Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2026

• Guanglan Zhou & Ziyi Wu, 2026, "A resilient model for trade volume forecasting under economic uncertainty: Addressing challenges in the global supply chain," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, volume 29, issue 1, pages 207-224, March, DOI: 10.15240/tul/001/2026-1-013.
• Shahryar Ghorbani & Figen Yildirim & Ali Altug Bicer & Reza Rostamzadeh & Jonas Saparauskas, 2026, "Forecasting major currency exchange rates using long short-term memory networks: Evidence from multi-currency time series analysis," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, volume 29, issue 2, pages 220-239, July, DOI: 10.15240/tul/001/2026-2-014.
• Alfonso Ugarte, 2026, "Panel Local Projections without Fixed-Effects: A Cumulative-Difference OLS Estimator," Working Papers, BBVA Bank, Economic Research Department, number 26/09, Jun.
• Sergio A. Lago Alves & Waldyr Dutra Areosa & Carlos Viana de Carvalho, 2026, "Beating the “pros” with a semi-structural model of their own inflation forecasts," Staff Working Papers, Bank of Canada, number 26-11, Mar, DOI: 10.34989/swp-2026-11.
• Gabriel Rodriguez Rondon & Jean-Marie Dufour & Md. Nazmul Ahsan, 2026, "Estimation and Inference for Stochastic Volatility Models with Heavy-Tailed Distributions," Staff Working Papers, Bank of Canada, number 26-8, Mar, DOI: 10.34989/swp-2026-8.
• Irma Alonso-Alvarez & Iván Kataryniuk & David López-Salido & Javier J. Pérez, 2026, "Geopolitical Oil-Supply Scenarios and Macroeconomic Risks: A Bayesian Toolkit for Policy Analysis," Occasional Papers, Banco de España, number 2616, Sep, DOI: https://doi.org/10.53479/44325.
• Andrea De Polis & Leonardo Melosi & Ivan Petrella, 2026, "The Taming of the Skew: Asymmetric Inflation Risk and Monetary Policy," Working Papers, Banco de España, number 2626, Sep, DOI: https://doi.org/10.53479/44247.
• Luca Bacco & Tiziana Laureti & Juri Marcucci & Luigi Palumbo & Daniele Sasso & Luca Vollero, 2026, "Nowcasting the Italian consumer price index using online prices and machine learning," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 1026, Jun.
• Andrea Del Monaco & Luigi Longo & Juri Marcucci & Irene Tafani, 2026, "Reddit's 'pulse' on US inflation: forecasting with large language models," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 1028, Jun.
• Michela Eugenia Pasetto, 2026, "A method for forecasting unquoted shares of non-financial corporations in the Italian financial accounts," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 1037, Jul.
• Donato Ceci & Claudia Pacella & Fabrizio Venditti, 2026, "Consumption and saving in the euro area after COVID: a scenario analysis," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 1047, Jul.
• Filippo Natoli & Sharath Sonti, 2026, "Overconfident forecasters and the impact of inflation information: evidence from a randomized survey experiment," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1532, Apr.
• Rocío Clara A. Mora-Quiñones & Antonio José Orozco-Gallo & Dora Alicia Mora-Pérez, 2026, "Sentiment and Uncertainty Indices from economic news in Colombia," Borradores de Economia, Banco de la Republica de Colombia, number 1340, Jan, DOI: 10.32468/be.1340.
• Aarón Levi Garavito-Acosta & Wilmer Martinez-Rivera & Camilo González-Sabogal & Johanna Barbosa-Buitrago & Nathaly Vergel-Serrano, 2026, "Determinantes y pronóstico de la cuenta corriente para Colombia," Borradores de Economia, Banco de la Republica de Colombia, number 1360, Aug.
• Renato Vassallo & Margherita Philipp & Christopher Rauh & Hannes Mueller & Laura Mayoral, 2026, "Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events," Working Papers, Barcelona School of Economics, number 1555, Jan.
• Ramón Talvi Robledo & Christopher Rauh & Ben Seimon & Hannes Mueller & Laura Mayoral, 2026, "Forecasting Forced Displacement Flows Using Machine Learning with Text Data," Working Papers, Barcelona School of Economics, number 1573, Apr.
• Doan Gia Bao Ngoc & Luu Minh Quan & Truong Thi Thanh Ha & Nguyen Duc Minh Tan & Phan Thi Minh Huyen & Tran Duy Thanh, 2026, "Building a new hybrid machine learning model for improvement insurance cross-sell prediction," Ho Chi Minh City Open University Journal of Science - Economics and Business Administration, Ho Chi Minh City Open University Journal of Science, Ho Chi Minh City Open University, volume 16, issue 1, pages 93-111, DOI: 10.46223/HCMCOUJS.econ.en.16.1.4306.
• Zhanna Shuvalova, 2026, "Forecasting Fixed Capital Investment with Patent Activity Indicators," Russian Journal of Money and Finance, Bank of Russia, volume 85, issue 2, pages 37-66, June.
• Danila Ovechkin, 2026, "Estimation and forecasting with a Nonlinear Phillips Curve based on heterogeneous sensitivity between economic activity and CPI components," Bank of Russia Working Paper Series, Bank of Russia, number wps161, Jan.
• Alexander Eliseev & Sergei Seleznev, 2026, "Fake Date Tests: Can We Trust In-sample Accuracy of LLMs in Macroeconomic Forecasting?," Bank of Russia Working Paper Series, Bank of Russia, number wps167, Mar.
• Oguzhan Cepni & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2026, "Political Geography and Stock Market Volatility: The Role of Political Alignment Across Sentiment Regimes," Scottish Journal of Political Economy, Scottish Economic Society, volume 73, issue 1, February, DOI: 10.1111/sjpe.70028.
• Yuriy Gorodnichenko & Vittal Vasudevan, 2026, "Macroeconomic Expectations in a War," Scottish Journal of Political Economy, Scottish Economic Society, volume 73, issue 3, July, DOI: 10.1111/sjpe.70064.
• Hilde C. Bjørnland & Nicolás Hardy & Dimitris Korobilis, 2026, "Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 03/2026, Apr.
• Nicolas Hardy & Dimitris Korobilis, 2026, "Generalized Bayesian Composite Quantile Regression with an Application to Equity Premium Forecasting," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 04/2026, Apr.
• Tom Doan, 2026, "MATHESONSTAVREVEL2013: RATS programs to replicate Matheson-Stavrev(2013) non-linear state-space model," Statistical Software Components, Boston College Department of Economics, number RTJ00054, revised .
• Panagiotis Delis & Georgios Kontogeorgos, 2026, "Outlier-robust evaluation of fixed-event macroeconomic survey expectations," Working Papers, Bank of Greece, number 356, Jan, DOI: 10.52903/wp2026356.
• Dimitrios P. Louzis, 2026, "Trend inflation and inflation expectations in high dimensional vector autoregressions," Working Papers, Bank of Greece, number 360, Mar, DOI: 10.52903/wp2026360.
• Zacharias Bragoudakis & Alexandros Karakitsios & Evangelia Kasimati, 2026, "Short-term inflation projections: Τhe new BOG’STIP model," Working Papers, Bank of Greece, number 363, Jun, DOI: 10.52903/wp2026363.
• Nonejad Nima, 2026, "Out-of-Sample Density Prediction of the End-of-Month Price of Crude Oil and the U.S. Economic Policy Uncertainty Index," Journal of Time Series Econometrics, De Gruyter, volume 18, issue 1, pages 1-47, DOI: 10.1515/jtse-2025-0007.
• Psaradakis Zacharias & Sola Martin & Spagnolo Nicola & Yunis Patricio, 2026, "Predictive Accuracy of Impulse Responses Estimated Using Local Projections and Vector Autoregressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 30, issue 3, pages 431-441, DOI: 10.1515/snde-2024-0053.
• Liu Ruipeng & Segnon Mawuli & Gupta Rangan & Bouri Elie, 2026, "Conventional and Unconventional Monetary Policy Rate Uncertainty and Stock Market Volatility: A Forecasting Perspective," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 30, issue 4, pages 595-619, DOI: 10.1515/snde-2024-0108.
• Leon-Gonzalez Roberto & Majoni Blessings, 2026, "Approximate Factor Models with a Common Multiplicative Factor for Stochastic Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 30, issue 4, pages 649-678, DOI: 10.1515/snde-2024-0103.
• Abbara Omar & Zevallos Mauricio, 2026, "On the Estimation of Asymmetric Long Memory Stochastic Volatility Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 30, issue 4, pages 847-867, DOI: 10.1515/snde-2024-0092.
• Mayoral, L. & Mueller, H. & Philipp, M. & Rauh, C. & Vassallo, R., 2026, "Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2609, Jan.
• Congressional Budget Office, 2026, "The Accuracy of CBO's Budget Projections for Fiscal Year 2025," Reports, Congressional Budget Office, number 61916, Jan.
• Xu, Yongdeng, 2026, "Learn the measure, estimate the moment: machine-learned drivers in dynamic conditional correlation models," Cardiff Economics Working Papers, Cardiff University, Cardiff Business School, Economics Section, number E2026/12, Sep.
• Karanasos, Menelaos & Xu, Yongdeng & Yfanti, Stavroula & Zopounidis, Constantin, 2026, "Enforcing an Admissible Parameter Space for Vector MEM: The Fundamental Role of Matrix Inequality Constraints," Cardiff Economics Working Papers, Cardiff University, Cardiff Business School, Economics Section, number E2026/3, Mar.
• Rouven Beiner & Bernd Süssmuth, 2026, "Monotonic Polynomial GARCH Models for Conditional Higher Moments," CESifo Working Paper Series, CESifo, number 12734.
• Veni Arakelia & Guglielmo Maria Caporale & Mirto M. Gasparinatou & Menelaos Karanasos, 2026, "Machine Learning and Liquidity Dynamics in European Stock Markets," CESifo Working Paper Series, CESifo, number 12829.
• Vegard H. Larsen & Leif Anders Thorsrud, 2026, "Zero-Shot Conditional Forecasting and the Information Content of Central Bank Paths," CESifo Working Paper Series, CESifo, number 12972.
• Jesús Fernández-Villaverde & Carlos Sanz, 2026, "Social Sorting and Political Cleavages: Evidence from 2.5 Million Electoral Candidates," CESifo Working Paper Series, CESifo, number 13005.
• Uluc Aysun & Melanie Guldi, 2026, "Revisiting exchange rate predictability: Can machine learning with theoretical filtering outperform canonical models?," Working Papers, University of Central Florida, Department of Economics, number 2026-01, Jan.
• Dalibor Stevanovic, 2026, "Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure," CIRANO Working Papers, CIRANO, number 2026s-06, Apr.
• Filip Blaha & Jan Botka & Josef Sveda & Ales Michl, 2026, "AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights," Working Papers, Czech National Bank, Research and Statistics Department, number 2026/09, Apr.
• Juan Carlos Gutiérrez-Betancur, 2026, "Del tsunami al terremoto demográfico en Colombia," Documentos de Trabajo de Valor Público, Universidad EAFIT, number 023626.
• Carlos Palomino Selem & Ruth Milagros Delgado Yana, 2026, "Comparative analysis between traditional momentum and machine learning (random forest): evidence from the S&P 500 (2000-2024)," Revista Tendencias, Universidad de Narino, volume 27, issue 02, pages 32-61, July, DOI: 10.22267/rtend.26272.296.
• Ghezzi, Fabrizio & Timmermann, Allan & Yang, Max, 2026, "Gauging Hourly Economic Activity in Your Neighborhood," CEPR Discussion Papers, Centre for Economic Policy Research, number 21098, Jan.
• Hauzenberger, Niko & Marcellino, Massimiliano & Pfarrhofer, Michael & Stelzer, Anna, 2026, "Direct Gaussian Process Predictive Regressions with Mixed Frequency Data," CEPR Discussion Papers, Centre for Economic Policy Research, number 21214, Feb.
• Guo, Hongfei & Marín, Juan Miguel & Veiga, Helena, 2026, "Target-Driven Bayesian Stacking of Realized and Implied Volatility Forecasts," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 49851, Apr.
• Guo, Hongfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2026, "Diagnosing and Stabilizing Dynamic Correlations in Multivariate Stochastic Volatility Models," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 50561, Jul.
• Guo, Honfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2026, "Testing Whether Volatility Model Gains Persist: A Prespecified Holdout in Tail Risk Forecasting," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 50798, Sep.
• Çiğdem LAZOĞLU & Uğur KARABEY, 2026, "Evaluating the impact of stochastic interest rates and COVID-19 on financial performance under IFRS 17," JODE - Journal of Demographic Economics, Cambridge University Press, volume 92, issue 2, pages 277-296, June, DOI: 10.1017/dem.2024.25.
• Moench, Emanuel & Stein, Tobias, 2026, "Equity Premium Predictability over the Business Cycle," Journal of Financial and Quantitative Analysis, Cambridge University Press, volume 61, issue 3, pages 1216-1246, May.
• Valérie Mignon & Marc Joëts & Christophe Hurlin, 2026, "ZICO: A Credit Scoring Approach to Detecting Zombie Papers," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2026-18.
• Eraslan, Sercan & Fabbri, Andrea & Saiz, Lorena, 2026, "Short-term forecasting of euro area economic activity in an uncertain world," Economic Bulletin Articles, European Central Bank, volume 8.
• Chahad, Mohammed & Mogliani, Matteo & Bańbura, Marta & Kulikov, Dmitry & Montes-Galdón, Carlos & Landau, Bettina & Meunier, Baptiste & Odendahl, Florens & Paredes, Joan & Sigwalt, Antoine & Theofilako, 2026, "Macro-at-Risk in the euro area Expert Group on Macro-at-Risk Time-Series Workstream," Occasional Paper Series, European Central Bank, number 396, Aug.
• Szörfi, Béla, 2026, "The factors behind output gap revisions," Occasional Paper Series, European Central Bank, number 399, Sep.
• Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2026, "A robust approach to tilting: parametric relative entropy," Working Paper Series, European Central Bank, number 3200, Mar.
• Delgado-Téllez, Mar & Ceglar, Andrej & Spiteri, Sarah & Lebouteiller, Léonore & Vorderobermeier, Nicole, 2026, "Beat the heat, the role of heat waves and droughts in regional EU economies," Working Paper Series, European Central Bank, number 3248, Jun.
• Consolo, Agostino & Foroni, Claudia & Lissona, Claudio & Schroeder, Christofer, 2026, "Forecasting the euro area job vacancy rate with earnings calls data," Working Paper Series, European Central Bank, number 3269, Aug.
• Nocciola, Luca & Scaglioni, Samuele, 2026, "Learning probability of default and stress testing," Working Paper Series, European Central Bank, number 3277, Aug.
• Mertens, Elmar & Clark, Todd E., 2026, "Entropic tilting of forecasts to SPF histograms: analytics & applications," Working Paper Series, European Central Bank, number 3284, Sep.
• Fonseca, Luís & Martorana, Giulia & Schupp, Fabian & Trebbi, Giovanni, 2026, "Inflation narratives and risk premia," Working Paper Series, European Central Bank, number 3288, Sep.
• Schröder, Maximilian, 2026, "Multivariate uncertainty and distributional transmission," Working Paper Series, European Central Bank, number 3290, Sep.
• Jiang, Min & Shi, Jichuan & Zheng, Yukai & Zhou, Wei, 2026, "The role of alternative data in micro-enterprises’ credit risk assessment in China — Empirical evidence based on machine learning," Journal of Behavioral and Experimental Finance, Elsevier, volume 49, issue C, DOI: 10.1016/j.jbef.2026.101154.
• Mahler, Daniel Gerszon & Schoch, Marta & Lakner, Christoph & Nguyen, Minh Cong, 2026, "A parsimonious approach to predicting income distributions," Journal of Development Economics, Elsevier, volume 180, issue C, DOI: 10.1016/j.jdeveco.2025.103695.
• Yang, Zheng & Wu, Haocheng & Kuo, Biing-Shen & Ma, Yongkai, 2026, "Forecasting Chinese equity premium: A dimensionality reduction combination approach," Journal of Economic Dynamics and Control, Elsevier, volume 186, issue C, DOI: 10.1016/j.jedc.2026.105308.
• Labonne, Paul & Thorsrud, Leif Anders, 2026, "Risky news and credit market sentiment," Journal of Economic Dynamics and Control, Elsevier, volume 187, issue C, DOI: 10.1016/j.jedc.2026.105331.
• Chironna, Gianpiero & Orlando, Giuseppe, 2026, "Predicting bank defaults in Italy: A comparative analysis of conventional and machine learning approaches," Economic Analysis and Policy, Elsevier, volume 89, issue C, pages 788-833, DOI: 10.1016/j.eap.2025.12.002.
• De Rosa, Mauricio & Vilá, Joan, 2026, "Taxing the rich in Latin America: Effects of a wealth tax on revenue and distribution," Economic Analysis and Policy, Elsevier, volume 90, issue C, pages 1440-1466, DOI: 10.1016/j.eap.2026.02.016.
• Koppolu, Sarath Chandra & Hoeschle, Lisa & Maruejols, Lucie, 2026, "Potential for energy poverty reduction by error decomposition with machine learning," Economic Analysis and Policy, Elsevier, volume 90, issue C, pages 417-435, DOI: 10.1016/j.eap.2026.01.032.
• Goosen, Kasper & de Vette, Nander & Willem van den End, Jan, 2026, "The impact of uncertainty on economic tail risk: bank capital as mitigating factor," Economic Analysis and Policy, Elsevier, volume 91, issue C, pages 1469-1485, DOI: 10.1016/j.eap.2026.05.001.
• Yanotti, María B. & Navon, Yonatan & de Silva, Ashton & Angelopoulos, Sveta & Sinclair, Sarah, 2026, "The walking economy: Pedestrian counts as a real-time indicator of regional economic activity," Economic Analysis and Policy, Elsevier, volume 92, issue C, pages 641-662, DOI: 10.1016/j.eap.2026.06.032.
• Shah, Sayar Ahmad & Garg, Bhavesh, 2026, "Dynamics of exchange rate pass-through: The role of pricing strategies and economic shocks," Economic Modelling, Elsevier, volume 154, issue C, DOI: 10.1016/j.econmod.2025.107353.
• Raj, Prakash & Selvaraju, N., 2026, "Bitcoin volatility modeling with realized measures and jump dynamics," Economic Modelling, Elsevier, volume 160, issue C, DOI: 10.1016/j.econmod.2026.107615.
• Zeng, Tao & Wang, Kaixin & Fan, Yanjing & Liu, Xiaobin, 2026, "Systemic default probability and return predictability: Evidence from China," Economic Modelling, Elsevier, volume 160, issue C, DOI: 10.1016/j.econmod.2026.107617.
• Prüser, Jan & Blagov, Boris, 2026, "Improving inference and forecasting in VAR models using cross-sectional information," Economic Modelling, Elsevier, volume 160, issue C, DOI: 10.1016/j.econmod.2026.107618.
• Pan, Zhiyuan & Zhou, Yue & Huang, Chuwen, 2026, "The economic value of variable selection methods: Evidence from volatility forecasting," Economic Modelling, Elsevier, volume 163, issue C, DOI: 10.1016/j.econmod.2026.107739.
• Guerzoni, Marco & Riso, Luigi & Zoia, M. Grazia, 2026, "Extreme weather events as the main driver of electricity price volatility in Italy: A GARCH-MIDAS approach with machine learning-based variable selection," The North American Journal of Economics and Finance, Elsevier, volume 81, issue C, DOI: 10.1016/j.najef.2025.102512.
• Aslam, Adnan & Brahmana, Rayenda Khresna, 2026, "Systemic spillovers in high-growth private market sectors: determinants and portfolio implications," The North American Journal of Economics and Finance, Elsevier, volume 82, issue C, DOI: 10.1016/j.najef.2025.102579.
• Bonaccolto, Giovanni & Karmakar, Sayar & Bouri, Elie & Gupta, Rangan, 2026, "Spillover and predictability of volatility of 50 major cryptocurrencies: Evidence from a LASSO-regularized Quantile VAR," The North American Journal of Economics and Finance, Elsevier, volume 85, issue C, DOI: 10.1016/j.najef.2026.102668.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2026, "Predictive influence of Reddit sentiment on AI and tech moguls for digital financial assets: evidence from KAN and DES methodology," The North American Journal of Economics and Finance, Elsevier, volume 85, issue C, DOI: 10.1016/j.najef.2026.102672.
• Wang, Zhufeng & Wang, Lu, 2026, "Macroeconomic forecasting based on high-dimensional datasets: a smooth transition three pass regression filter model," The North American Journal of Economics and Finance, Elsevier, volume 86, issue C, DOI: 10.1016/j.najef.2026.102697.
• Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2026, "Extreme climate and natural disaster risk in financial markets: A CoES approach," The North American Journal of Economics and Finance, Elsevier, volume 86, issue C, DOI: 10.1016/j.najef.2026.102700.
• Caldeira, João F. & Cordeiro, Werley C., 2026, "Decomposing nominal and real yield curves and inflation forecasting: Evidence from Brazil," Economics Letters, Elsevier, volume 258, issue C, DOI: 10.1016/j.econlet.2025.112712.
• Goulet Coulombe, Philippe & Klieber, Karin, 2026, "An adaptive moving average for macroeconomic monitoring," Economics Letters, Elsevier, volume 259, issue C, DOI: 10.1016/j.econlet.2025.112773.
• Quinlan, Rory & Pinheiro, Roberto, 2026, "BLS payroll revisions: Forecasting recessions," Economics Letters, Elsevier, volume 261, issue C, DOI: 10.1016/j.econlet.2026.112859.
• Morita, Hiroshi, 2026, "Forecasting GDP growth with stock returns: Time-series or cross-sectional information?," Economics Letters, Elsevier, volume 263, issue C, DOI: 10.1016/j.econlet.2026.112946.
• Zevallos, Mauricio & Rubesam, Alexandre, 2026, "Finite-sample properties of the Campbell and Thompson out-of-sample R2," Economics Letters, Elsevier, volume 265, issue C, DOI: 10.1016/j.econlet.2026.113011.
• Guidolin, Massimo & Ionta, Serena, 2026, "Predicting commodity returns with climate variables: Statistical loss functions vs. economic value," Economics Letters, Elsevier, volume 265, issue C, DOI: 10.1016/j.econlet.2026.113028.
• Stosik, Jan & Zaremba, Adam, 2026, "Short-term reversal persists globally—If properly measured," Economics Letters, Elsevier, volume 267, issue C, DOI: 10.1016/j.econlet.2026.113113.
• Patton, Andrew J. & Zhang, Haozhe, 2026, "Bespoke realized volatility: Tailored measures of risk for volatility prediction," Journal of Econometrics, Elsevier, volume 254, issue PA, DOI: 10.1016/j.jeconom.2025.106122.
• Lunsford, Kurt G. & West, Kenneth D., 2026, "An empirical evaluation of some long-horizon macroeconomic forecasts," Journal of Econometrics, Elsevier, volume 255, issue C, DOI: 10.1016/j.jeconom.2026.106221.
• Gao, Zhan & Lee, Ji Hyung & Mei, Ziwei & Shi, Zhentao, 2026, "LASSO inference for high dimensional predictive regressions," Journal of Econometrics, Elsevier, volume 255, issue C, DOI: 10.1016/j.jeconom.2026.106240.
• Menzel, Konrad, 2026, "Transfer estimates for causal effects across heterogeneous sites," Journal of Econometrics, Elsevier, volume 255, issue C, DOI: 10.1016/j.jeconom.2026.106250.
• Kumbhakar, Subal C. & Mallick, Sushanta K., 2026, "Bayesian methods in economics and finance: A unified survey and taxonomy," Journal of Econometrics, Elsevier, volume 256, issue PB, DOI: 10.1016/j.jeconom.2026.106269.
• Sun, Yuying & Chen, Feng & Gao, Jiti, 2026, "Model averaging for time–varying vector autoregressions," Journal of Econometrics, Elsevier, volume 257, issue C, DOI: 10.1016/j.jeconom.2026.106308.
• Maddock, Luke & Nelson, Frank & Altringer, Levi & McKee, Sophie C., 2026, "Estimating the value of ecosystem services: A machine learning approach for Missouri wetlands," Ecosystem Services, Elsevier, volume 79, issue C, DOI: 10.1016/j.ecoser.2026.101849.
• Garcia, Pablo & Jacquinot, Pascal & Lenarčič, Črt & Mavromatis, Kostas & Papadopoulou, Niki & Silgado-Gómez, Edgar, 2026, "Green transition in the euro area: Domestic and global factors," European Economic Review, Elsevier, volume 182, issue C, DOI: 10.1016/j.euroecorev.2025.105206.
• Li, Mengheng & Mendieta-Muñoz, Ivan, 2026, "Unpacking trend inflation: Evidence from a factor correlated unobserved components model of sticky and flexible prices," European Economic Review, Elsevier, volume 187, issue C, DOI: 10.1016/j.euroecorev.2026.105377.
• Wang, Haibo, 2026, "Modeling bank systemic risk of emerging markets under geopolitical shocks: Empirical evidence from BRICS countries," Emerging Markets Review, Elsevier, volume 74, issue C, DOI: 10.1016/j.ememar.2026.101502.
• Bonato, Matteo & Gupta, Rangan & Pierdzioch, Christian, 2026, "Do shortages forecast aggregate and sectoral U.S. stock market realized variance? Evidence from a century of data," Journal of Empirical Finance, Elsevier, volume 86, issue C, DOI: 10.1016/j.jempfin.2026.101726.
• Babiak, Mykola & Baruník, Jozef, 2026, "Deep learning, predictability, and optimal portfolio returns," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101705.
• Cheng, Mingmian, 2026, "Sparse heterogeneous auto-regressive model for volatility forecasting," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101708.
• Li, Gang & Wang, Shuqi & Wei, K.C. John, 2026, "What drives retail investors’ overconfidence? The role of information acquisition costs," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101709.
• Jiao, Lei & Zhou, Qing (Clara), 2026, "Economic conditions and portfolio tail risk: A probability-weighted simulation approach," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101715.
• Rahimikia, Eghbal & Poon, Ser-Huang, 2026, "Machine learning for realised volatility forecasting," Journal of Empirical Finance, Elsevier, volume 88, issue C, DOI: 10.1016/j.jempfin.2026.101739.
• Nam, Kyungsik & Seo, Won-Ki, 2026, "Nonlinear temperature sensitivity of residential electricity demand: Evidence from a distributional regression approach," Energy Economics, Elsevier, volume 153, issue C, DOI: 10.1016/j.eneco.2025.109076.
• Ghelasi, Paul & Ziel, Florian, 2026, "A data-driven merit order: Learning a fundamental electricity price model," Energy Economics, Elsevier, volume 154, issue C, DOI: 10.1016/j.eneco.2025.109114.
• Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2026, "The predictive content of U.S. Energy Information Administration oil market forecasts," Energy Economics, Elsevier, volume 156, issue C, DOI: 10.1016/j.eneco.2026.109214.
• Das, Abhinav & Schlüter, Stephan & Schneider, Lorenz, 2026, "Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting," Energy Economics, Elsevier, volume 157, issue C, DOI: 10.1016/j.eneco.2026.109233.
• Theile, Philipp, 2026, "The shape of U — On the structure of utility from electric vehicle charging," Energy Economics, Elsevier, volume 161, issue C, DOI: 10.1016/j.eneco.2026.109519.
• Díaz-Díaz, Raimundo & Galiano, Aida & Martín-Álvarez, Juan Manuel & Barrientos-Marín, Jorge, 2026, "From policy to reality: Forecasting Spain's vehicle fleet trajectory toward 2030 climate targets," Energy Policy, Elsevier, volume 215, issue C, DOI: 10.1016/j.enpol.2026.115298.
• Yu, Deshui & Tang, Jiachen & Zhou, Mingtao, 2026, "Trade policy uncertainty and stock returns: A tale of two periods," International Review of Financial Analysis, Elsevier, volume 109, issue C, DOI: 10.1016/j.irfa.2025.104789.
• Klinkowska, Olga & Zadorozhna, Olha, 2026, "The yield curve strikes back: New evidence of its predictive power for economic activity and inflation," International Review of Financial Analysis, Elsevier, volume 113, issue C, DOI: 10.1016/j.irfa.2026.105128.
• Li, Jupeng & Hou, Weijie & Zhang, Zongxin, 2026, "A coupled autoregressive extreme-value model for dynamic tail risk with risk spirals," Finance Research Letters, Elsevier, volume 105, issue C, DOI: 10.1016/j.frl.2026.110187.
• Sheng, Xin & Cepni, Oguzhan & Gupta, Rangan & Markovski, Minko, 2026, "Mixed frequency machine learning forecasting of the growth of real gross fixed capital formation in the United States: the role of extreme weather conditions," Finance Research Letters, Elsevier, volume 106, issue C, DOI: 10.1016/j.frl.2026.110271.
• Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2026, "Credit standards: A new predictor of U.S. stock market realized volatility," Finance Research Letters, Elsevier, volume 106, issue C, DOI: 10.1016/j.frl.2026.110298.
• Lacombe, Donald J. & Yuan, Yuan & Qian, Pengyu, 2026, "A Bayesian Student-t specification for the MF2-GARCH model with applications in equity and crypto markets," Finance Research Letters, Elsevier, volume 107, issue C, DOI: 10.1016/j.frl.2026.110283.
• Wang, Chenguang & Yao, Kai & Liu, Jinpeng, 2026, "Salience, asymmetric effect and stock returns," Finance Research Letters, Elsevier, volume 107, issue C, DOI: 10.1016/j.frl.2026.110360.
• Huang, Junhui & Wu, Jianbin, 2026, "Do LLM-based overnight news indicators add value beyond A50 futures?," Finance Research Letters, Elsevier, volume 107, issue C, DOI: 10.1016/j.frl.2026.110367.
• Frydrych, Sylwia, 2026, "Transition risk and ESG materiality in credit rating downgrades: evidence from explainable machine learning," Finance Research Letters, Elsevier, volume 107, issue C, DOI: 10.1016/j.frl.2026.110408.
• Zhou, Fan & Guo, Wenjing, 2026, "Time-varying network structure and volatility prediction in the cryptocurrency market," Finance Research Letters, Elsevier, volume 87, issue C, DOI: 10.1016/j.frl.2025.109028.
• Papíková, Lenka & Papík, Mário, 2026, "Similarity failure proximity score: A network-based metric for bankruptcy prediction," Finance Research Letters, Elsevier, volume 94, issue C, DOI: 10.1016/j.frl.2026.109631.
• Chen, Qitong & Chen, Xingyi & Chen, Zhenrui, 2026, "Avoiding weak-factor selection in sPCA-based factor-augmented regression: An all subset-averaging perspective," Finance Research Letters, Elsevier, volume 98, issue C, DOI: 10.1016/j.frl.2026.109870.
• Polakow, Daniel Adam & Flint, Emlyn James & Turro, Isabella Cristina Josephine & van Rooyen, Joané, 2026, "Prediction reconditioned: Revisiting relevance," Finance Research Letters, Elsevier, volume 99, issue C, DOI: 10.1016/j.frl.2026.109854.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2026, "Are defense stocks sensitive to Reddit sentiments on Middle East conflicts? Insights from Google’s TabNet and Wavelet Quantile correlation," Finance Research Letters, Elsevier, volume 99, issue C, DOI: 10.1016/j.frl.2026.109887.
• Bie, Siyu & Feng, Guanhao & Guo, Naixin & He, Jingyu, 2026, "Can news predict firm bankruptcy?," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2025.101002.
• Zhang, Qunzi, 2026, "Commodity sentiment in predicting index futures returns," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2025.101025.
• Wang, Yicheng & Lera, Sandro Claudio, 2026, "Meta-learning for return prediction in shifting market regimes," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2025.101042.
• Ross, Landon J. & Horn, Jim & Pilanci, Mert & Luo, Kaihong & Zhou, Guofu, 2026, "Bottom up vs. top down: What does firm 10-K tell us?," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2026.101070.
• Hibbeln, Martin T. & Kopp, Raphael M. & Urban, Noah, 2026, "Predictive multiplicity, procedural multiplicity, and heterogeneous machine learning ensembles in recovery rate forecasting," Journal of Financial Stability, Elsevier, volume 83, issue C, DOI: 10.1016/j.jfs.2026.101510.
• Li, Yan & Qian, Zongxin, 2026, "Systemic risk measures and macroeconomic shocks: An update of empirical evidence," Journal of Financial Stability, Elsevier, volume 84, issue C, DOI: 10.1016/j.jfs.2026.101520.
• Crawley, Andrew & Daigneault, Adam & Gendron, Jonathan, 2026, "Where the trees fall: Macroeconomic forecasts for forest-reliant states," Forest Policy and Economics, Elsevier, volume 186, issue C, DOI: 10.1016/j.forpol.2026.103739.
• Mei, Ziwei & Sheng, Liugang & Shi, Zhentao, 2026, "Nickell bias in panel local projection: Financial crises are worse than you think," Journal of International Economics, Elsevier, volume 160, issue C, DOI: 10.1016/j.jinteco.2025.104210.
• Avanzi, Benjamin & Dong, Eric T. & Laub, Patrick J. & Wong, Bernard, 2026, "Distributional refinement network: Distributional forecasting via deep learning," Insurance: Mathematics and Economics, Elsevier, volume 128, issue C, DOI: 10.1016/j.insmatheco.2026.103246.
• Rusyda, Hasna Afifah & Shi, Yanlin & Shang, Han Lin, 2026, "Forecast mortality rates with copula-based approaches: Novel evidence from integrated reconciliation," Insurance: Mathematics and Economics, Elsevier, volume 129, issue C, DOI: 10.1016/j.insmatheco.2026.103263.
• Charles, Amélie & Darné, Olivier, 2026, "Forecasting volatility and risk management in natural gas markets," International Economics, Elsevier, volume 187, issue C, DOI: 10.1016/j.inteco.2026.100714.
• Chen, Kairan & Granville, Brigitte & Matousek, Roman, 2026, "Decoding central bank communications with large language models," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 109, issue C, DOI: 10.1016/j.intfin.2026.102325.
• Tong, Bin & Li, Rui & Xu, Yuanrong, 2026, "Asymptotically unbiased extreme Expected Shortfall and tail risk forecasting in international financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 111, issue C, DOI: 10.1016/j.intfin.2026.102352.
• Benmoussa, Amor Aniss & Ellwanger, Reinhard & Snudden, Stephen, 2026, "Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?," International Journal of Forecasting, Elsevier, volume 42, issue 1, pages 281-295, DOI: 10.1016/j.ijforecast.2025.02.009.
• Mitchell, James & Shiroff, Taylor & Braitsch, Hana, 2026, "Practice makes perfect: Learning effects with household point and density forecasts of inflation," International Journal of Forecasting, Elsevier, volume 42, issue 2, pages 315-329, DOI: 10.1016/j.ijforecast.2025.06.002.
• Bürgi, Constantin & Ortiz, Julio L., 2026, "Overreaction through anchoring," International Journal of Forecasting, Elsevier, volume 42, issue 2, pages 512-526, DOI: 10.1016/j.ijforecast.2025.08.002.
• Boug, Pål & Hungnes, Håvard & Kurita, Takamitsu, 2026, "Getting back on track: Forecasting after extreme observations," International Journal of Forecasting, Elsevier, volume 42, issue 2, pages 548-569, DOI: 10.1016/j.ijforecast.2025.08.005.
• Alsayed, Ahmed R.M. & Cameletti, Michela, 2026, "Air demand forecasting for passengers and freight in Italy: A comparison of two statistical models," Journal of Air Transport Management, Elsevier, volume 134, issue C, DOI: 10.1016/j.jairtraman.2026.102975.
• Xiao, Wei & Ji, Yangyang, 2026, "Heuristic macroeconomic expectations," Journal of Economic Behavior & Organization, Elsevier, volume 248, issue C, DOI: 10.1016/j.jebo.2026.107669.
• Lopez-Lira, Alejandro & Tang, Yuehua, 2026, "Can ChatGPT forecast stock price movements? Return predictability and large language models," Journal of Financial Economics, Elsevier, volume 184, issue C, DOI: 10.1016/j.jfineco.2026.104335.
• McWilliams, William & Stewart, Shamar L. & Massa, Olga Isengildina, 2026, "Food price inflation forecasting: Insights from a Macroeconomic Auto-Regressive Random Forest approach," Food Policy, Elsevier, volume 142, issue C, DOI: 10.1016/j.foodpol.2026.103115.
• Godinho, Frederico & Neugebauer, Katja, 2026, "House hunting high and low: Constructing a Housing Search Index for Portugal," Journal of Housing Economics, Elsevier, volume 73, issue C, DOI: 10.1016/j.jhe.2026.102163.
• d’Albis, Hippolyte & Salem, Mélika Ben & Boubtane, Ekrame, 2026, "Extending working lives in Japan: Evidence and lessons from an outlier," The Journal of the Economics of Ageing, Elsevier, volume 34, issue C, DOI: 10.1016/j.jeoa.2026.100643.
• Farag, Markos, 2026, "Threshold effects in oil–metal volatility spillovers: Evidence from industrial and precious metals," Resources Policy, Elsevier, volume 119, issue C, DOI: 10.1016/j.resourpol.2026.105983.
• Doojav, Gan-Ochir, 2026, "Fiscal-monetary interactions and commodity prices in a commodity-exporting economy," Resources Policy, Elsevier, volume 120, issue C, DOI: 10.1016/j.resourpol.2026.106008.
• Kwon, Alexander & Maliar, Lilia, 2026, "Predicting retirement and Social Security claiming decisions using machine learning," Labour Economics, Elsevier, volume 102, issue C, DOI: 10.1016/j.labeco.2026.102915.
• Fiaschi, Davide & Tealdi, Cristina, 2026, "Let’s roll back! The challenging task of regulating temporary contracts," Labour Economics, Elsevier, volume 102, issue C, DOI: 10.1016/j.labeco.2026.102937.
• Bolivar, Osmar, 2026, "High-frequency inflation forecasting: A two-step machine learning methodology," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 7, issue 1, DOI: 10.1016/j.latcb.2025.100172.
• Gemmi, Luca & Valchev, Rosen, 2026, "Biased surveys," Journal of Monetary Economics, Elsevier, volume 157, issue C, DOI: 10.1016/j.jmoneco.2025.103868.
• Hubrich, Kirstin & Schüler, Yves & Waggoner, Daniel, 2026, "Financial shocks and leverage of financial institutions: When do they matter?," Journal of Monetary Economics, Elsevier, volume 158, issue C, DOI: 10.1016/j.jmoneco.2026.103900.
• Yu, Bo & Peng, Weijia & Yao, Chun & Lan, Wei, 2026, "Forecasting realized volatility of Shanghai oil futures with mix-frequency uncertainty factors," Pacific-Basin Finance Journal, Elsevier, volume 98, issue C, DOI: 10.1016/j.pacfin.2026.103150.
• Choi, Insu & Lim, Soyeong & Kim, Seoyeon & Choi, Yeona & Han, Subin & Kim, Woo Chang, 2026, "Metric-based technical indicators for yield forecasting," Pacific-Basin Finance Journal, Elsevier, volume 98, issue C, DOI: 10.1016/j.pacfin.2026.103169.
• Bonato, Matteo & Demirer, Riza & Gupta, Rangan & Olaniran, Abeeb, 2026, "Does mining activity drive crash risks in bitcoin?," The Quarterly Review of Economics and Finance, Elsevier, volume 105, issue C, DOI: 10.1016/j.qref.2025.102082.
• Salisu, Afees A. & Gupta, Rangan & Cepni, Oguzhan, 2026, "Housing market variables and predictability of state-level stock market volatility of the United States: Fundamentals versus sentiments in a mixed-frequency framework," The Quarterly Review of Economics and Finance, Elsevier, volume 105, issue C, DOI: 10.1016/j.qref.2025.102087.
• Caporin, Massimiliano & Gupta, Rangan & Subramaniam, Sowmya & Torrent, Hudson S., 2026, "Supply Constraints and Conditional Distribution Predictability of Inflation and its Volatility: A Nonparametric Mixed-Frequency Causality-in-Quantiles Approach," Research in Economics, Elsevier, volume 80, issue 2, DOI: 10.1016/j.rie.2026.101128.
• Mati, Sagiru & Usman, Abdullahi G. & Ismael, Goran Yousif & Babuga, Umar Tijjani & Nadarajah, Saralees & Masoud, Serag & Uzun Ozsahin, Dilber & Abba, Sani I., 2026, "Explainable support vector regression coupled with quantum firefly optimisation algorithm for carbon emission prediction in West Africa: The role of socioeconomic, energy, and environmental factors," Renewable Energy, Elsevier, volume 256, issue PE, DOI: 10.1016/j.renene.2025.124298.
• Bargman, Daniil, 2026, "Latent variable modelling by supervised diffusion," International Review of Economics & Finance, Elsevier, volume 106, issue C, DOI: 10.1016/j.iref.2026.104972.
• Fasanya, Ismail O. & Oyewole, Oluwatomisin J. & Saleh Al-Faryan, Mamdouh Abdulaziz, 2026, "The inflation-energy nexus in OPEC: A nonlinear forecasting perspective," International Review of Economics & Finance, Elsevier, volume 107, issue C, DOI: 10.1016/j.iref.2026.105096.
• Khadivar, Hamed & Davis, Frederick & Khadivar, Ameneh & Stetsyuk, Ivan, 2026, "Predicting takeover rumor accuracy with machine learning," International Review of Economics & Finance, Elsevier, volume 108, issue C, DOI: 10.1016/j.iref.2026.105204.
• Barasal Morales, Adriano, 2026, "Climate calm? Long-run temperature signals and volatility in EU carbon futures," International Review of Economics & Finance, Elsevier, volume 108, issue C, DOI: 10.1016/j.iref.2026.105221.
• Chen, Ying & Peng, Liang & Sheng, Jiliang, 2026, "Predictability of climate policy uncertainty index," International Review of Economics & Finance, Elsevier, volume 108, issue C, DOI: 10.1016/j.iref.2026.105293.
• Huang, Zhuo & Tan, Ying & Yang, Zi & Zhang, Xun, 2026, "The impact of economic uncertainty on household portfolio choice: Evidence from China," International Review of Economics & Finance, Elsevier, volume 109, issue C, DOI: 10.1016/j.iref.2026.105418.
• Jahangiri, Eshagh & Corazza, Marco, 2026, "Sentiment-based stock price prediction in developing countries: Evidence from Iran," International Review of Economics & Finance, Elsevier, volume 109, issue C, DOI: 10.1016/j.iref.2026.105423.
• Luo, Jiawen & Fu, Shengjie & Cepni, Oguzhan & Gupta, Rangan, 2026, "Climate risks and forecastability of US inflation: Evidence from dynamic quantile model averaging," International Review of Economics & Finance, Elsevier, volume 110, issue C, DOI: 10.1016/j.iref.2026.105576.
• Goel, Aditya, 2026, "Measuring macroeconomic surprise magnitude with prediction markets: Market-implied dispersion from Kalshi," International Review of Economics & Finance, Elsevier, volume 110, issue C, DOI: 10.1016/j.iref.2026.105577.
• Pinitjitsamut, Montchai, 2026, "Timing commodity sales under tail-risk control: State-dependent rules and global price signals in rubber markets," International Review of Economics & Finance, Elsevier, volume 111, issue C, DOI: 10.1016/j.iref.2026.105622.
• Venkatesh, Hari, 2026, "Do balance sheet vulnerabilities predict financial crises? Evidence from machine learning and Shapley decomposition," International Review of Economics & Finance, Elsevier, volume 111, issue C, DOI: 10.1016/j.iref.2026.105731.
• Migliavacca, Milena & Anwer, Zaheer & Fandella, Paola, 2026, "Geopolitical risk and stock market volatility: The case of US weapon and non-weapon firms," Research in International Business and Finance, Elsevier, volume 81, issue C, DOI: 10.1016/j.ribaf.2025.103195.
• Mei, Dexiang & Li, Xiaotao, 2026, "Forecasting of Chinese stock price using a hybrid neural network model," Research in International Business and Finance, Elsevier, volume 82, issue C, DOI: 10.1016/j.ribaf.2025.103232.
• Hamida, Amal Ben & de Peretti, Christian & Belkacem, Lotfi, 2026, "Benford’s law and intraday microstructure anomalies: Forecasting market movements with high-frequency data," Research in International Business and Finance, Elsevier, volume 84, issue C, DOI: 10.1016/j.ribaf.2026.103302.
• Sun, Hao & Zhu, Xiaoqian & Li, Jianping, 2026, "Revealing corporate accounting fraud: From the perspective of individual investors," Research in International Business and Finance, Elsevier, volume 86, issue C, DOI: 10.1016/j.ribaf.2026.103385.
• Bao, Guanhao & Cui, Baisheng, 2026, "Research on the impact of U.S. monetary policy uncertainty on international oil price forecasting: A deep learning approach based on frequency domain decomposition," Research in International Business and Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.ribaf.2026.103391.
• Aslam, Adnan & Brahmana, Rayenda Khresna, 2026, "The dynamic relationship among private and public markets and its most important features," Research in International Business and Finance, Elsevier, volume 89, issue C, DOI: 10.1016/j.ribaf.2026.103490.
• Liu, Junjie & Song, Shijie, 2026, "Real-time dynamic higher-order moments of cryptocurrencies for volatility forecasting and risk measurement: New evidence from the SHARV–SK model," Research in International Business and Finance, Elsevier, volume 90, issue C, DOI: 10.1016/j.ribaf.2026.103521.
• Zhang, Ruohan, 2026, "Impact of category-specific equity market volatility on green and brown energy stocks," Research in International Business and Finance, Elsevier, volume 90, issue C, DOI: 10.1016/j.ribaf.2026.103526.
• Kim, Hyun Hak, 2026, "Measuring concentration risk with a herd sentiment index: Evidence from Korean financial markets," Research in International Business and Finance, Elsevier, volume 90, issue C, DOI: 10.1016/j.ribaf.2026.103543.
• Fontana, Stefania & Guccio, Calogero & Pignataro, Giacomo & Vidoli, Francesco, 2026, "Better politicians, fewer deaths? Local resilience in overcoming the pandemic crisis in Italy," Social Science & Medicine, Elsevier, volume 398, issue C, DOI: 10.1016/j.socscimed.2026.119198.
• Du, Juan & Wu, Desheng, 2026, "Enhancing exchange rates forecasting: Leveraging long short-term memory with technical indicators," Structural Change and Economic Dynamics, Elsevier, volume 80, issue C, pages 136-148, DOI: 10.1016/j.strueco.2026.07.006.
• Yilin Xiao & Jamie L. Cross, 2026, "Regularized Random Subspace Regressions," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-13, Feb.
• Hilde C. Bjornland & Nicolas Hardy & Dimitris Korobilis, 2026, "Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-39, May.
• Luke Hartigan, 2026, "Estimating the Common Output Cycle in Australia," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-78, Sep.
• Rishabh Choudhary & Chetan Dave & Chetan Ghate, 2026, "Forecasting Indian Core Inflation: Simple Made Simpler," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-83, Sep.
• Gaddy, Hampton, 2026, "We are our memory: a flexible framework for quantifying the demographic imprints of the past," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 138974, Jun.
• Koop, Gary & McIntyre, Stuart & Mitchell, James & Wu, Ping, 2026, "Incorporating Micro Data into Macro Models using Pseudo VARs," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2026-04, Feb.
• Himaz, Rozana & Salmanidou, Dimitra & Ghaffarian, Saman, 2026, "Machine Learning for Estimating Catastrophic Health Spending in Disaster-Affected, Data-Scarce Settings," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2026-05, Mar.
• Slawomir Bukowski & Joanna Bukowska & Jacek Woloszyn & Agnieszka Molga, 2026, "Forecasting the EUR/PLN Exchange RateUsing Classical and Artificial Intelligence Methods:An Empirical Comparison of ARIMA, XGBoost, LSTMand Hybrid Models on NBP Data 2015-2026," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2, pages 295-317.
• Anna Gembalska-Kwiecien, 2026, "Attempted Development of a Methodology to Support Project Implementation Risk Management in a Manufacturing Enterprise," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2, pages 57-66.
• Eduard Gracia, 2026, "Follow the median: revisiting bubbles and cycles," UB School of Economics Working Papers, University of Barcelona School of Economics, number 2026/497.
• Chiara Casoli & Riccardo Lucchetti, 2026, "A rotated Dynamic Factor Model for the yield curve: squeezing out information when it matters," Working Papers, Fondazione Eni Enrico Mattei, number 2026.03, Jan.
• Gary Koop & Stuart McIntyre & James Mitchell & Ping Wu, 2026, "Incorporating Micro Data into Macro Models Using Pseudo VARs," Working Papers, Federal Reserve Bank of Cleveland, number 26-04, Feb, DOI: 10.26509/frbc-wp-202604.
• Todd E. Clark & Florian Huber & Gary Koop, 2026, "A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors," Working Papers, Federal Reserve Bank of Cleveland, number 26-14, Jun, DOI: 10.26509/frbc-wp-202614.
• Nicholas Fritsch & Edward Simpson Prescott, 2026, "Macroeconomic Parameter Instability in Auto Loan Loss Models," Working Papers, Federal Reserve Bank of Cleveland, number 26-18, Jul, DOI: 10.26509/frbc-wp-202618.
• Kevin J. Lansing & Adam Hale Shapiro, 2026, "Measuring Inflation Shock Momentum," Working Paper Series, Federal Reserve Bank of San Francisco, number 2026-10, Apr, DOI: 10.24148/wp2026-10.