10.1016/j.ijforecast.2022.04.002.
• Cascaldi-Garcia, Danilo & Ferreira, Thiago R.T. & Giannone, Domenico & Modugno, Michele, 2024, "Back to the present: Learning about the euro area through a now-casting model," International Journal of Forecasting, Elsevier, volume 40, issue 2, pages 661-686, DOI: 10.1016/j.ijforecast.2023.04.005.
• Poutré, Cédric & Dionne, Georges & Yergeau, Gabriel, 2024, "The profitability of lead–lag arbitrage at high frequency," International Journal of Forecasting, Elsevier, volume 40, issue 3, pages 1002-1021, DOI: 10.1016/j.ijforecast.2023.09.001.
• Gonzalo, Jesús & Pitarakis, Jean-Yves, 2024, "Out-of-sample predictability in predictive regressions with many predictor candidates," International Journal of Forecasting, Elsevier, volume 40, issue 3, pages 1166-1178, DOI: 10.1016/j.ijforecast.2023.10.005.
• Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024, "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1521-1538, DOI: 10.1016/j.ijforecast.2024.01.001.
• Berrisch, Jonathan & Ziel, Florian, 2024, "Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1568-1586, DOI: 10.1016/j.ijforecast.2024.01.005.
• Gibbs, Christopher G. & Vasnev, Andrey L., 2024, "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1734-1751, DOI: 10.1016/j.ijforecast.2024.03.002.
• Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024, "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, volume 158, issue C, DOI: 10.1016/j.jbankfin.2023.107035.
• Cheng, Hang & Guo, Hui & Shi, Yongdong, 2024, "Multifactor conditional equity premium model: Evidence from China's stock market," Journal of Banking & Finance, Elsevier, volume 161, issue C, DOI: 10.1016/j.jbankfin.2024.107117.
• Cao, Cong, 2024, "How to better predict the effect of urban traffic and weather on air pollution? Norwegian evidence from machine learning approaches," Journal of Economic Behavior & Organization, Elsevier, volume 221, issue C, pages 544-569, DOI: 10.1016/j.jebo.2024.03.018.
• Zhang, Li & Liang, Chao & Huynh, Luu Duc Toan & Wang, Lu & Damette, Olivier, 2024, "Measuring the impact of climate risk on renewable energy stock volatility: A case study of G20 economies," Journal of Economic Behavior & Organization, Elsevier, volume 223, issue C, pages 168-184, DOI: 10.1016/j.jebo.2024.05.005.
• Clements, Michael P., 2024, "Survey expectations and adjustments for multiple testing," Journal of Economic Behavior & Organization, Elsevier, volume 224, issue C, pages 338-354, DOI: 10.1016/j.jebo.2024.06.009.
• Qiu, Yajie & Deschamps, Bruno & Liu, Xiaoquan, 2024, "Uncertainty and macroeconomic forecasts: Evidence from survey data," Journal of Economic Behavior & Organization, Elsevier, volume 224, issue C, pages 463-480, DOI: 10.1016/j.jebo.2024.06.008.
• Xiao, Wei, 2024, "Initial anchors and limited information in learning-to-forecast experiments," Journal of Economic Behavior & Organization, Elsevier, volume 225, issue C, pages 192-227, DOI: 10.1016/j.jebo.2024.06.038.
• Chen, Heng & Li, Xu & Pei, Guangyu & Xin, Qian, 2024, "Heterogeneous overreaction in expectation formation: Evidence and theory," Journal of Economic Theory, Elsevier, volume 218, issue C, DOI: 10.1016/j.jet.2024.105839.
• Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan & Li, Yan, 2024, "The out-of-sample performance of carry trades," Journal of International Money and Finance, Elsevier, volume 143, issue C, DOI: 10.1016/j.jimonfin.2024.103042.
• Hecq, Alain & Issler, João Victor & Voisin, Elisa, 2024, "A short term credibility index for central banks under inflation targeting: An application to Brazil," Journal of International Money and Finance, Elsevier, volume 143, issue C, DOI: 10.1016/j.jimonfin.2024.103057.
• Bei, Zeyun & Lin, Juan & Zhou, Yinggang, 2024, "No safe haven, only diversification and contagion — Intraday evidence around the COVID-19 pandemic," Journal of International Money and Finance, Elsevier, volume 143, issue C, DOI: 10.1016/j.jimonfin.2024.103069.
• Conlon, Thomas & Cotter, John & Eyiah-Donkor, Emmanuel, 2024, "Forecasting the price of oil: A cautionary note," Journal of Commodity Markets, Elsevier, volume 33, issue C, DOI: 10.1016/j.jcomm.2023.100378.
• Lazar, Emese & Pan, Jingqi & Wang, Shixuan, 2024, "On the estimation of Value-at-Risk and Expected Shortfall at extreme levels," Journal of Commodity Markets, Elsevier, volume 34, issue C, DOI: 10.1016/j.jcomm.2024.100391.
• Ma, Tian & Li, Ganghui & Zhang, Huajing, 2024, "Stock return predictability using economic narrative: Evidence from energy sectors," Journal of Commodity Markets, Elsevier, volume 35, issue C, DOI: 10.1016/j.jcomm.2024.100418.
• Li, Kaixin & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2024, "Forecasting crude oil returns with oil-related industry ESG indices," Journal of Commodity Markets, Elsevier, volume 36, issue C, DOI: 10.1016/j.jcomm.2024.100444.
• Cavicchioli, Maddalena, 2024, "A matrix unified framework for deriving various impulse responses in Markov switching VAR: Evidence from oil and gas markets," The Journal of Economic Asymmetries, Elsevier, volume 29, issue C, DOI: 10.1016/j.jeca.2023.e00349.
• Alves, Renan Santos & Palma, Andreza A., 2024, "The effectiveness of fiscal policy in Brazil through the MIDAS Lens," Journal of Policy Modeling, Elsevier, volume 46, issue 1, pages 113-128, DOI: 10.1016/j.jpolmod.2023.10.004.
• Sen, Abhibasu & Dutta Choudhury, Karabi, 2024, "Forecasting the Crude Oil prices for last four decades using deep learning approach," Resources Policy, Elsevier, volume 88, issue C, DOI: 10.1016/j.resourpol.2023.104438.
• Simionescu, Mihaela & Cifuentes-Faura, Javier, 2024, "The digital economy and energy poverty in Central and Eastern Europe," Utilities Policy, Elsevier, volume 91, issue C, DOI: 10.1016/j.jup.2024.101841.
• Bolivar, Osmar, 2024, "GDP nowcasting: A machine learning and remote sensing data-based approach for Bolivia," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 5, issue 3, DOI: 10.1016/j.latcb.2024.100126.
• Durand, Luigi & Fornero, Jorge Alberto, 2024, "Estimating the output gap in times of COVID-19," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 5, issue 4, DOI: 10.1016/j.latcb.2024.100129.
• Han, Zhao, 2024, "Asymmetric information and misaligned inflation expectations," Journal of Monetary Economics, Elsevier, volume 143, issue C, DOI: 10.1016/j.jmoneco.2023.10.010.
• Gazzani, Andrea & Venditti, Fabrizio & Veronese, Giovanni, 2024, "Oil price shocks in real time," Journal of Monetary Economics, Elsevier, volume 144, issue C, DOI: 10.1016/j.jmoneco.2023.12.005.
• López-Salido, David & Loria, Francesca, 2024, "Inflation at risk," Journal of Monetary Economics, Elsevier, volume 145, issue S, DOI: 10.1016/j.jmoneco.2024.103570.
• Tong, Bin & Diao, Xundi & Li, Xiaoping, 2024, "Forecasting VaRs via hybrid EVT with normal and non-normal filters: A comparative analysis from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, volume 83, issue C, DOI: 10.1016/j.pacfin.2024.102271.
• Pan, Ging-Ginq & Shiu, Yung-Ming & Wu, Tu-Cheng, 2024, "Extrapolation and option-implied kurtosis in volatility forecasting," Pacific-Basin Finance Journal, Elsevier, volume 84, issue C, DOI: 10.1016/j.pacfin.2024.102286.
• Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024, "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, volume 84, issue C, DOI: 10.1016/j.pacfin.2024.102325.
• Huang, Xiaowei & He, Chenyu & Zhang, Man, 2024, "Economic policy uncertainty and capital flows' tail risk in China," Pacific-Basin Finance Journal, Elsevier, volume 85, issue C, DOI: 10.1016/j.pacfin.2024.102370.
• Huynh, Tran & Uebelmesser, Silke, 2024, "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, volume 81, issue C, DOI: 10.1016/j.ejpoleco.2023.102484.
• Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024, "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, volume 234, issue C, DOI: 10.1016/j.jpubeco.2024.105098.
• Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Bouri, Elie, 2024, "Energy-related uncertainty and international stock market volatility," The Quarterly Review of Economics and Finance, Elsevier, volume 95, issue C, pages 280-293, DOI: 10.1016/j.qref.2024.04.005.
• Wang, Lu & Wang, Xing & Liang, Chao, 2024, "Natural gas volatility prediction via a novel combination of GARCH-MIDAS and one-class SVM," The Quarterly Review of Economics and Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.qref.2024.101927.
• du Plessis, Emile, 2024, "Reading between the lines: Quantitative text analysis of banking crises," Research in Economics, Elsevier, volume 78, issue 4, DOI: 10.1016/j.rie.2024.101000.
• Mati, Sagiru & Baita, Abubakar Jamilu & Ismael, Goran Yousif & Abdullahi, Salisu Garba & Samour, Ahmed & Ozsahin, Dilber Uzun, 2024, "Enhancing CO2 emissions prediction in Africa: A novel approach integrating enviroeconomic factors and nature-inspired neural network in the presence of unit root," Renewable Energy, Elsevier, volume 237, issue PA, DOI: 10.1016/j.renene.2024.121561.
• Westphal, Igor, 2024, "The effects of reducing renewable power intermittency through portfolio diversification," Renewable and Sustainable Energy Reviews, Elsevier, volume 197, issue C, DOI: 10.1016/j.rser.2024.114415.
• Manner, Hans & Rodríguez, Gabriel & Stöckler, Florian, 2024, "A changepoint analysis of exchange rate and commodity price risks for Latin American stock markets," International Review of Economics & Finance, Elsevier, volume 89, issue PA, pages 1385-1403, DOI: 10.1016/j.iref.2023.08.021.
• Guo, Yanfeng & Zhao, Huanyu, 2024, "Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China," International Review of Economics & Finance, Elsevier, volume 89, issue PB, pages 446-457, DOI: 10.1016/j.iref.2023.10.004.
• Luo, Tao & Sun, Huaping & Zhang, Lixia & Bai, Jiancheng, 2024, "Do the dynamics of macroeconomic attention drive the yen/dollar exchange market volatility?," International Review of Economics & Finance, Elsevier, volume 89, issue PB, pages 597-611, DOI: 10.1016/j.iref.2023.09.012.
• Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024, "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, volume 90, issue C, pages 123-135, DOI: 10.1016/j.iref.2023.11.014.
• Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Zeng, Zhi-Jian & Gong, Jue, 2024, "Forecasting global stock market volatilities: A shrinkage heterogeneous autoregressive (HAR) model with a large cross-market predictor set," International Review of Economics & Finance, Elsevier, volume 93, issue PB, pages 673-711, DOI: 10.1016/j.iref.2024.05.008.
• Peng, Lijuan & Liang, Chao & Yang, Baoying & Wang, Lu, 2024, "Crude oil volatility forecasting: Insights from a novel time-varying parameter GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, volume 94, issue C, DOI: 10.1016/j.iref.2024.103413.
• Patra, Saswat, 2024, "An empirical analysis of the volume-volatility nexus in crude oil markets under structural breaks: Implications for forecasting," International Review of Economics & Finance, Elsevier, volume 94, issue C, DOI: 10.1016/j.iref.2024.103434.
• He, Mengxi & Wen, Danyan & Xing, Lu & Zhang, Yaojie, 2024, "Industry volatility concentration and the predictability of aggregate stock market volatility," International Review of Economics & Finance, Elsevier, volume 95, issue C, DOI: 10.1016/j.iref.2024.103488.
• Zhang, Xincheng & Wu, Shaojiang, 2024, "Natural resources and sustainable development: Evidence from the dynamic correlation between crude oil and gold market," International Review of Economics & Finance, Elsevier, volume 96, issue PB, DOI: 10.1016/j.iref.2024.103665.
• Semenov, Andrei, 2024, "Overreaction and underreaction to new information and the directional forecast of exchange rates," International Review of Economics & Finance, Elsevier, volume 96, issue PC, DOI: 10.1016/j.iref.2024.103676.
• Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024, "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, volume 67, issue PA, DOI: 10.1016/j.ribaf.2023.102155.
• Yousaf, Imran & Arfaoui, Nadia & Gubareva, Mariya, 2024, "Spillovers and hedging effectiveness between oil and US equity sectors: Evidence from the COVID pre- and post-vaccination phases," Research in International Business and Finance, Elsevier, volume 69, issue C, DOI: 10.1016/j.ribaf.2023.102204.
• Pan, Zhigang & Bai, Zhihong & Xing, Xiaochao & Wang, Zhufeng, 2024, "US inflation and global commodity prices: Asymmetric interdependence," Research in International Business and Finance, Elsevier, volume 69, issue C, DOI: 10.1016/j.ribaf.2024.102245.
• Lohmann, Christian & Ohliger, Thorsten, 2024, "Predicting the cure of a defaulted company: Nonlinear relationships between loan-related variables and the cure probability," Research in International Business and Finance, Elsevier, volume 70, issue PB, DOI: 10.1016/j.ribaf.2024.102395.
• Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2024, "Financial stress and realized volatility: The case of agricultural commodities," Research in International Business and Finance, Elsevier, volume 71, issue C, DOI: 10.1016/j.ribaf.2024.102442.
• Belhadj, Besma, 2024, "Fuzzy multiple regressions for Cross-Section and Panel data," Socio-Economic Planning Sciences, Elsevier, volume 91, issue C, DOI: 10.1016/j.seps.2023.101761.
• Citterio, Alberto, 2024, "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, volume 92, issue C, DOI: 10.1016/j.seps.2024.101818.
• Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024, "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, volume 199, issue C, DOI: 10.1016/j.techfore.2023.123080.
• Ghosh, Indranil & Jana, Rabin K., 2024, "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, volume 200, issue C, DOI: 10.1016/j.techfore.2023.123148.
• Yang, Jinyu & Dong, Dayong & Liang, Chao, 2024, "Climate policy uncertainty and the U.S. economic cycle," Technological Forecasting and Social Change, Elsevier, volume 202, issue C, DOI: 10.1016/j.techfore.2024.123344.
• Zhang, Xincheng, 2024, "Country-level energy-related uncertainties and stock market returns: Insights from the U.S. and China," Technological Forecasting and Social Change, Elsevier, volume 204, issue C, DOI: 10.1016/j.techfore.2024.123437.
• Saâdaoui, Foued & Rabbouch, Hana, 2024, "Financial forecasting improvement with LSTM-ARFIMA hybrid models and non-Gaussian distributions," Technological Forecasting and Social Change, Elsevier, volume 206, issue C, DOI: 10.1016/j.techfore.2024.123539.
• Lamperti, Fabio, 2024, "Unlocking machine learning for social sciences: The case for identifying Industry 4.0 adoption across business restructuring events," Technological Forecasting and Social Change, Elsevier, volume 207, issue C, DOI: 10.1016/j.techfore.2024.123627.
• Alexandros Botsis & Christoph Gortz & Plutarchos Sakellaris, 2024, "Quantifying Qualitative Survey Data with Panel Data Structure," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2024-21, Mar.
• Matthew Agarwala & Matt Burke & Jennifer Doherty-Bigara & Patrycja Klusak & Kamiar Mohaddes, 2024, "Climate Change and Sovereign Risk: A Regional Analysis for the Caribbean," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2024-26, Apr.
• Roshen Fernando, 2024, "Global Economic Impacts of Physical Climate Risks on Agriculture and Energy," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2024-37, Jun.
• Roshen Fernando, 2024, "Impact of Physical Climate Risks on Financial Assets," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2024-38, Jun.
• Roshen Fernando & Warwick McKibbin, 2024, "Global Economic Impacts of Antimicrobial Resistance," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2024-41, Jun.
• Leo Krippner, 2024, "Applications of Vector Autoregressions in Their Scalar Autoregressive Component Form," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2024-71, Dec.
• Matt Burke & Matthew Agarwala & Patrycja Klusak & Kamiar Mohaddes, 2024, "Climate Policy and Sovereign Debt: The Impact of Transition Scenarios on Sovereign Creditworthiness," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2024-73, Dec.
• Kirtac, Kemal & Germano, Guido, 2024, "Sentiment trading with large language models," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 122592, Apr.
• Kumar, Utkarsh & Ahmad, Wasim & Uddin, Gazi Salah, 2024, "Bayesian Markov switching model for BRICS currencies' exchange rates," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 122816, Sep.
• Simionescu, Mihaela & Schneider, Nicolas & Gavurova, Beata, 2024, "A Bayesian vector-autoregressive application with time-varying parameters on the monetary shocks-production network nexus," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 125580, Sep.
• Massimiliano Marcellino & Michael Pfarrhofer, 2024, "Bayesian nonparametric methods for macroeconomic forecasting," Chapters, Edward Elgar Publishing, chapter 5, in: Michael P. Clements & Ana Beatriz Galvão, "Handbook of Research Methods and Applications in Macroeconomic Forecasting".
• Francesco Furno & Domenico Giannone, 2024, "Nowcasting recession risk," Chapters, Edward Elgar Publishing, chapter 7, in: Michael P. Clements & Ana Beatriz Galvão, "Handbook of Research Methods and Applications in Macroeconomic Forecasting".
• Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2024, "Lessons from nowcasting GDP across the world," Chapters, Edward Elgar Publishing, chapter 8, in: Michael P. Clements & Ana Beatriz Galvão, "Handbook of Research Methods and Applications in Macroeconomic Forecasting".
• Christine Amsler & Robert James & Artem Prokhorov & Peter Schmidt, 2024, "Improving Predictions of Technical Inefficiency," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of Subal Kumbhakar", DOI: 10.1108/S0731-905320240000046011.
• Kwame Asiam Addey & John Baptist D. Jatoe, 2024, "Implications of crop yield distributions for multiperil crop insurance rating in Ghana: a lasso model application," Agricultural Finance Review, Emerald Group Publishing Limited, volume 84, issue 2/3, pages 246-265, August, DOI: 10.1108/AFR-05-2024-0078.
• Siddhartha S. Bora & Ani L. Katchova, 2024, "Multi-step commodity forecasts using deep learning," Agricultural Finance Review, Emerald Group Publishing Limited, volume 84, issue 4/5, pages 269-296, September, DOI: 10.1108/AFR-08-2023-0105.
• Bingzi Jin & Xiaojie Xu, 2024, "Predicting wholesale edible oil prices through Gaussian process regressions tuned with Bayesian optimization and cross-validation," Asian Journal of Economics and Banking, Emerald Group Publishing Limited, volume 9, issue 1, pages 64-82, December, DOI: 10.1108/AJEB-06-2024-0070.
• Ehsanul Hassan & Muhammad Awais-E-Yazdan & Ramona Birau & Peter Wanke & Yong Aaron Tan, 2024, "Predicting financial distress in non-financial sector of Pakistan using PCA and logit," International Journal of Islamic and Middle Eastern Finance and Management, Emerald Group Publishing Limited, volume 17, issue 3, pages 485-508, June, DOI: 10.1108/IMEFM-10-2023-0404.
• Vighneswara Swamy & Vijayakumar Narayanamurthy, 2024, "Are private banks more sensitive to changes in reserve requirements? Evidence from an emerging market," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, volume 30, issue 59, pages 79-115, December, DOI: 10.1108/JEFAS-11-2022-0261.
• Souhir Amri Amamou & Mouna Ben Daoud & Saoussen Aguir Bargaoui, 2024, "Green bonds forecasting: evidence from pre-crisis, Covid-19 and Russian–Ukrainian crisis frameworks," Journal of Economic Studies, Emerald Group Publishing Limited, volume 52, issue 1, pages 179-193, June, DOI: 10.1108/JES-01-2024-0061.