Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2024

• 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.
• Pål Boug & Håvard Hungnes & Takamitsu Kurita, 2024, "The empirical modelling of house prices and debt revisited: a policy-oriented perspective," Empirical Economics, Springer, volume 66, issue 1, pages 369-404, January, DOI: 10.1007/s00181-023-02461-3.
• Zirui Guo & Yihan Li & Guangyan Jia, 2024, "Research on the effectiveness of the volatility–tail risk-managed portfolios in China’s market," Empirical Economics, Springer, volume 66, issue 3, pages 1191-1222, March, DOI: 10.1007/s00181-023-02493-9.
• Thomas F. P. Wiesen & Paul M. Beaumont, 2024, "A joint impulse response function for vector autoregressive models," Empirical Economics, Springer, volume 66, issue 4, pages 1553-1585, April, DOI: 10.1007/s00181-023-02496-6.
• Michal Franta & Jan Libich, 2024, "Holding the economy by the tail: analysis of short- and long-run macroeconomic risks," Empirical Economics, Springer, volume 66, issue 4, pages 1443-1489, April, DOI: 10.1007/s00181-023-02514-7.
• Zhikai Zhang & Yaojie Zhang & Yudong Wang, 2024, "Forecasting the equity premium using weighted regressions: Does the jump variation help?," Empirical Economics, Springer, volume 66, issue 5, pages 2049-2082, May, DOI: 10.1007/s00181-023-02521-8.
• Huawei Niu & Tianyu Liu, 2024, "Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model," Empirical Economics, Springer, volume 67, issue 1, pages 75-96, July, DOI: 10.1007/s00181-023-02551-2.
• Robert Lehmann, 2024, "A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting," Empirical Economics, Springer, volume 67, issue 2, pages 817-838, August, DOI: 10.1007/s00181-024-02566-3.
• Nima Nonejad, 2024, "Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark," Empirical Economics, Springer, volume 67, issue 4, pages 1497-1539, October, DOI: 10.1007/s00181-024-02599-8.
• Fameliti Stavroula & Skintzi Vasiliki, 2024, "Macroeconomic attention and commodity market volatility," Empirical Economics, Springer, volume 67, issue 5, pages 1967-2007, November, DOI: 10.1007/s00181-024-02613-z.
• Yasmeen Bayaa & Mahmoud Qadan, 2024, "Interest rate uncertainty and the shape of the yield curve of U.S. treasury bonds," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 14, issue 4, pages 981-1003, December, DOI: 10.1007/s40822-024-00278-8.
• Aktham Maghyereh & Salem Adel Ziadat, 2024, "Pattern and determinants of tail-risk transmission between cryptocurrency markets: new evidence from recent crisis episodes," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-34, December, DOI: 10.1186/s40854-023-00592-1.
• Xiaozhen Jing & Dezhong Xu & Bin Li & Tarlok Singh, 2024, "Does the U.S. extreme indicator matter in stock markets? International evidence," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-27, December, DOI: 10.1186/s40854-024-00610-w.
• Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024, "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-30, December, DOI: 10.1186/s40854-024-00625-3.
• Malvina Marchese & María Dolores Martínez-Miranda & Jens Perch Nielsen & Michael Scholz, 2024, "Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-16, December, DOI: 10.1186/s40854-024-00657-9.
• Fred Espen Benth & Carlo Sgarra, 2024, "A Barndorff-Nielsen and Shephard model with leverage in Hilbert space for commodity forward markets," Finance and Stochastics, Springer, volume 28, issue 4, pages 1035-1076, October, DOI: 10.1007/s00780-024-00546-0.
• Ioannis Sitzimis, 2024, "Forecasting methods in Greek coastal shipping: The case of Southwest Crete," Future Business Journal, Springer, volume 10, issue 1, pages 1-16, December, DOI: 10.1186/s43093-024-00352-2.
• Leila Hedhili Zaier & Khaled Mokni & Ahdi Noomen Ajmi, 2024, "Causality relationships between climate policy uncertainty, renewable energy stocks, and oil prices: a mixed-frequency causality analysis," Future Business Journal, Springer, volume 10, issue 1, pages 1-11, December, DOI: 10.1186/s43093-024-00399-1.
• 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.
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• 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".
• 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.
• Dinci J. Penzin & Kazeem O. Isah & Afees A. Salisu, 2024, "Climate change-stock return volatility nexus in advanced economies: the role of technology shocks," Journal of Economic Studies, Emerald Group Publishing Limited, volume 52, issue 1, pages 119-135, May, DOI: 10.1108/JES-08-2023-0419.
• Luan Thanh Le & Trang Xuan-Thi-Thu, 2024, "Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood," Maritime Business Review, Emerald Group Publishing Limited, volume 9, issue 3, pages 243-262, July, DOI: 10.1108/MABR-10-2023-0074.
• A. Szepeluk & D. Tomczyszyn & A. Cyburt, 2024, "Application of Technical Analysis Stochastic Oscillator for Early Detection of Epidemiological Changes Based on Covid-19 Data in Poland," European Research Studies Journal, European Research Studies Journal, volume 0, issue 3, pages 1069-1082.
• Oksana Kiforenko & Iwona Szczepaniak, 2024, "The War’s Impact on Ukraine’s Agricultural Production – Projections Vs. Real Data," European Research Studies Journal, European Research Studies Journal, volume 0, issue 3, pages 241-269.
• Bartosz Przysucha & Piotr Bednarczuk & Wlodzimierz Martyniuk & Ewa Golec & Michal Jasienski & Damian Pliszczuk, 2024, "Monte Carlo Simulation as a Demand Forecasting Tool," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 103-113.
• Pawel Rymarczyk & Cezary Figura & Lukasz Wojciechowski & Kamila Cwik & Piotr Stalinski, 2024, "Evaluating the Effectiveness of Advertising Campaigns in the Fast-Food Industry Using an Analytical Engine," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 126-136.
• Pawel Olszewski & Leszek Gil & Natalia Rak & Tomasz Wolowiec & Michal Jasienski, 2024, "Construction of Regression Models Predicting Lead Times and Classification Models," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 179-189.
• Krzysztof Krol & Pawel Kaleta & Dariusz Kasperek & Sylwia Skrzypek-Ahmed & Emanuel Jozefacki & Agnieszka Chmielowska-Marmucka, 2024, "Analysis System for Logistics and Production Processes: A Methodological Approach to Signal Analysis for Forecasting," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 59-71.
• Tomasz Smutek & Jan Sikora & Sylwester Bogacki & Marek Rutkowski & Dariusz Wozniak, 2024, "Use of Autoencoder and One-Hot Encoding for Customer Segmentation," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 72-82.
• Boris Fisera & Filip Ostrihon, 2024, "Constructing Prediction Regions for Exchange Rate Path Forecasts: The Potential of Calibration," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, volume 74, issue 4, pages 432-472, October.
• Jiri Kukacka & Erik Zila, 2024, "Wealth, Cost, and Misperception: Empirical Estimation of Three Interaction Channels in a Financial-Macroeconomic Agent-Based Model," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2024/22, May, revised May 2024.
• Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024, "What drives the European carbon market? Macroeconomic factors and forecasts," Working Papers, Fondazione Eni Enrico Mattei, number 2024.02, Feb.
• Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024, "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2022-16b, Feb, DOI: 10.29338/wp2022-16b.
• Edward S. Knotek & Saeed Zaman, 2024, "Nowcasting Inflation," Working Papers, Federal Reserve Bank of Cleveland, number 24-06, Mar, DOI: 10.26509/frbc-wp-202406.
• Ina Hajdini & Andre Kurmann, 2024, "Predictable Forecast Errors in Full-Information Rational Expectations Models with Regime Shifts," Working Papers, Federal Reserve Bank of Cleveland, number 24-08, Apr, DOI: 10.26509/frbc-wp-202408.
• Todd E. Clark & Gergely Ganics & Elmar Mertens, 2024, "Constructing Fan Charts from the Ragged Edge of SPF Forecasts," Working Papers, Federal Reserve Bank of Cleveland, number 22-36R, Aug, DOI: 10.26509/frbc-wp-202236r.
• Michael Clements & Robert W. Rich & Joseph Tracy, 2024, "An Investigation into the Uncertainty Revision Process of Professional Forecasters," Working Papers, Federal Reserve Bank of Cleveland, number 24-19, Sep, DOI: 10.26509/frbc-wp-202419.
• Kurt Graden Lunsford & Kenneth D. West, 2024, "An Empirical Evaluation of Some Long-Horizon Macroeconomic Forecasts," Working Papers, Federal Reserve Bank of Cleveland, number 24-20, Sep, DOI: 10.26509/frbc-wp-202420.
• Hana Braitsch & James Mitchell & Taylor Shiroff, 2024, "Practice Makes Perfect: Learning Effects with Household Point and Density Forecasts of Inflation," Working Papers, Federal Reserve Bank of Cleveland, number 24-25, Nov, DOI: 10.26509/frbc-wp-202425.
• Jesus Cañas & Aparna Jayashankar & Emily Kerr & Diego Morales-Burnett, 2024, "Texas Manufacturing Outlook Survey: Survey Methodology, Performance and Forecast Accuracy," Working Papers, Federal Reserve Bank of Dallas, number 2402, Mar, DOI: 10.24149/wp2402.
• Mohammad R. Jahan-Parvar & Charles Knipp & Pawel J. Szerszen, 2024, "Trend-Cycle Decomposition and Forecasting Using Bayesian Multivariate Unobserved Components," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2024-100, Dec, DOI: 10.17016/FEDS.2024.100.
• Miguel Faria-e-Castro & Fernando Leibovici, 2024, "Artificial Intelligence and Inflation Forecasts," Review, Federal Reserve Bank of St. Louis, volume 106, issue 12, pages 1-14, November, DOI: 10.20955/r.2024.12.
• Sergey V. Arzhenovskiy, 2024, "Forecasting GDP Dynamics Based on the Bank of Russia’s Enterprise Monitoring Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 31-44, February, DOI: 10.31107/2075-1990-2024-1-31-44.
• Ștefan Rusu & Marcel Ioan Boloș & Marius Leordeanu, 2024, "Comparative analysis of regression models for stock price prediction: Linear, support vector, polynomial, and LASSO," Journal of Financial Studies, Institute of Financial Studies, volume 9, issue 17, pages 143-156, November, DOI: 10.55654/JFS.2024.9.17.09.
• Konstantinos Kofidis & Cătălina Lucia Cocianu, 2024, "Comparative analysis of RF, SVR with Gaussian kernel and LSTM for predicting loan defaults," Journal of Financial Studies, Institute of Financial Studies, volume 9, issue 17, pages 91-106, November, DOI: 10.55654/JFS.2024.9.17.06.
• Vera Barinova & Margarita Gvozdeva & Stepan Zemtsov, 2024, "Small and medium-sized enterprises in Russia in the context of sanctions," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2024-1330, revised 2024.
• Rangan Gupta & Anandamayee Majumdar & Christian Pierdzioch & Onur Polat, 2024, "Climate Risks and Real Gold Returns over 750 Years," Forecasting, MDPI, volume 6, issue 4, pages 1-16, October.
• Prodosh Eugene Simlai, 2024, "Risk Characterization of Firms with ESG Attributes Using a Supervised Machine Learning Method," JRFM, MDPI, volume 17, issue 5, pages 1-9, May.
• Dean Fantazzini, 2024, "Adaptive Conformal Inference for Computing Market Risk Measures: An Analysis with Four Thousand Crypto-Assets," JRFM, MDPI, volume 17, issue 6, pages 1-44, June.
• Rangan Gupta & Christian Pierdzioch, 2024, "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Mathematics, MDPI, volume 12, issue 18, pages 1-26, September.
• Muhammad Akmal Farouqi & Gigih Fitrianto, 2024, "Systemic Effects on Intersectoral Linkages: Framework and Analysis," Gadjah Mada Economics Working Paper Series, Department of Economics, Faculty of Economics and Business, Universitas Gadjah Mada, number 202403001, Mar.
• Matias Quiroz & Laleh Tafakori & Hans Manner, 2024, "Forecasting Realized Covariances Using HAR-Type Models," Graz Economics Papers, University of Graz, Department of Economics, number 2024-20, Dec.
• Tobias Eibinger & Karl W. Steininger & Hans Manner, 2024, "The Development of Austrian Greenhouse Gas Emissions since 2021," Graz Economics Papers, University of Graz, Department of Economics, number 2024-23, Dec.
• Gary Cornwall & Marina Gindelsky, 2024, "House Prices, Debt Burdens, and the Heterogeneous Effects of Mortgage Rate Shocks," Working Papers, The George Washington University, The Center for Economic Research, number 2024-003, Sep.