Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2024

• Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024, "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, volume 244, issue 2, DOI: 10.1016/j.jeconom.2024.105746.
• 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.
• Peter J. Zeitsch, 2024, "Convertible Bond Arbitrage Smart Beta," Computational Economics, Springer;Society for Computational Economics, volume 63, issue 1, pages 159-192, January, DOI: 10.1007/s10614-022-10335-6.
• Dibyendu Maiti & Naveen Kumar & Debajit Jha & Soumyadipta Sarkar, 2024, "Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)," Computational Economics, Springer;Society for Computational Economics, volume 63, issue 3, pages 1095-1120, March, DOI: 10.1007/s10614-023-10414-2.
• Pierre Rostan & Alexandra Rostan & John Wall, 2024, "Measuring the Resilience to the Covid-19 Pandemic of Eurozone Economies with Their 2050 Forecasts," Computational Economics, Springer;Society for Computational Economics, volume 63, issue 3, pages 1137-1157, March, DOI: 10.1007/s10614-023-10425-z.
• Helong Li & Guanglong Xu & Qin Huang & Rubin Ruan & Weiguo Zhang, 2024, "COVID-19 Impact on Stock Markets: A Multiscale Event Analysis Perspective," Computational Economics, Springer;Society for Computational Economics, volume 63, issue 3, pages 1191-1212, March, DOI: 10.1007/s10614-023-10448-6.
• Yamin Ahmad & Adam Check & Ming Chien Lo, 2024, "Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, volume 63, issue 6, pages 2139-2173, June, DOI: 10.1007/s10614-023-10397-0.
• Efstathios Polyzos & Costas Siriopoulos, 2024, "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 1, pages 225-262, July, DOI: 10.1007/s10614-023-10429-9.
• Aparna Gupta & Vipula Rawte & Mohammed J. Zaki, 2024, "Predicting Firm Financial Performance from SEC Filing Changes Using Automatically Generated Dictionary," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 1, pages 307-334, July, DOI: 10.1007/s10614-023-10443-x.
• Rebecca Westphal & Didier Sornette, 2024, "How Market Intervention can Prevent Bubbles and Crashes: An Agent Based Modelling Approach," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 3, pages 1315-1356, September, DOI: 10.1007/s10614-023-10462-8.
• Maolin Cheng & Bin Liu, 2024, "Quarterly Data Forecasting Method Based on Extended Grey GM(2, 1, Σsin) Model and Its Application in China’s Quarterly GDP Forecasting," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 4, pages 2385-2412, October, DOI: 10.1007/s10614-023-10518-9.
• Shun Chen & Lingling Guo & Lei Ge, 2024, "Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 5, pages 2853-2878, November, DOI: 10.1007/s10614-024-10547-y.
• Aykut Ekinci & Safa Sen, 2024, "Forecasting Bank Failure in the U.S.: A Cost-Sensitive Approach," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 6, pages 3161-3179, December, DOI: 10.1007/s10614-023-10537-6.
• Jie Cheng, 2024, "Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 6, pages 3617-3643, December, DOI: 10.1007/s10614-024-10571-y.
• Andrea Baldin & Trine Bille & Raghava Rao Mukkamala & Ravi Vatrapu, 2024, "The impact of social media activities on theater demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, volume 48, issue 2, pages 199-220, June, DOI: 10.1007/s10824-023-09480-z.
• Batuhan Kilic & Onur Can Bayrak & Fatih Gülgen & Mert Gurturk & Perihan Abay, 2024, "Unveiling the impact of machine learning algorithms on the quality of online geocoding services: a case study using COVID-19 data," Journal of Geographical Systems, Springer, volume 26, issue 4, pages 601-622, October, DOI: 10.1007/s10109-023-00435-8.
• Michael Allan Ribers & Hannes Ullrich, 2024, "Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing," Quantitative Marketing and Economics (QME), Springer, volume 22, issue 4, pages 445-483, December, DOI: 10.1007/s11129-024-09284-1.
• Alessandro Bitetto & Paola Cerchiello & Stefano Filomeni & Alessandra Tanda & Barbara Tarantino, 2024, "Can we trust machine learning to predict the credit risk of small businesses?," Review of Quantitative Finance and Accounting, Springer, volume 63, issue 3, pages 925-954, October, DOI: 10.1007/s11156-024-01278-0.
• Chuxuan Xiao & Winifred Huang & David P. Newton, 2024, "Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models," Review of Quantitative Finance and Accounting, Springer, volume 63, issue 3, pages 979-1006, October, DOI: 10.1007/s11156-024-01279-z.
• Afees A. Salisu & Rangan Gupta & Oguzhan Cepni & Petre Caraiani, 2024, "Oil shocks and state-level stock market volatility of the United States: a GARCH-MIDAS approach," Review of Quantitative Finance and Accounting, Springer, volume 63, issue 4, pages 1473-1510, November, DOI: 10.1007/s11156-024-01295-z.
• Csizmadia, Péter & Kosztyán, Zsolt Tibor & Fehérvölgyi, Beáta & Hausz, Frigyes, 2024, "Intézményi koncentráció és az innovációs hálózatok vizsgálata
  [Institutional concentration and innovation networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), volume 0, issue 12, pages 1351-1380, DOI: 10.18414/KSZ.2024.12.1351.
• Chin Kuo-Hsuan & Lau Chi Ho, 2024, "Inflation Forecast Combination: Evidence from Taiwan," Review of Economics, De Gruyter, volume 75, issue 3, pages 215-231, DOI: 10.1515/roe-2024-0054.
• Bachmann Ronald & Boockmann Bernhard & Vonnahme Christina & Wiemann Jan Simon, 2024, "Internationale Dateninnovationen: Potenziale für die deutsche Arbeitsmarkt- und Sozialpolitik," Zeitschrift für Wirtschaftspolitik, De Gruyter, volume 73, issue 1, pages 1-23, May, DOI: 10.1515/zfwp-2024-2001.
• Paulo Barbosa & João Cortes & João Amador, 2024, "Distance to Export: A Machine Learning Approach with Portuguese Firms," GEE Papers, Gabinete de Estratégia e Estudos, Ministério da Economia, number 182, Jul, revised Jul 2024.
• David Cronin & Niall McInerney, 2024, "Institutional Quality and Official Budgetary Forecast Performance in EU Member States," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, volume 80, issue 2, pages 165-192, DOI: 10.1628/fa-2024-0005.
• Caravaggio, Nicola & Resce, Giuliano & Idola Francesca, Spanò, 2024, "Is Local Taxation Predictable? A Machine Learning Approach," Economics & Statistics Discussion Papers, University of Molise, Department of Economics, number esdp24098, Sep.
• Tomasz Piotr Kostyra, 2024, "Forecasting the yield curve for Poland with the PCA and machine learning," Bank i Kredyt, Narodowy Bank Polski, volume 55, issue 4, pages 459-478.
• Jeff Dominitz & Charles F. Manski, 2024, "Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory," NBER Working Papers, National Bureau of Economic Research, Inc, number 32269, Mar.
• Christiane Baumeister & Florian Huber & Massimiliano Marcellino, 2024, "Risky Oil: It's All in the Tails," NBER Working Papers, National Bureau of Economic Research, Inc, number 32524, May.
• Thorsten Drautzburg & Jesús Fernández-Villaverde & Pablo A. Guerrón-Quintana & Dick Oosthuizen, 2024, "Filtering with Limited Information," NBER Working Papers, National Bureau of Economic Research, Inc, number 32754, Jul.
• Alison W. Baulos & Jorge Luis García & James J. Heckman, 2024, "Perry Preschool at 50: What Lessons Should Be Drawn and Which Criticisms Ignored?," NBER Working Papers, National Bureau of Economic Research, Inc, number 32972, Sep.
• Richard Calvo & Vincent Pons & Jesse M. Shapiro, 2024, "Pitfalls of Demographic Forecasts of US Elections," NBER Working Papers, National Bureau of Economic Research, Inc, number 33016, Oct.
• Ruslan Goyenko & Bryan T. Kelly & Tobias J. Moskowitz & Yinan Su & Chao Zhang, 2024, "Trading Volume Alpha," NBER Working Papers, National Bureau of Economic Research, Inc, number 33037, Oct.
• Iva Glišic, 2024, "A comparison of using MIDAS and LSTM models for GDP nowcasting," Working Papers Bulletin, National Bank of Serbia, number 22, Mar.
• A. Bourgeois & B. Favetto, 2024, "Construction d’intervalles de confiance et relecture du passe avec le modèle Mesange," Documents de Travail de l'Insee - INSEE Working Papers, Institut National de la Statistique et des Etudes Economiques, number 2024-07.
• M. Lenza & I. Moutachaker & I. Moutachaker, 2024, "Density forecasts of inflation : a quantile regression forest approach," Documents de Travail de l'Insee - INSEE Working Papers, Institut National de la Statistique et des Etudes Economiques, number 2024-12.
• A. Quartier-La-Tente, 2024, "Utilisation de modèles de regression à coefficients variant dans le temps pour la prevision conjoncturelle," Documents de Travail de l'Insee - INSEE Working Papers, Institut National de la Statistique et des Etudes Economiques, number 2024-16.
• Lagesh Meethale Aravalath & Subhendu Dutta, 2024, "Forecasting World Food Price Volatility: Performance of the GARCH Model with Different Distributions Assumptions," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 120-141, March.
• Tsvetan Tsvetkov, 2024, "An Innovative Approach for Vulnerability Assessment of a Nuclear Facility’s Physical Protection System," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 4, pages 829-845, December.
• Raphaela Hyee & Herwig Immervoll & Rodrigo Fernández & Jongmi Lee & Karl Handscomb, 2024, "How reliable are social safety nets in situations of acute economic need?: Extended estimates for 14 OECD countries," OECD Social, Employment and Migration Working Papers, OECD Publishing, number 317, Dec.
• Fabrice Murtin & Max Salomon-Ermel, 2024, "Nowcasting subjective well-being with Google Trends: A meta-learning approach," OECD Papers on Well-being and Inequalities, OECD Publishing, number 27, Jun, DOI: 10.1787/cbdfb5d9-en.
• Petra Greso & Karin Klieber, 2024, "The role of inflation subcomponents: applying maximally forward-looking core inflation to euro area countries," OeNB Bulletin, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/2024-1, pages 1-22.
• Friedrich Fritzer, 2024, "The instability of leading indicators in forecasting Austrian inflation: lessons from the COVID-19 pandemic and the energy crisis," OeNB Bulletin, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/24-1, pages 1-18.
• Ștefan RUSU & Marcel BOLOȘ, 2024, "Machine Learning Clustering In Financial Markets: A Literature Review," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, volume 33, issue 1, pages 330-336, July.
• Ke-Li Xu & Junjie Guo, 2024, "A New Test for Multiple Predictive Regression," Journal of Financial Econometrics, Oxford University Press, volume 22, issue 1, pages 119-156.
• Luca Vincenzo Ballestra & Enzo D’Innocenzo & Andrea Guizzardi, 2024, "Score-Driven Modeling with Jumps: An Application to S&P500 Returns and Options," Journal of Financial Econometrics, Oxford University Press, volume 22, issue 2, pages 375-406.
• Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2024, "Volatility Forecasting with Machine Learning and Intraday Commonality," Journal of Financial Econometrics, Oxford University Press, volume 22, issue 2, pages 492-530.
• Donggyu Kim & Minseog Oh & Xinyu Song & Yazhen Wang, 2024, "Factor Overnight GARCH-Itô Models," Journal of Financial Econometrics, Oxford University Press, volume 22, issue 5, pages 1209-1235.
• Jesús Gil Jaime & Jose Olmo, 2024, "Measuring and Testing Systemic Risk from the Cross-Section of Stock Returns†," Journal of Financial Econometrics, Oxford University Press, volume 22, issue 5, pages 1503-1531.
• Monica P Bhatt & Sara B Heller & Max Kapustin & Marianne Bertrand & Christopher Blattman, 2024, "Predicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago," The Quarterly Journal of Economics, President and Fellows of Harvard College, volume 139, issue 1, pages 1-56.
• Sebastian Denk & Gunter Löffler, 2024, "Predicting the Equity Premium with Combination Forecasts: A Reappraisal," The Review of Asset Pricing Studies, Society for Financial Studies, volume 14, issue 4, pages 545-577.
• Yufeng Han & Ai He & David E Rapach & Guofu Zhou, 2024, "Cross-sectional expected returns: new Fama–MacBeth regressions in the era of machine learning," Review of Finance, European Finance Association, volume 28, issue 6, pages 1807-1831.
• Vipul Kumar Singh & Pawan Kumar, 2024, "Effectiveness of deterministic option pricing models: new evidence from Nifty and Bank Nifty Index options," Journal of Asset Management, Palgrave Macmillan, volume 25, issue 2, pages 172-189, March, DOI: 10.1057/s41260-024-00348-1.
• Sakai Ando & Taehoon Kim, 2024, "Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, volume 72, issue 4, pages 1386-1410, December, DOI: 10.1057/s41308-023-00225-8.
• Manuel Monge, 2024, "Trends and persistence in global olive oil prices after COVID-19," Journal of Revenue and Pricing Management, Palgrave Macmillan, volume 23, issue 5, pages 481-488, October, DOI: 10.1057/s41272-024-00481-x.
• Thorsten Drautzburg & Jesus Fernandez-Villaverde & Pablo Guerron-Quintana & Dick Oosthuizen, 2024, "Filtering with Limited Information," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 24-016, Jul.
• Maria S. Mavillonio, 2024, "Textual Representation of Business Plans and Firm Success," Discussion Papers, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy, number 2024/308, May.
• Caterina Giannetti & Maria Saveria Mavillonio, 2024, "Crowdfunding Success: Human Insights vs Algorithmic Textual Extraction," Discussion Papers, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy, number 2024/315, Nov.
• Silva Lopes, Artur, 2024, "Assessing Income Convergence with a Long-Run Forecasting Approach: Some New Results," MPRA Paper, University Library of Munich, Germany, number 120143, Feb, revised Jun 2022.
• Polbin, Andrey & Shumilov, Andrei, 2024, "Прогнозирование Основных Российских Макроэкономических Показателей С Помощью Tvp-Модели С Байесовским Сжатием Параметров
  [Forecasting key Russian macroeconomic variables using a TVP model with Bayesian shrinkage]," MPRA Paper, University Library of Munich, Germany, number 120170.
• Frank, Luis, 2024, "Proyección del Consumo Privado de Argentina por medio de un Modelo de Corrección de Errores
  [Projection of Argentina's Private Consumption through an Error Correction Model]," MPRA Paper, University Library of Munich, Germany, number 121181, Jun.
• Fantazzini, Dean, 2024, "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper, University Library of Munich, Germany, number 121214.
• Foutzopoulos, Giorgos & Pandis, Nikolaos & Tsagris, Michail, 2024, "Predicting full retirement attainment of NBA players," MPRA Paper, University Library of Munich, Germany, number 121540, Jul.
• Mahmood, Asif & Ali, Ringchan, 2024, "A Measure of Financial Conditions for Pakistan," MPRA Paper, University Library of Munich, Germany, number 121952, Sep.
• Yang, Linge, 2024, "Shaping the USDA Agriculture Innovation Agenda: Addressing Agricultural Nonpoint Source Pollution from A Point Source Perspective," MPRA Paper, University Library of Munich, Germany, number 122265, Oct.
• Cherkashin, Alexander & Sakhadzhi, Vladislav & Guliev, Ruslan & Bolshunova, Elena, 2024, "Practical Methods for Predicting Customer Retention," MPRA Paper, University Library of Munich, Germany, number 122400, Oct.
• Черкашин, Александр & Сахаджи, Владислав & Гулиев, Руслан & Большунова, Елена, 2024, "Практические Методы Прогнозирования Сохранения Клиентской Базы (Перевод На Русский Язык)
  [Practical Methods for Predicting Customer Retention]," MPRA Paper, University Library of Munich, Germany, number 122483, Oct.
• Elshin, Leonid & Mikhalevich, Polina & Mingulov, Almaz, 2024, "Эмпирическая Оценка Влияния Экспортно-Импортных Операций На Экономический Рост Регионов Рф В Условиях Внешнего Давления
  [Empirical assessment of the impact of export-import operations on the economic growth of the regions of the Russian Federation," MPRA Paper, University Library of Munich, Germany, number 122704, Sep.
• Elshin, Leonid & Mingulov, Almaz & Mikhalevich, Polina, 2024, "Оценка Перспектив Устойчивого Развития Регионов Рф В Условиях Ограничения Экспортно-Импортных Операций С Кнр
  [Assessment of the Prospects for Sustainable Development of Russian Regions in the Context of Restricted Export-Import Operations with Chi," MPRA Paper, University Library of Munich, Germany, number 122705, Sep.
• Elshin, Leonid & Mingulov, Almaz & Mikhalevich, Polina, 2024, "Потенциал Замедления Экономики Регионов В Условиях Локализации Внешнеэкономической Деятельности С Кнр
  [Potential for a slowdown in regional economies in the context of localization of foreign economic activity with China]," MPRA Paper, University Library of Munich, Germany, number 122706, Sep.
• Elshin, Leonid & Mikhalevich, Polina & Mingulov, Almaz, 2024, "Прогностическая Оценка Устойчивого Развития Импортозависимых Секторов Экономики Региона В Условиях Внешнего Давления
  [Forecast assessment of sustainable development of import-dependent sectors of the regional economy under external pressure]," MPRA Paper, University Library of Munich, Germany, number 122707, Sep.
• Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024, "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper, University Library of Munich, Germany, number 122899, Oct.
• Korobova, Elena & Fantazzini, Dean, 2024, "Stablecoins and credit risk: when do they stop being stable?," MPRA Paper, University Library of Munich, Germany, number 122951.
• Rodriguez, A.E. & Kucsma, Kristen, 2024, "On the Use of the Bass Model for Forecasting Pecuniary Damages: a Reappraisal," MPRA Paper, University Library of Munich, Germany, number 124948, Nov.
• Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024, "Forecasting Growth-at-Risk of the United States: Housing Price versus Housing Sentiment or Attention," Working Papers, University of Pretoria, Department of Economics, number 202401, Jan.
• Massimiliano Caporin & Petre Caraiani & Oguzhan Cepni & Rangan Gupta, 2024, "Predicting the Conditional Distribution of US Stock Market Systemic Stress: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202407, Mar.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024, "Forecasting Realized US Stock Market Volatility: Is there a Role for Economic Policy Uncertainty?," Working Papers, University of Pretoria, Department of Economics, number 202408, Mar.
• Afees A. Salisu & Ahamuefula E.Oghonna & Rangan Gupta & Oguzhan Cepni, 2024, "Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202409, Mar.
• Bruno Tag Sales & Hudson Da Silva Torrent & Rangan Gupta, 2024, "Forecasting Real Housing Price Returns of the United States using Machine Learning: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202412, Mar.
• Oguzhan Cepni & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2024, "Political Geography and Stock Market Volatility: The Role of Political Alignment across Sentiment Regimes," Working Papers, University of Pretoria, Department of Economics, number 202414, Mar.
• Matteo Foglia & Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2024, "Long-Span Multi-Layer Spillovers between Moments of Advanced Equity Markets: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202415, Apr.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Qiang Ji, 2024, "Energy Market Uncertainties and Exchange Rate Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202418, Apr.
• Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2024, "Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging," Working Papers, University of Pretoria, Department of Economics, number 202420, May.
• Thanoj K. Muddana & Komal S.R. Bhimireddy & Anandamayee Majumdar & Rangan Gupta, 2024, "Forecasting Gold Returns Volatility Over 1258-2023: The Role of Moments," Working Papers, University of Pretoria, Department of Economics, number 202421, May.
• Rangan Gupta & Christian Pierdzioch, 2024, "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Working Papers, University of Pretoria, Department of Economics, number 202423, Jun.
• Elie Bouri & Rangan Gupta & Asingamaanda Liphadzi & Christian Pierdzioch, 2024, "Forecasting Stock Returns Volatility of the G7 Over Centuries: The Role of Climate Risks," Working Papers, University of Pretoria, Department of Economics, number 202424, Jun.
• Kejin Wu & Sayar Karmakar & Rangan Gupta, 2024, "GARCHX-NoVaS: A Model-Free Approach to Incorporate Exogenous Variables," Working Papers, University of Pretoria, Department of Economics, number 202425, Jun.
• Rangan Gupta & Christian Pierdzioch & Aviral K. Tiwari, 2024, "Gasoline Prices and Presidential Approval Ratings of the United States," Working Papers, University of Pretoria, Department of Economics, number 202427, Jun.
• Rangan Gupta & Christian Pierdzioch, 2024, "Climate Policy Uncertainty and Financial Stress: Evidence for China," Working Papers, University of Pretoria, Department of Economics, number 202428, Jun.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta, 2024, "Geopolitical Risks and Oil Returns Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202429, Jun.
• Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Sisa Shiba, 2024, "Energy Market Uncertainties and Gold Return Volatility: A GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202431, Jul.
• 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.
• 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.
• Sandra Dreher & Sebastian Eichfelder & Felix Noth, 2024, "Does IFRS information on tax loss carryforwards and negative performance improve predictions of earnings and cash flows?," Journal of Business Economics, Springer, volume 94, issue 1, pages 1-39, January, DOI: 10.1007/s11573-023-01147-7.
• J. Peter Leo Deepak & Yavana Rani Subramanian & J. Josephine Lalitha & K. Vidhya, 2024, "Optimum Level of Currency Reserves: Investigation and Forecasting of Indian Rupee Using ARIMA Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 20, issue 1, pages 137-150, August, DOI: 10.1007/s41549-023-00091-3.
• Maximilian Boeck & Massimiliano Marcellino & Michael Pfarrhofer & Tommaso Tornese, 2024, "Predicting Tail-Risks for the Italian Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 20, issue 3, pages 339-366, November, DOI: 10.1007/s41549-025-00106-1.
• Jörg Döpke & Tim Köhler & Lars Tegtmeier, 2024, "Are they worth it? – An evaluation of predictions for NBA ‘Fantasy Sports’," Journal of Economics and Finance, Springer;Academy of Economics and Finance, volume 48, issue 1, pages 142-165, March, DOI: 10.1007/s12197-023-09646-7.
• 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.