Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2023

• Zhu, Qinwen & Diao, Xundi & Wu, Chongfeng, 2023, "Volatility forecast with the regularity modifications," Finance Research Letters, Elsevier, volume 58, issue PA, DOI: 10.1016/j.frl.2023.104008.
• Feng, Yun & Hou, Weijie & Song, Yuping, 2023, "Tail risk in the Chinese stock market: An AEV model on the maximal drawdowns," Finance Research Letters, Elsevier, volume 58, issue PA, DOI: 10.1016/j.frl.2023.104294.
• Zhang, Zhihao, 2023, "Are climate risks helpful for understanding inflation in BRICS countries?," Finance Research Letters, Elsevier, volume 58, issue PB, DOI: 10.1016/j.frl.2023.104441.
• Luo, Tao & Zhang, Lixia & Sun, Huaping & Bai, Jiancheng, 2023, "Enhancing exchange rate volatility prediction accuracy: Assessing the influence of different indices on the USD/CNY exchange rate," Finance Research Letters, Elsevier, volume 58, issue PB, DOI: 10.1016/j.frl.2023.104483.
• Shu, Qi & Xiong, Heng & Jiang, Wenjun & Mamon, Rogemar, 2023, "A novel perspective on forecasting non-ferrous metals’ volatility: Integrating deep learning techniques with econometric models," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104482.
• Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023, "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104501.
• Barua, Ronil & Sharma, Anil K., 2023, "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104515.
• Jin, Daxiang & Yu, Jize, 2023, "Predicting cryptocurrency market volatility: Novel evidence from climate policy uncertainty," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104520.
• Zhu, Jiaji & Han, Wei & Zhang, Junchao, 2023, "Does climate risk matter for gold price volatility?," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104544.
• Coita, Ioana-Florina & Belbe, Stefana (Ștefana) & Mare, Codruta (Codruța) & Osterrieder, Joerg & Hopp, Christian, 2023, "Modelling taxpayers’ behaviour based on prediction of trust using sentiment analysis," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104549.
• Galil, Koresh & Hauptman, Ami & Rosenboim, Rosit Levy, 2023, "Prediction of corporate credit ratings with machine learning: Simple interpretative models," Finance Research Letters, Elsevier, volume 58, issue PD, DOI: 10.1016/j.frl.2023.104648.
• Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023, "Climate risks and realized volatility of major commodity currency exchange rates," Journal of Financial Markets, Elsevier, volume 62, issue C, DOI: 10.1016/j.finmar.2022.100760.
• Alexandridis, Antonios K. & Apergis, Iraklis & Panopoulou, Ekaterini & Voukelatos, Nikolaos, 2023, "Equity premium prediction: The role of information from the options market," Journal of Financial Markets, Elsevier, volume 64, issue C, DOI: 10.1016/j.finmar.2022.100801.
• Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023, "Climate risks and state-level stock market realized volatility," Journal of Financial Markets, Elsevier, volume 66, issue C, DOI: 10.1016/j.finmar.2023.100854.
• Engel, Charles & Wu, Steve Pak Yeung, 2023, "Forecasting the U.S. Dollar in the 21st Century," Journal of International Economics, Elsevier, volume 141, issue C, DOI: 10.1016/j.jinteco.2023.103715.
• Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023, "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, volume 145, issue C, DOI: 10.1016/j.jinteco.2023.103773.
• Dang, Ou & Feng, Mingbin & Hardy, Mary R., 2023, "Two-stage nested simulation of tail risk measurement: A likelihood ratio approach," Insurance: Mathematics and Economics, Elsevier, volume 108, issue C, pages 1-24, DOI: 10.1016/j.insmatheco.2022.10.002.
• Ugarte Montero, Andrey & Wagner, Joël, 2023, "On potential information asymmetries in long-term care insurance: A simulation study using data from Switzerland," Insurance: Mathematics and Economics, Elsevier, volume 111, issue C, pages 230-241, DOI: 10.1016/j.insmatheco.2023.04.003.
• Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2023, "Intergenerational actuarial fairness when longevity increases: Amending the retirement age," Insurance: Mathematics and Economics, Elsevier, volume 113, issue C, pages 161-184, DOI: 10.1016/j.insmatheco.2023.08.007.
• Li, Johnny Siu-Hang & Liu, Yanxin & Chan, Wai-Sum, 2023, "Hedging longevity risk under non-Gaussian state-space stochastic mortality models: A mean-variance-skewness-kurtosis approach," Insurance: Mathematics and Economics, Elsevier, volume 113, issue C, pages 96-121, DOI: 10.1016/j.insmatheco.2023.08.001.
• Naimoli, Antonio, 2023, "The information content of sentiment indices in forecasting Value at Risk and Expected Shortfall: a Complete Realized Exponential GARCH-X approach," International Economics, Elsevier, volume 176, issue C, DOI: 10.1016/j.inteco.2023.100459.
• Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023, "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 82, issue C, DOI: 10.1016/j.intfin.2022.101684.
• Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023, "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 88, issue C, DOI: 10.1016/j.intfin.2023.101825.
• Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023, "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 88, issue C, DOI: 10.1016/j.intfin.2023.101856.
• Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht, 2023, "Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence," International Journal of Forecasting, Elsevier, volume 39, issue 1, pages 266-278, DOI: 10.1016/j.ijforecast.2021.11.005.
• Fortin, Alain-Philippe & Simonato, Jean-Guy & Dionne, Georges, 2023, "Forecasting expected shortfall: Should we use a multivariate model for stock market factors?," International Journal of Forecasting, Elsevier, volume 39, issue 1, pages 314-331, DOI: 10.1016/j.ijforecast.2021.11.010.
• Bańbura, Marta & Bobeica, Elena, 2023, "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, volume 39, issue 1, pages 364-390, DOI: 10.1016/j.ijforecast.2021.12.001.
• Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023, "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 570-586, DOI: 10.1016/j.ijforecast.2022.01.003.
• Haase, Felix & Neuenkirch, Matthias, 2023, "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 587-605, DOI: 10.1016/j.ijforecast.2022.01.004.
• Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023, "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 606-622, DOI: 10.1016/j.ijforecast.2022.01.005.
• Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023, "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 841-868, DOI: 10.1016/j.ijforecast.2022.02.010.
• Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023, "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 884-900, DOI: 10.1016/j.ijforecast.2022.03.001.
• Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, volume 39, issue 3, pages 1145-1162, DOI: 10.1016/j.ijforecast.2022.04.009.
• Knotek, Edward S. & Zaman, Saeed, 2023, "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, volume 39, issue 4, pages 1736-1760, DOI: 10.1016/j.ijforecast.2022.04.007.
• Čapek, Jan & Crespo Cuaresma, Jesús & Hauzenberger, Niko & Reichel, Vlastimil, 2023, "Macroeconomic forecasting in the euro area using predictive combinations of DSGE models," International Journal of Forecasting, Elsevier, volume 39, issue 4, pages 1820-1838, DOI: 10.1016/j.ijforecast.2022.09.002.
• Magnus, Jan R. & Vasnev, Andrey L., 2023, "On the uncertainty of a combined forecast: The critical role of correlation," International Journal of Forecasting, Elsevier, volume 39, issue 4, pages 1895-1908, DOI: 10.1016/j.ijforecast.2022.10.002.
• Matsumoto, Akito & Pescatori, Andrea & Wang, Xueliang, 2023, "Commodity prices and global economic activity," Japan and the World Economy, Elsevier, volume 66, issue C, DOI: 10.1016/j.japwor.2023.101177.
• komaki, Yasuyuki, 2023, "Why is the forecast error of quarterly GDP in Japan so large? – From an international comparison of quarterly GDP forecast situation," Japan and the World Economy, Elsevier, volume 66, issue C, DOI: 10.1016/j.japwor.2023.101192.
• Oh, Hyungna & Lee, Jae Yoon & Jeong, Eunmi & Kim, Jee Young, 2023, "Simulated effects of carbon pricing on industrial sector energy use," Japan and the World Economy, Elsevier, volume 68, issue C, DOI: 10.1016/j.japwor.2023.101222.
• Krivorotov, George, 2023, "Machine learning-based profit modeling for credit card underwriting - implications for credit risk," Journal of Banking & Finance, Elsevier, volume 149, issue C, DOI: 10.1016/j.jbankfin.2023.106785.
• Arai, Natsuki, 2023, "The FOMC’s new individual economic projections and macroeconomic theories," Journal of Banking & Finance, Elsevier, volume 151, issue C, DOI: 10.1016/j.jbankfin.2023.106845.
• Lohmann, Christian & Möllenhoff, Steffen, 2023, "Dark premonitions: Pre-bankruptcy investor attention and behavior," Journal of Banking & Finance, Elsevier, volume 151, issue C, DOI: 10.1016/j.jbankfin.2023.106853.
• Ellwanger, Reinhard & Snudden, Stephen, 2023, "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, volume 154, issue C, DOI: 10.1016/j.jbankfin.2023.106962.
• Du, Zaichao & Escanciano, Juan Carlos & Zhu, Guangwei, 2023, "The case for CASE: Estimating heterogeneous systemic effects," Journal of Banking & Finance, Elsevier, volume 157, issue C, DOI: 10.1016/j.jbankfin.2023.107022.
• Ferri, Piero & Cristini, Annalisa & Tramontana, Fabio, 2023, "Meta-models of the Phillips curve and income distribution," Journal of Economic Behavior & Organization, Elsevier, volume 213, issue C, pages 215-232, DOI: 10.1016/j.jebo.2023.07.020.
• Hall, Stephen G. & Tavlas, George S. & Wang, Yongli, 2023, "Drivers and spillover effects of inflation: The United States, the euro area, and the United Kingdom☆," Journal of International Money and Finance, Elsevier, volume 131, issue C, DOI: 10.1016/j.jimonfin.2022.102776.
• Boucher, C. & Jasinski, A. & Tokpavi, S., 2023, "Conditional mean reversion of financial ratios and the predictability of returns," Journal of International Money and Finance, Elsevier, volume 137, issue C, DOI: 10.1016/j.jimonfin.2023.102907.
• Glas, Alexander & Heinisch, Katja, 2023, "Conditional macroeconomic survey forecasts: Revisions and errors," Journal of International Money and Finance, Elsevier, volume 138, issue C, DOI: 10.1016/j.jimonfin.2023.102927.
• Tafuro, Andrea, 2023, "Labour market rigidity and expansionary austerity," Journal of Macroeconomics, Elsevier, volume 75, issue C, DOI: 10.1016/j.jmacro.2022.103495.
• Demirel, Ufuk Devrim & Otterson, James, 2023, "Quantifying the uncertainty of long-term macroeconomic projections," Journal of Macroeconomics, Elsevier, volume 75, issue C, DOI: 10.1016/j.jmacro.2023.103501.
• Arin, K. Peren & Devereux, Kevin & Mazur, Mieszko, 2023, "Taxes and firm investment," Journal of Macroeconomics, Elsevier, volume 76, issue C, DOI: 10.1016/j.jmacro.2023.103517.
• Garcia, Pablo & Jacquinot, Pascal & Lenarčič, Črt & Lozej, Matija & Mavromatis, Kostas, 2023, "Global models for a global pandemic: The impact of COVID-19 on small euro area economies," Journal of Macroeconomics, Elsevier, volume 77, issue C, DOI: 10.1016/j.jmacro.2023.103551.
• Schade, Philipp & Schuhmacher, Monika C., 2023, "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, volume 19, issue C, DOI: 10.1016/j.jbvi.2022.e00357.
• Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023, "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, volume 30, issue C, DOI: 10.1016/j.jcomm.2022.100285.
• Salisu, Afees A. & Adediran, Idris & Omoke, Philip C. & Tchankam, Jean Paul, 2023, "Gold and tail risks," Resources Policy, Elsevier, volume 80, issue C, DOI: 10.1016/j.resourpol.2022.103154.
• Swamy, Vighneswara & Lagesh, M.A., 2023, "Does happy Twitter forecast gold price?," Resources Policy, Elsevier, volume 81, issue C, DOI: 10.1016/j.resourpol.2023.103299.
• Fasanya, Ismail O. & Oyewole, Oluwatomisin J., 2023, "On the connection between international REITs and oil markets: The role of economic policy uncertainty," Resources Policy, Elsevier, volume 81, issue C, DOI: 10.1016/j.resourpol.2023.103335.
• Salisu, Afees A. & Ndako, Umar B. & Vo, Xuan Vinh, 2023, "Transition risk, physical risk, and the realized volatility of oil and natural gas prices," Resources Policy, Elsevier, volume 81, issue C, DOI: 10.1016/j.resourpol.2023.103383.
• Salisu, Afees A. & Ndako, Umar B. & Vo, Xuan Vinh, 2023, "Oil price and the Bitcoin market," Resources Policy, Elsevier, volume 82, issue C, DOI: 10.1016/j.resourpol.2023.103437.
• Karmakar, Sayar & Gupta, Rangan & Cepni, Oguzhan & Rognone, Lavinia, 2023, "Climate risks and predictability of the trading volume of gold: Evidence from an INGARCH model," Resources Policy, Elsevier, volume 82, issue C, DOI: 10.1016/j.resourpol.2023.103438.
• Salisu, Afees A. & Ogbonna, Ahamuefula E. & Vo, Xuan Vinh, 2023, "Oil tail risks and the realized variance of consumer prices in advanced economies," Resources Policy, Elsevier, volume 83, issue C, DOI: 10.1016/j.resourpol.2023.103755.
• Ayinde, Taofeek O. & Olaniran, Abeeb O. & Abolade, Onomeabure C. & Ogbonna, Ahamuefula Ephraim, 2023, "Technology shocks - Gold market connection: Is the effect episodic to business cycle behaviour?," Resources Policy, Elsevier, volume 84, issue C, DOI: 10.1016/j.resourpol.2023.103771.
• Peng, Lijuan & Liang, Chao, 2023, "Sustainable development during the post-COVID-19 period: Role of crude oil," Resources Policy, Elsevier, volume 85, issue PA, DOI: 10.1016/j.resourpol.2023.103843.
• Liu, Zhenya & Teka, Hanen & You, Rongyu, 2023, "Conditional autoencoder pricing model for energy commodities," Resources Policy, Elsevier, volume 86, issue PA, DOI: 10.1016/j.resourpol.2023.104060.
• Hardy, Nicolás & Ferreira, Tiago & Quinteros, Maria J. & Magner, Nicolás S., 2023, "“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone," Resources Policy, Elsevier, volume 86, issue PA, DOI: 10.1016/j.resourpol.2023.104251.
• Andres–Escayola, Erik & Berganza, Juan Carlos & Campos, Rodolfo G. & Molina, Luis, 2023, "A BVAR toolkit to assess macrofinancial risks in Brazil and Mexico," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 4, issue 1, DOI: 10.1016/j.latcb.2022.100079.
• Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023, "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 4, issue 2, DOI: 10.1016/j.latcb.2023.100087.
• Gómez-Puig, Marta & Pieterse-Bloem, Mary & Sosvilla-Rivero, Simón, 2023, "Dynamic connectedness between credit and liquidity risks in euro area sovereign debt markets," Journal of Multinational Financial Management, Elsevier, volume 68, issue C, DOI: 10.1016/j.mulfin.2023.100800.
• Lu, Yueliang (Jacques) & Tian, Weidong, 2023, "An on-line machine learning return prediction," Pacific-Basin Finance Journal, Elsevier, volume 79, issue C, DOI: 10.1016/j.pacfin.2023.102049.
• Umar, Zaghum & Riaz, Yasir & Shahab, Yasir & Teplova, Tamara, 2023, "Network connectedness of the term structure of yield curve and global Sukuks," Pacific-Basin Finance Journal, Elsevier, volume 80, issue C, DOI: 10.1016/j.pacfin.2023.102056.
• Narayan, Shivani & Kumar, Dilip & Bouri, Elie, 2023, "Systemically important financial institutions and drivers of systemic risk: Evidence from India," Pacific-Basin Finance Journal, Elsevier, volume 82, issue C, DOI: 10.1016/j.pacfin.2023.102155.
• Arancibia, Rodrigo García & Llop, Pamela & Lovatto, Mariel, 2023, "Nonparametric prediction for univariate spatial data: Methods and applications," Papers in Regional Science, Elsevier, volume 102, issue 3, pages 635-673, DOI: 10.1111/pirs.12735.
• Belhadj, Besma, 2023, "New fuzzy multiple regressions for the instantaneous and panel data “The determinants of Poverty in the Countries MENA”," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 615, issue C, DOI: 10.1016/j.physa.2023.128565.
• Cronin, David & McInerney, Niall, 2023, "Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment," European Journal of Political Economy, Elsevier, volume 76, issue C, DOI: 10.1016/j.ejpoleco.2022.102227.
• Demirer, Riza & Gupta, Rangan & Salisu, Afees A. & van Eyden, Reneé, 2023, "Firm-level business uncertainty and the predictability of the aggregate U.S. stock market volatility during the COVID-19 pandemic," The Quarterly Review of Economics and Finance, Elsevier, volume 88, issue C, pages 295-302, DOI: 10.1016/j.qref.2023.02.002.
• Salisu, Afees A. & Gupta, Rangan & Bouri, Elie, 2023, "Testing the forecasting power of global economic conditions for the volatility of international REITs using a GARCH-MIDAS approach," The Quarterly Review of Economics and Finance, Elsevier, volume 88, issue C, pages 303-314, DOI: 10.1016/j.qref.2023.02.004.
• Zhang, Li & Wang, Lu & Peng, Lijuan & Luo, Keyu, 2023, "Measuring the response of clean energy stock price volatility to extreme shocks," Renewable Energy, Elsevier, volume 206, issue C, pages 1289-1300, DOI: 10.1016/j.renene.2023.02.066.
• Yao, Youfu & Hong, Yun, 2023, "Can comment letters impact excess cash holdings? Evidence from China," International Review of Economics & Finance, Elsevier, volume 83, issue C, pages 900-922, DOI: 10.1016/j.iref.2022.11.003.
• Hong, Yanran & Yu, Jize & Su, Yuquan & Wang, Lu, 2023, "Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility," International Review of Economics & Finance, Elsevier, volume 84, issue C, pages 358-368, DOI: 10.1016/j.iref.2022.11.023.
• Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023, "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, volume 84, issue C, pages 369-381, DOI: 10.1016/j.iref.2022.11.030.
• Liu, Xiaoqun & Zhang, Yuchen & Tian, Mengqiao & Chao, Youcong, 2023, "Financial distress and jump tail risk: Evidence from China's listed companies," International Review of Economics & Finance, Elsevier, volume 85, issue C, pages 316-336, DOI: 10.1016/j.iref.2023.01.007.
• Li, Zepei & Huang, Haizhen, 2023, "Challenges for volatility forecasts of US fossil energy spot markets during the COVID-19 crisis," International Review of Economics & Finance, Elsevier, volume 86, issue C, pages 31-45, DOI: 10.1016/j.iref.2023.02.004.
• Wu, Xinyu & He, Qizhi & Xie, Haibin, 2023, "Forecasting VIX with time-varying risk aversion," International Review of Economics & Finance, Elsevier, volume 88, issue C, pages 458-475, DOI: 10.1016/j.iref.2023.06.034.
• Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2023, "Forecasting crude oil prices: A reduced-rank approach," International Review of Economics & Finance, Elsevier, volume 88, issue C, pages 698-711, DOI: 10.1016/j.iref.2023.07.001.
• Pirayesh Neghab, Davood & Bradrania, Reza & Elliott, Robert, 2023, "Deliberate premarket underpricing: New evidence on IPO pricing using machine learning," International Review of Economics & Finance, Elsevier, volume 88, issue C, pages 902-927, DOI: 10.1016/j.iref.2023.07.008.
• Li, Xiafei & Guo, Qiang & Liang, Chao & Umar, Muhammad, 2023, "Forecasting gold volatility with geopolitical risk indices," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2022.101857.
• Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023, "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2023.101905.
• González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023, "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2023.101907.
• Ghosh, Bikramaditya & Bouri, Elie & Wee, Jung Bum & Zulfiqar, Noshaba, 2023, "Return and volatility properties: Stylized facts from the universe of cryptocurrencies and NFTs," Research in International Business and Finance, Elsevier, volume 65, issue C, DOI: 10.1016/j.ribaf.2023.101945.
• Bouras, Christos & Christou, Christina & Gupta, Rangan & Lesame, Keagile, 2023, "Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks," Research in International Business and Finance, Elsevier, volume 65, issue C, DOI: 10.1016/j.ribaf.2023.101952.
• He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023, "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, volume 65, issue C, DOI: 10.1016/j.ribaf.2023.101983.
• Duan, Kun & Wang, Rui & Chen, Shun & Ge, Lei, 2023, "Exploring the predictability of attention mechanism with LSTM: Evidence from EU carbon futures prices," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102020.
• Grudniewicz, Jan & Ślepaczuk, Robert, 2023, "Application of machine learning in algorithmic investment strategies on global stock markets," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102052.
• Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Long, Jian-You & Zhou, Yang, 2023, "Forecasting stock market volatility under parameter and model uncertainty," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102084.
• Bitetto, Alessandro & Cerchiello, Paola & Filomeni, Stefano & Tanda, Alessandra & Tarantino, Barbara, 2023, "Machine learning and credit risk: Empirical evidence from small- and mid-sized businesses," Socio-Economic Planning Sciences, Elsevier, volume 90, issue C, DOI: 10.1016/j.seps.2023.101746.
• Mitze, Timo & Makkonen, Teemu, 2023, "Can large-scale RDI funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19," Technological Forecasting and Social Change, Elsevier, volume 186, issue PB, DOI: 10.1016/j.techfore.2022.122073.
• Miguel Herculano & Punnoose Jacob, 2023, "Financial Condition Indices in an Incomplete Data Environment," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-42, Aug.
• Leo Krippner, 2023, "Estimating and Applying Autoregression Models via Their Eigensystem Representation," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-47, Oct.
• Roshen Fernando & Caterina Lepore, 2023, "Global Economic Impacts of Physical Climate Risks," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-50, Oct.
• Felix Chan & Laurent Pauwels, 2023, "Optimal Forecast Combination with Mean Absolute Error Loss," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-59, Nov.
• Roshen Fernando, 2023, "Impact of Demographic Trends on Antimicrobial Resistance," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-60, Nov.
• Roshen Fernando, 2023, "Impact of Physical Climate Risks on Antimicrobial Resistance," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2023-61, Nov.
• Osmar Bolivar & Christian Huanto, 2023, "Geopolitical Risk Shocks: Macroeconomic Effects in Bolivia, Chile, and Peru," Cuadernos de Investigación Económica Boliviana, Ministerio de Economía y Finanzas Públicas de Bolivia, volume 6, issue 2, pages 62-83, December.
• Zhang, Ning & Gong, Yujing & Xue, Xiaohan, 2023, "Less disagreement, better forecasts: adjusted risk measures in the energy futures market," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 118451, Oct.
• Ibarra, Raul, 2023, "The yield spread as a predictor of economic activity in Mexico: the role of the term premium," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 120717, Oct.
• Julia del Amo Valor & Marcos Martín Mateos & Javier J. Pérez, 2023, "La evaluación de las políticas públicas desde una perspectiva macroeconómica en el contexto europeo," EKONOMIAZ. Revista vasca de Economía, Gobierno Vasco / Eusko Jaurlaritza / Basque Government, volume 103, issue 01, pages 285-299.
• Alain Hecq & Elisa Voisin, 2023, "Predicting Crashes in Oil Prices During The Covid-19 Pandemic with Mixed Causal-Noncausal Models," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications", DOI: 10.1108/S0731-90532023000045B010.
• Yoonseok Lee & Donggyu Sul, 2023, "Depth-weighted Forecast Combination: Application to COVID-19 Cases," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications", DOI: 10.1108/S0731-90532023000045B011.
• Valeriia Baklanova & Aleksei Kurkin & Tamara Teplova, 2023, "Investor sentiment and the NFT hype index: to buy or not to buy?," China Finance Review International, Emerald Group Publishing Limited, volume 14, issue 3, pages 522-548, December, DOI: 10.1108/CFRI-06-2023-0175.
• Aivars Spilbergs & Diego Norena-Chavez & Eleftherios Thalassinos & Graţiela Georgiana Noja & Mirela Cristea, 2023, "Challenges to Credit Risk Management in the Context of Growing Macroeconomic Instability in the Baltic States Caused by COVID-19," Contemporary Studies in Economic and Financial Analysis, Emerald Group Publishing Limited, "Digital Transformation, Strategic Resilience, Cyber Security and Risk Management", DOI: 10.1108/S1569-37592023000111A006.
• Soumya Bhadury & Satadru Das & Saurabh Ghosh & Pawan Gopalakrishnan, 2023, "Impact of crude prices shock on GDP growth: using a linear, nonlinear and extreme value framework," Indian Growth and Development Review, Emerald Group Publishing Limited, volume 16, issue 1, pages 91-103, March, DOI: 10.1108/IGDR-05-2022-0065.
• Özgür İcan & Taha Buğra Çelik, 2023, "Weak-form market efficiency and corruption: a cross-country comparative analysis," Journal of Capital Markets Studies, Emerald Group Publishing Limited, volume 7, issue 1, pages 72-90, April, DOI: 10.1108/JCMS-12-2022-0046.
• Elias Shohei Kamimura & Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2023, "A recent review on optimisation methods applied to credit scoring models," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, volume 28, issue 56, pages 352-371, June, DOI: 10.1108/JEFAS-09-2021-0193.
• Afees Salisu & Douglason Godwin Omotor, 2023, "Forecasting expenditure components in Nigeria," Journal of Economic Studies, Emerald Group Publishing Limited, volume 51, issue 4, pages 783-807, September, DOI: 10.1108/JES-02-2023-0087.
• Hardik Marfatia, 2023, "The financial market's ability to forecast economic growth: information from sectoral movements," Journal of Economic Studies, Emerald Group Publishing Limited, volume 50, issue 7, pages 1467-1484, January, DOI: 10.1108/JES-08-2022-0466.
• Mehdi Mili & Ahmed Bouteska, 2023, "Forecasting nonlinear dependency between cryptocurrencies and foreign exchange markets using dynamic copula: evidence from GAS models," Journal of Risk Finance, Emerald Group Publishing Limited, volume 24, issue 4, pages 464-482, May, DOI: 10.1108/JRF-04-2022-0074.
• McIntyre, Stuart & Koop, Gary & Mitchell, James & Poon, Aubrey & Wu, Ping, 2023, "Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2023-07, May.
• Galvão, Ana Beatriz & Macqueen, Rory, 2023, "Nowcasting UK consumption and investment with monthly output components and realtime indicators," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2023-10, Jun.
• Macqueen, Rory & Galvão, Ana Beatriz & Wright, Stephen, 2023, "Can sectoral out and trade data improve early estimates of consumption and investment? A preliminary investigation," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2023-11, Jun.
• Papailias, Fotis & Kapetanios, George, 2023, "Nowcasting Unpaid Production Activity and Quality Adjustments in Public Service," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2023-20, Nov.
• Abhigayan Adhikary & Manoranjan Pal, 2023, "Long Run Predictions Using Gompertz Curves - A State Wise Analysis of COVID-19 Infections in India," International Econometric Review (IER), Economic Research Association, volume 15, issue 2, pages 45-58, September.
• Mariusz Pyra, 2023, "A Scenario Analysis for the Decarbonisation Process in Poland’s Road Transport Sector," European Research Studies Journal, European Research Studies Journal, volume 0, issue 1, pages 411-432.
• Pablo Pincheira-Brown & Nicolás Hardy & Cristobal Henrriquez & Ignacio Tapia & Andrea Bentancor, 2023, "Forecasting Base Metal Prices with an International Stock Index," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, volume 73, issue 3, pages 277-302, October.
• Gary Koop & Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon & Ping Wu, 2023, "Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting," Working Papers, Federal Reserve Bank of Cleveland, number 23-09, May, DOI: 10.26509/frbc-wp-202309.
• Kurt Graden Lunsford & Kenneth D. West, 2023, "Random Walk Forecasts of Stationary Processes Have Low Bias," Working Papers, Federal Reserve Bank of Cleveland, number 23-18, Aug, DOI: 10.26509/frbc-wp-202318.
• Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023, "Predictive Density Combination Using a Tree-Based Synthesis Function," Working Papers, Federal Reserve Bank of Cleveland, number 23-30, Nov, DOI: 10.26509/frbc-wp-202330.
• James Mitchell & Saeed Zaman, 2023, "The Distributional Predictive Content of Measures of Inflation Expectations," Working Papers, Federal Reserve Bank of Cleveland, number 23-31, Nov, DOI: 10.26509/frbc-wp-202331.
• Todd E. Clark & Matthew V. Gordon & Saeed Zaman, 2023, "Forecasting Core Inflation and Its Goods, Housing, and Supercore Components," Working Papers, Federal Reserve Bank of Cleveland, number 23-34, Dec, DOI: 10.26509/frbc-wp-202334.
• Bennett Schmanski & Chiara Scotti & Clara Vega, 2023, "Fed Communication, News, Twitter, and Echo Chambers," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2023-036, May, DOI: 10.17016/FEDS.2023.036.
• Kenneth Eva & Fabian Winkler, 2023, "A Comprehensive Empirical Evaluation of Biases in Expectation Formation," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2023-042, Jun, DOI: 10.17016/FEDS.2023.042.
• Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin Sicilian, 2023, "Identifying Financial Crises Using Machine Learning on Textual Data," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), number 1374, Mar, DOI: 10.17016/IFDP.2023.1374.
• Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023, "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), number 1385, Dec, DOI: 10.17016/IFDP.2023.1385.
• Raffaella Giacomini & Sokbae Lee & Silvia Sarpietro, 2023, "A Robust Method for Microforecasting and Estimation of Random Effects," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2023-26, Aug, DOI: 10.21033/wp-2023-26.
• Irene Botosaru & Raffaella Giacomini & Martin Weidner, 2023, "Forecasted Treatment Effects," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2023-32, Aug, DOI: 10.21033/wp-2023-32.
• Maximilian Ahrens & Deniz Erdemlioglu & Michael McMahon & Christopher J. Neely & Xiye Yang, 2023, "Mind Your Language: Market Responses to Central Bank Speeches," Working Papers, Federal Reserve Bank of St. Louis, number 2023-013, May, revised 28 Sep 2024, DOI: 10.20955/wp.2023.013.
• Miguel Faria-e-Castro & Fernando Leibovici, 2023, "Artificial Intelligence and Inflation Forecasts," Working Papers, Federal Reserve Bank of St. Louis, number 2023-015, Jul, revised 26 Feb 2024, DOI: 10.20955/wp.2023.015.
• Aaron Amburgey & Michael W. McCracken, 2023, "Growth-at-Risk is Investment-at-Risk," Working Papers, Federal Reserve Bank of St. Louis, number 2023-020, Aug, revised 14 Aug 2025, DOI: 10.20955/wp.2023.020.
• Sílvia Gonçalves & Michael W. McCracken & Yongxu Yao, 2023, "Bootstrapping out-of-sample predictability tests with real-time data," Working Papers, Federal Reserve Bank of St. Louis, number 2023-029, Nov, revised 03 Sep 2024, DOI: 10.20955/wp.2023.029.
• Katie Baker & Martín Almuzara & Hannah O’Keeffe & Argia M. Sbordone, 2023, "Reintroducing the New York Fed Staff Nowcast," Liberty Street Economics, Federal Reserve Bank of New York, number 20230908, Sep.
• Thorsten Drautzburg, 2023, "A Structural Approach to Combining External and DSGE Model Forecasts," Working Papers, Federal Reserve Bank of Philadelphia, number 23-10, Jun, DOI: 10.21799/frbp.wp.2023.10.
• Ludovic Dobbelaere & Igor Lebrun, 2023, "Working Paper 07-23 - Évaluation de la précision des prévisions à court terme et des perspectives à moyen terme du BFP. Une mise à jour du Working Paper 05-20
  [Working Paper 07-23 - Evaluatie van de nauwkeurigheid van de korte- en middellange-t," Working Papers, Federal Planning Bureau, Belgium, number 202307, Dec.
• Boris I. Alekhin, 2023, "Interregional Differences in Inflation through the Prism of Ackley’s Theory," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 8-25, February, DOI: 10.31107/2075-1990-2023-1-8-25.
• Tsukhlo Sergey, 2023, "Russian industry in 2022," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2023-1281, revised 2023.
• Barinova Vera & Zemtsov Stepan & Demidova Ksenia & Levakov P., 2023, "Business activity of small and medium-sized enterprises in Russia in the context of sanctions," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2023-1285, revised 2023.
• Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023, "Analysis of Opportunities to Improve the Quality of Natural Resource Price by Combining Forecasts Resulting from Methods Based on Regression Estimates of Weights
  [Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами К," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
• Anastasia D. Petaykina, 2023, "Predicting Changes in Household Consumption Using Neural Networks
  [Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Konstantin S. Rybak, 2023, "Evaluating the Role of Global Factors in GDP Nowcasting
  [Анализ Важности Глобальных Факторов Для Наукастинга Ввп]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 18-23, December.
• Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023, "Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
• Anastasia D. Petaykina, 2023, "Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Konstantin S. Rybak, 2023, "Анализ Важности Глобальных Факторов Для Наукастинга Ввп," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 18-23, December.
• Andrey Polbin & Andrei Shumilov, 2023, "Forecasting inflation in Russia using a TVP model with Bayesian shrinkage," Working Papers, Gaidar Institute for Economic Policy, number wpaper-2023-1462, revised 2023.
• Dean Fantazzini & Yufeng Xiao, 2023, "Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases," Econometrics, MDPI, volume 11, issue 3, pages 1-73, August.
• James T. E. Chapman & Ajit Desai, 2023, "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, volume 5, issue 4, pages 1-32, November.
• Rangan Gupta & Yuvana Jaichand & Christian Pierdzioch & Reneé van Eyden, 2023, "Realized Stock-Market Volatility of the United States and the Presidential Approval Rating," Mathematics, MDPI, volume 11, issue 13, pages 1-27, July.
• Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023, "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, volume 11, issue 9, pages 1-21, April.
• Mihnea Constantinescu, 2023, "Sparse Warcasting," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 15-2023, Sep, revised 02 Oct 2023.
• John B. Guerard & Dimitrios D. Thomakos & Foteini Kyriazi & Konstantinos Mamais, 2023, "On the Predictability of the DJIA and S&P500 Indices," Working Papers, The George Washington University, The Center for Economic Research, number 2023-001, Jan.
• Dr. Marc Ingo Wolter & Florian Bernardt & Jannik Daßler & Saskia Reuschel & Dr. Britta Stöver, 2023, "Klimafolgen und Anpassung – 2023," GWS Research Report Series, GWS - Institute of Economic Structures Research, number 23-6.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print, HAL, number emse-04624940, Jul, DOI: 10.1016/j.ijforecast.2022.04.009.
• Jonathan Benchimol & Lahcen Bounader, 2023, "Optimal monetary policy under bounded rationality," Post-Print, HAL, number emse-04624979, Aug, DOI: 10.1016/j.jfs.2023.101151.
• Laurent Ferrara & Anna Simoni, 2023, "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print, HAL, number hal-03919944, Oct, DOI: 10.1080/07350015.2022.2116025.
• F. Blasques & Christian Francq & Sébastien Laurent, 2023, "Quasi score-driven models," Post-Print, HAL, number hal-04069143, May, DOI: 10.1016/j.jeconom.2021.12.005.
• Andreas Dibiasi & Samad Sarferaz, 2023, "Measuring macroeconomic uncertainty: A cross-country analysis," Post-Print, HAL, number hal-04167343, Apr, DOI: 10.1016/j.euroecorev.2023.104383.
• Sahed Abdelkader & Kahoui Hacene, 2023, "Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network," Post-Print, HAL, number hal-04183403, Jun.
• Benjamin Monnery & François-Charles Wolff, 2023, "Is participatory democracy in line with social protest? Evidence from the French Yellow Vests movement," Post-Print, HAL, number hal-04197291, DOI: 10.1007/s11127-023-01105-5.
• Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2023, "A penalized two-pass regression to predict stock returns with time-varying risk premia," Post-Print, HAL, number hal-04325655, Dec, DOI: 10.1016/j.jeconom.2022.12.004.
• Abdelhakim Aknouche & Christian Francq, 2023, "Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models," Post-Print, HAL, number hal-05417229, Dec, DOI: 10.1016/j.jeconom.2021.09.002.
• Benjamin Monnery & François-Charles Wolff, 2023, "Is participatory democracy in line with social protest? Evidence from the French Yellow Vests movement," Working Papers, HAL, number hal-04194969.
• J. van den Berg, Gerard & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2023, "Predicting re-employment: machine learning versus assessments by unemployed workers and by their caseworkers," Working Paper Series, IFAU - Institute for Evaluation of Labour Market and Education Policy, number 2023:22, Nov.
• Andersson, Jonas & Sheybanivaziri, Samaneh, 2023, "Probabilistic forecasting of electricity prices using an augmented LMARX-model," Discussion Papers, Norwegian School of Economics, Department of Business and Management Science, number 2023/11, Jul.
• Bårdsen, Gunnar & Nymoen, Ragnar, 2023, "Dynamic time series modelling and forecasting of COVID-19 in Norway," Memorandum, Oslo University, Department of Economics, number 3/2023, May.
• Vladimir Sviyazov, 2023, "Is There a Weekend Effect? Russian Stock Market Research Based on Fuzzy Systems," HSE Economic Journal, National Research University Higher School of Economics, volume 27, issue 3, pages 412-434.
• Watanabe, Toshiaki & Nakajima, Jouchi, 2023, "High-frequency realized stochastic volatility model," Discussion paper series, Hitotsubashi Institute for Advanced Study, Hitotsubashi University, number HIAS-E-127, Jan.
• Kouach Yassine & EL Attar Abderrahim & EL Hachloufi Mostafa, 2023, "Retakaful Contributions Model Using Machine Learning Techniques," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, volume 9, issue 3, pages 511-532, September, DOI: https://doi.org/10.21098/jimf.v9i3..
• Saurabh Ghosh & Abhishek Ranjan, 2023, "A Machine Learning Approach to GDP Nowcasting: An Emerging Market Experience," Bulletin of Monetary Economics and Banking, Bank Indonesia, volume 26, issue Special I, pages 33-54, February, DOI: https://doi.org/10.59091/1410-8046..
• Fortin, Ines & Hlouskova, Jaroslava, 2023, "Regime-dependent nowcasting of the Austrian economy," IHS Working Paper Series, Institute for Advanced Studies, number 51, Dec.
• Marcus Buckmann & Andreas Joseph, 2023, "An Interpretable Machine Learning Workflow with an Application to Economic Forecasting," International Journal of Central Banking, International Journal of Central Banking, volume 19, issue 4, pages 449-522, October.
• Caterina Lepore & Roshen Fernando, 2023, "Global Economic Impacts of Physical Climate Risks," IMF Working Papers, International Monetary Fund, number 2023/183, Sep.
• José Eduardo Medina Reyes & Agustín Ignacio Cabrera Llanos & Salvador Cruz Aké, 2023, "Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 18, issue 3, pages 1-22, Julio - S.
• Enrique R. Casares & María Guadalupe García-Salazar & Leobardo Pedro Plata Pérez & José Manuel Ramos Varela, 2023, "Deuda externa y crecimiento económico. Una calibración para México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 18, issue 3, pages 1-24, Julio - S.

2022

• Zhu, Yinchu & Timmermann, Allan, 2022, "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 329-347, DOI: 10.1016/j.jeconom.2021.10.006.
• Bollerslev, Tim & Medeiros, Marcelo C. & Patton, Andrew J. & Quaedvlieg, Rogier, 2022, "From zero to hero: Realized partial (co)variances," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 348-360, DOI: 10.1016/j.jeconom.2021.04.013.
• Gardner, Ben & Scotti, Chiara & Vega, Clara, 2022, "Words speak as loudly as actions: Central bank communication and the response of equity prices to macroeconomic announcements," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 387-409, DOI: 10.1016/j.jeconom.2021.07.014.
• Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022, "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 500-519, DOI: 10.1016/j.jeconom.2021.04.012.
• Diebold, Francis X. & Rudebusch, Glenn D., 2022, "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 520-534, DOI: 10.1016/j.jeconom.2020.12.007.
• du Plessis, Emile, 2022, "Multinomial modeling methods: Predicting four decades of international banking crises," Economic Systems, Elsevier, volume 46, issue 2, DOI: 10.1016/j.ecosys.2022.100979.
• Korobilis, Dimitris, 2022, "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, volume 148, issue C, DOI: 10.1016/j.euroecorev.2022.104241.
• Rubesam, Alexandre, 2022, "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, volume 51, issue PB, DOI: 10.1016/j.ememar.2022.100891.
• Zhang, Han & Guo, Bin & Liu, Lanbiao, 2022, "The time-varying bond risk premia in China," Journal of Empirical Finance, Elsevier, volume 65, issue C, pages 51-76, DOI: 10.1016/j.jempfin.2021.11.004.
• Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022, "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, volume 106, issue C, DOI: 10.1016/j.eneco.2021.105760.
• Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022, "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, volume 106, issue C, DOI: 10.1016/j.eneco.2021.105802.
• Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022, "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, volume 107, issue C, DOI: 10.1016/j.eneco.2022.105811.
• Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022, "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105862.
• Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022, "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105934.
• Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022, "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105936.
• Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022, "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105947.
• Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022, "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105960.
• Umar, Zaghum & Aharon, David Y. & Esparcia, Carlos & AlWahedi, Wafa, 2022, "Spillovers between sovereign yield curve components and oil price shocks," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105963.
• Xing, Li-Min & Zhang, Yue-Jun, 2022, "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, volume 110, issue C, DOI: 10.1016/j.eneco.2022.106014.