Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2023

• Doojav Gan-Ochir & Luvsannyam Davaajargal, 2023, "Forecasting Inflation in Mongolia: A Dynamic Model Averaging Approach," Journal of Time Series Econometrics, De Gruyter, volume 15, issue 1, pages 27-48, January, DOI: 10.1515/jtse-2020-0021.
• Abbara Omar & Zevallos Mauricio, 2023, "Estimation and forecasting of long memory stochastic volatility models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 27, issue 1, pages 1-24, February, DOI: 10.1515/snde-2020-0106.
• Martina Jakob & Sebastian Heinrich, 2023, "Measuring Human Capital with Social Media Data and Machine Learning," University of Bern Social Sciences Working Papers, University of Bern, Department of Social Sciences, number 46, May.
• Carole Bonnet & Sandrine Juin & Anne Laferrère, 2023, "Financer la perte d'autonomie : la piste des prêts viagers hypothécaires et de l'assurance obligatoire," Revue d'économie financière, Association d'économie financière, volume 0, issue 4, pages 217-233.
• Congressional Budget Office, 2023, "The Accuracy of CBO’s Budget Projections for Fiscal Year 2022," Reports, Congressional Budget Office, number 58603, Jan.
• Congressional Budget Office, 2023, "An Evaluation of CBO’s Projections of Outlays From 1984 to 2021," Reports, Congressional Budget Office, number 58613, Apr.
• Congressional Budget Office, 2023, "CBO’s Economic Forecasting Record: 2023 Update," Reports, Congressional Budget Office, number 59078, Jun.
• Congressional Budget Office, 2023, "The Accuracy of CBO’s Budget Projections for Fiscal Year 2023," Reports, Congressional Budget Office, number 59682, Dec.
• Byoung Hark Yoo, 2023, "Conditional Forecasting With a Bayesian Vector Autoregression: Working Paper 2023-08," Working Papers, Congressional Budget Office, number 59629, Nov.
• Constantin Bürgi, 2023, "How to Deal With Missing Observations in Surveys of Professional Forecasters," CESifo Working Paper Series, CESifo, number 10203.
• Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2023, "Variable Selection in High Dimensional Linear Regressions with Parameter Instability," CESifo Working Paper Series, CESifo, number 10223.
• Robert Lehmann & Ida Wikman, 2023, "Quarterly GDP Estimates for the German States: New Data for Business Cycle Analyses and Long-Run Dynamics," CESifo Working Paper Series, CESifo, number 10280.
• Robert Lehmann, 2023, "READ-GER: Introducing German Real-Time Regional Accounts Data for Revision Analysis and Nowcasting," CESifo Working Paper Series, CESifo, number 10315.
• Kajal Lahiri & Cheng Yang, 2023, "ROC and PRC Approaches to Evaluate Recession Forecasts," CESifo Working Paper Series, CESifo, number 10449.
• Friederike Fourné & Robert Lehmann, 2023, "From Shopping to Statistics: Tracking and Nowcasting Private Consumption Expenditures in Real-Time," CESifo Working Paper Series, CESifo, number 10764.
• Sören Blomquist, 2023, "Evaluating the Discrete Choice and BN Methods to Estimate Labor Supply Functions," CESifo Working Paper Series, CESifo, number 10827.
• Stefan Sauer & Moritz Schasching & Klaus Wohlrabe, 2023, "Handbook of ifo Surveys," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 100.
• Benjamin Born & Zeno Enders & Manuel Menkhoff & Gernot J. Müller & Knut Niemann, 2023, "Firm Expectations and News: Micro v Macro," ifo Working Paper Series, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 400.
• Charles Kenny & Zack Gehan, 2023, "Scenarios for Future Global Growth to 2050," Working Papers, Center for Global Development, number 634, Mar.
• Bryan Kelly & Semyon Malamud & Mohammad Pourmohammadi & Fabio Trojani, 2023, "Universal Portfolio Shrinkage," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 23-119, Dec.
• Markus Leippold & Hanlin Yang, 2023, "Mixed-Frequency Predictive Regressions with Parameter Learning," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 23-39, Mar, revised Jun 2023.
• Michal Andrle & Jan Bruha, 2023, "A Sparse Kalman Filter: A Non-Recursive Approach," Working Papers, Czech National Bank, Research and Statistics Department, number 2023/13, Nov.
• Miguel Sebastiano Chalup Calmotti & Luis Fernando Escobar Caba, 2023, "Efectos macroeconómicos de la política fiscal durante la crisis del Covid-19: evidencia de Bolivia a nivel regional," Revista de Economía del Rosario, Universidad del Rosario, volume 26, issue 1, pages 1-39.
• Seydyss Garay Rodríguez & Pavel Vidal-Alejandro & Julieth Cerón-Ordoñez, 2023, "El monitoreo del sector de la construcción en el Valle del Cauca," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, volume 42, issue 75, pages 237-271.
• Oscar López-Solís & Alexander Fernando Haro Sarango & Ana Córdova-Pacheco & Juan Pérez-Briceño, 2023, "El teorema Modigliani-Miller: un análisis desde la estructura de capital mediante modelos Data Mining en pymes del sector comercio," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, volume 15, issue 1, pages 45-66.
• Emile Cammeraat & Brinn Hekkelman & Pim Kastelein & Suzanne Vissers, 2023, "Predictability and (co-)incidence of labor and health shocks," CPB Discussion Paper, CPB Netherlands Bureau for Economic Policy Analysis, number 453, Dec, DOI: 10.34932/n1bs-qm11.
• Ahrens, Maximilian & Erdemlioglu, Deniz & Mcmahon, Michael & Neely, Christopher J & Yang, Xiye, 2023, "Mind Your Language: Market Responses to Central Bank Speeches," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 18191, Jun.
• Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano, 2023, "Forecasting US Inflation Using Bayesian Nonparametric Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 18244, Jun.
• Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023, "Density forecasts of inflation: a quantile regression forest approach," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 18298, Jul.
• Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2023, "Labour at risk," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 18432, Sep.
• Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2023, "Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 18549, Oct.
• Moon, Hyungsik Roger & Schorfheide, Frank & Zhang, Boyuan, 2023, "Bayesian Estimation of Panel Models under Potentially Sparse Heterogeneity," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 18560, Oct.
• Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023, "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster, number 10523, Jun.
• Verena Monschang & Mark Trede & Bernd Wilfling, 2023, "Multi-horizon uniform superior predictive ability revisited: A size-exploiting and consistent test," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster, number 10623, Nov.
• Mohana Mondal & Michael P. Cameron & Jacques Poot, 2023, "Towards a Dynamic Spatial Microsimulation Model for Projecting Auckland’s Spatial Distribution of Ethnic Groups," RFBerlin Discussion Paper Series, ROCKWOOL Foundation Berlin (RFBerlin), number 2303, Feb.
• Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2023, "Deep Dynamic Factor Models," Working Papers, Center for Research in Economics and Statistics, number 2023-08, May.
• Gerard J. van den Berg & Max Kunaschk & Julia Lang & Gesine Stephan & Arne Uhlendorf, 2023, "Predicting Re-Employment: Machine Learning Versus Assessments by Unemployed Workers and by Their Caseworkers," Working Papers, Center for Research in Economics and Statistics, number 2023-09, Aug.
• Marín Díazaraque, Juan Miguel & Lopes Moreira da Veiga, María Helena, 2023, "Shock-triggered asymmetric response stochastic volatility," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 36569, Feb.
• González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2023, "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 37968, Jul.
• Simon SCHNÜRCH & Torsten KLEINOW & Andreas WAGNER, 2023, "Accounting for COVID-19-type shocks in mortality modeling: a comparative study," JODE - Journal of Demographic Economics, Cambridge University Press, volume 89, issue 3, pages 483-512, September, DOI: 10.1017/dem.2023.9.
• Kamil Dawid Grzebien & María Jesús Segovia-Vargas, 2023, "Predicción del riesgo de impago en los préstamos P2P," Revista de Economía y Finanzas (REyF), Asociación Cuadernos de Economía, volume 1, issue 2, pages 175-188, Mayo.
• Michael Sampson, 2023, "Learning About New Eras," Annals of Economics and Finance, Society for AEF, volume 24, issue 1, pages 1-12, May.
• Lee, Ji Hyung & Shin, Youngki, 2023, "Complete Subset Averaging For Quantile Regressions," Econometric Theory, Cambridge University Press, volume 39, issue 1, pages 146-188, February.
• Micocci, Francesca & Rungi, Armando, 2023, "Predicting Exporters with Machine Learning," World Trade Review, Cambridge University Press, volume 22, issue 5, pages 584-607, December.
• Sailesh BHAGHOE & Gavin OOFT, 2023, "Nowcasting quarterly GDP growth in Suriname with factor-MIDAS and mixed-frequency VAR models," Journal of Economics and Political Economy, EconSciences Journals, volume 10, issue 1, pages 1-18, March.
• Pablo Garcia & Pascal Jacquinot & ÄŒrt LenarÄ iÄ & Matija Lozej & Kostas Mavromatis, 2023, "Global models for a global pandemic: the impact of COVID-19 on small euro area economies," Working Papers, DNB, number 782, Jun.
• Benjamin Monnery & François-Charles Wolff, 2023, "Is participatory democracy in line with social protest? Evidence from the French Yellow Vests movement," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2023-23.
• Christopher E.S. WARBURTON & Jared PEMBERTON, 2023, "Volatile Financial Conditions, Asset Prices, and Investment Decisions: Analysis of daily data of DJIA and S&P500, from January to April of 2022," Applied Econometrics and International Development, Euro-American Association of Economic Development, volume 23, issue 1, pages 101-124.
• Germann, Maximilian & Kusmierczyk, Piotr & Puyo, Christelle, 2023, "Results of the 2022 climate risk stress test of the Eurosystem balance sheet," Economic Bulletin Boxes, European Central Bank, volume 2.
• Burban, Valentin & Schupp, Fabian, 2023, "Backcasting real interest rates and inflation expectations – combining market-based measures with historical data for related variables," Economic Bulletin Boxes, European Central Bank, volume 2.
• Emambakhsh, Tina & Fuchs, Maximilian & Kördel, Simon & Kouratzoglou, Charalampos & Lelli, Chiara & Pizzeghello, Riccardo & Salleo, Carmelo & Spaggiari, Martina, 2023, "The Road to Paris: stress testing the transition towards a net-zero economy," Occasional Paper Series, European Central Bank, number 328, Sep.
• Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023, "Forecasting euro area inflation with machine-learning models," Research Bulletin, European Central Bank, volume 112.
• Warne, Anders, 2023, "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series, European Central Bank, number 2768, Jan.
• Martínez, Carlos Cañizares & de Bondt, Gabe & Gieseck, Arne, 2023, "Forecasting housing investment," Working Paper Series, European Central Bank, number 2807, Apr.
• Bańbura, Marta & Belousova, Irina & Bodnár, Katalin & Tóth, Máté Barnabás, 2023, "Nowcasting employment in the euro area," Working Paper Series, European Central Bank, number 2815, May.
• Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023, "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series, European Central Bank, number 2830, Jul.
• Chinn, Menzie D. & Meunier, Baptiste & Stumpner, Sebastian, 2023, "Nowcasting world trade with machine learning: a three-step approach," Working Paper Series, European Central Bank, number 2836, Aug.
• Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2023, "Labour at risk," Working Paper Series, European Central Bank, number 2840, Aug.
• Chavleishvili, Sulkhan & Kremer, Manfred, 2023, "Measuring systemic financial stress and its risks for growth," Working Paper Series, European Central Bank, number 2842, Aug.
• Khondokar Jilhajj, 2023, "Forecasting Lending Interest Rate and Deposit Interest Rate of Bangladesh Using the Autoregressive Integrated Moving Average Model," International Journal of Economics and Financial Issues, Econjournals, volume 13, issue 3, pages 169-177, May.
• William Djamfa Mbiakop & Hlalefang Khobai & Djomo Choumbou Raoul Fani, 2023, "Response of Agriculture Production to Change of Foreign Direct Investment and Public Agriculture Expenditure in South Africa: A Monte Carlo Simulation Analysis," International Journal of Economics and Financial Issues, Econjournals, volume 13, issue 6, pages 1-7, November.
• Amine Mounir, 2023, "Crude Oil Price Movements between Fundamental and Uncertainty: Evidence from Frequency Causality Tests," International Journal of Energy Economics and Policy, Econjournals, volume 13, issue 3, pages 428-433, May.
• Mustofa Usman & M. Komarudin & Nurhanurawati Nurhanurawati & Edwin Russel & Ahmad Sidiq & Warsono Warsono & F. A.M Elfaki, 2023, "Dynamic Modeling and Analysis of Some Energy Companies of Indonesia Over the Year 2018 to 2022 By Using VAR(p)-CCC GARCH(r,s) Model: -," International Journal of Energy Economics and Policy, Econjournals, volume 13, issue 4, pages 542-554, July.
• Samuel John Parreno, 2023, "Forecasting the Total Non-coincidental Monthly System Peak Demand in the Philippines: A Comparison of Seasonal Autoregressive Integrated Moving Average Models and Artificial Neural Networks," International Journal of Energy Economics and Policy, Econjournals, volume 13, issue 5, pages 544-552, September.
• Abdulrazak Nur Mohamed & Idiris Sid Ali Mohamed, 2023, "Precious Metals and Oil Price Dynamics," International Journal of Energy Economics and Policy, Econjournals, volume 13, issue 6, pages 119-128, November.
• Hamdy Ahmad Aly Alhendawy & Mohammed Galal Abdallah Mostafa & Mohamed Ibrahim Elgohari & Ibrahim Abdalla Abdelraouf Mohamed & Nabil Medhat Arafat Mahmoud & Mohamed Ahmed Mohamed Mater, 2023, "Determinants of Renewable Energy Production in Egypt New Approach: Machine Learning Algorithms," International Journal of Energy Economics and Policy, Econjournals, volume 13, issue 6, pages 679-689, November.
• Kukacka, Jiri & Sacht, Stephen, 2023, "Estimation of heuristic switching in behavioral macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, volume 146, issue C, DOI: 10.1016/j.jedc.2022.104585.
• Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023, "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, volume 148, issue C, DOI: 10.1016/j.jedc.2023.104607.
• Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023, "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, volume 149, issue C, DOI: 10.1016/j.jedc.2023.104636.
• Shintani, Mototsugu & Ueda, Kozo, 2023, "Identifying the source of information rigidities in the expectations formation process," Journal of Economic Dynamics and Control, Elsevier, volume 150, issue C, DOI: 10.1016/j.jedc.2023.104653.
• Ahn, Hie Joo, 2023, "Duration structure of unemployment hazards and the trend unemployment rate," Journal of Economic Dynamics and Control, Elsevier, volume 151, issue C, DOI: 10.1016/j.jedc.2023.104664.
• Zheng, Tingguo & Ye, Shiqi & Hong, Yongmiao, 2023, "Fast estimation of a large TVP-VAR model with score-driven volatilities," Journal of Economic Dynamics and Control, Elsevier, volume 157, issue C, DOI: 10.1016/j.jedc.2023.104762.
• Tsuchiya, Yoichi, 2023, "Assessing the World Bank’s growth forecasts," Economic Analysis and Policy, Elsevier, volume 77, issue C, pages 64-84, DOI: 10.1016/j.eap.2022.10.017.
• Kawamoto, Takuji & Nakazawa, Takashi & Kishaba, Yui & Matsumura, Kohei & Nakajima, Jouchi, 2023, "Estimating the macroeconomic effects of Japan’s expansionary monetary policy under Quantitative and Qualitative Monetary Easing during 2013–2020," Economic Analysis and Policy, Elsevier, volume 78, issue C, pages 208-224, DOI: 10.1016/j.eap.2023.03.007.
• Sen, Anindya & Baker, John David & Zhang, Qihuang & Agarwal, Rishav Raj & Lam, Jean-Paul, 2023, "Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces," Economic Analysis and Policy, Elsevier, volume 78, issue C, pages 225-242, DOI: 10.1016/j.eap.2023.03.006.
• Shah, Sayar Ahmad & Garg, Bhavesh, 2023, "Identifying efficient policy mix under different targeting regimes: A tale of two crises," Economic Analysis and Policy, Elsevier, volume 78, issue C, pages 975-994, DOI: 10.1016/j.eap.2023.04.019.
• Zhang, Li & Li, Yan & Yu, Sixin & Wang, Lu, 2023, "Risk transmission of El Niño-induced climate change to regional Green Economy Index," Economic Analysis and Policy, Elsevier, volume 79, issue C, pages 860-872, DOI: 10.1016/j.eap.2023.07.006.
• Peng, Lijuan & Pan, Zhigang & Liang, Chao & Umar, Muhammad, 2023, "Exchange rate volatility predictability: A new insight from climate policy uncertainty," Economic Analysis and Policy, Elsevier, volume 80, issue C, pages 688-700, DOI: 10.1016/j.eap.2023.09.017.
• Arango-Castillo, Lenin & Orraca, María José & Molina, G. Stefano, 2023, "The global component of headline and core inflation in emerging market economies and its ability to improve forecasting performance," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106121.
• Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023, "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106160.
• Ojeda-Joya, Jair & Romero, José Vicente, 2023, "Global uncertainty shocks and exchange-rate expectations in Latin America," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106185.
• Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023, "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, volume 120, issue C, DOI: 10.1016/j.econmod.2022.106188.
• Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023, "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, volume 121, issue C, DOI: 10.1016/j.econmod.2023.106214.
• Zhang, Qin & Ni, He & Xu, Hao, 2023, "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, volume 122, issue C, DOI: 10.1016/j.econmod.2023.106204.
• Zhao, Shangwei & Xie, Tian & Ai, Xin & Yang, Guangren & Zhang, Xinyu, 2023, "Correcting sample selection bias with model averaging for consumer demand forecasting," Economic Modelling, Elsevier, volume 123, issue C, DOI: 10.1016/j.econmod.2023.106275.
• Qiu, Yue & Zheng, Yuchen, 2023, "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, volume 125, issue C, DOI: 10.1016/j.econmod.2023.106349.
• McKibbin, Warwick & Fernando, Roshen, 2023, "The global economic impacts of the COVID-19 pandemic," Economic Modelling, Elsevier, volume 129, issue C, DOI: 10.1016/j.econmod.2023.106551.
• Hambuckers, J. & Ulm, M., 2023, "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, volume 129, issue C, DOI: 10.1016/j.econmod.2023.106554.
• Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2023, "The confidence channel of U.S. financial uncertainty: Evidence from industry-level data," Economic Modelling, Elsevier, volume 129, issue C, DOI: 10.1016/j.econmod.2023.106557.
• Yan, Wan-Lin, 2023, "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.najef.2022.101867.
• Li, Houjian & Zhou, Deheng & Hu, Jiayu & Li, Junwen & Su, Mengying & Guo, Lili, 2023, "Forecasting the realized volatility of Energy Stock Market: A multimodel comparison," The North American Journal of Economics and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.najef.2023.101895.
• Wu, Xinyu & Zhao, An & Liu, Li, 2023, "Forecasting VIX using two-component realized EGARCH model," The North American Journal of Economics and Finance, Elsevier, volume 67, issue C, DOI: 10.1016/j.najef.2023.101934.
• Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023, "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, volume 67, issue C, DOI: 10.1016/j.najef.2023.101948.
• Caiado, Jorge & Lúcio, Francisco, 2023, "Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, volume 68, issue C, DOI: 10.1016/j.najef.2023.101971.
• Cepni, Oguzhan & Christou, Christina & Gupta, Rangan, 2023, "Forecasting national recessions of the United States with state-level climate risks: Evidence from model averaging in Markov-switching models," Economics Letters, Elsevier, volume 227, issue C, DOI: 10.1016/j.econlet.2023.111121.
• Winkelried, Diego, 2023, "Simple interpolations of inflation expectations," Economics Letters, Elsevier, volume 229, issue C, DOI: 10.1016/j.econlet.2023.111230.
• Cavicchioli, Maddalena, 2023, "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, volume 232, issue C, DOI: 10.1016/j.econlet.2023.111357.
• Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023, "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, volume 232, issue 1, pages 52-69, DOI: 10.1016/j.jeconom.2020.11.006.
• Hounyo, Ulrich & Lahiri, Kajal, 2023, "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, volume 232, issue 2, pages 445-468, DOI: 10.1016/j.jeconom.2021.09.011.
• Ding, Yashuang (Dexter), 2023, "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, volume 232, issue 2, pages 521-543, DOI: 10.1016/j.jeconom.2021.09.012.
• Blasques, F. & Francq, Christian & Laurent, Sébastien, 2023, "Quasi score-driven models," Journal of Econometrics, Elsevier, volume 234, issue 1, pages 251-275, DOI: 10.1016/j.jeconom.2021.12.005.
• Arias, Jonas E. & Rubio-Ramírez, Juan F. & Shin, Minchul, 2023, "Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 1054-1086, DOI: 10.1016/j.jeconom.2022.04.013.
• Lee, Ji Hyung & Park, Byoung G., 2023, "Nonparametric identification and estimation of the extended Roy model," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 1087-1113, DOI: 10.1016/j.jeconom.2022.10.001.
• Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023, "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 1355-1377, DOI: 10.1016/j.jeconom.2022.09.007.
• Abadir, Karim M. & Luati, Alessandra & Paruolo, Paolo, 2023, "GARCH density and functional forecasts," Journal of Econometrics, Elsevier, volume 235, issue 2, pages 470-483, DOI: 10.1016/j.jeconom.2022.04.010.
• Bennedsen, Mikkel & Lunde, Asger & Shephard, Neil & Veraart, Almut E.D., 2023, "Inference and forecasting for continuous-time integer-valued trawl processes," Journal of Econometrics, Elsevier, volume 236, issue 2, DOI: 10.1016/j.jeconom.2023.105476.
• Diebold, Francis X. & Rudebusch, Glenn D. & Göbel, Maximilian & Goulet Coulombe, Philippe & Zhang, Boyuan, 2023, "When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume," Journal of Econometrics, Elsevier, volume 236, issue 2, DOI: 10.1016/j.jeconom.2023.105479.
• Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023, "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, volume 236, issue 2, DOI: 10.1016/j.jeconom.2023.105490.
• Tu, Yundong & Xie, Xinling, 2023, "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, volume 237, issue 1, DOI: 10.1016/j.jeconom.2023.105509.
• Andersen, Torben G. & Li, Yingying & Todorov, Viktor & Zhou, Bo, 2023, "Volatility measurement with pockets of extreme return persistence," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2020.11.005.
• Aknouche, Abdelhakim & Francq, Christian, 2023, "Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2021.09.002.
• Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2023, "Evaluating forecast performance with state dependence," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2021.07.015.
• Berrisch, Jonathan & Ziel, Florian, 2023, "CRPS learning," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2021.11.008.
• Zhang, Xiaomeng & Zhang, Xinyu, 2023, "Optimal model averaging based on forward-validation," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.03.010.
• Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2023, "A flexible predictive density combination for large financial data sets in regular and crisis periods," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.11.004.
• Fan, Rui & Lee, Ji Hyung & Shin, Youngki, 2023, "Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.11.006.
• Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023, "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.11.007.
• Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023, "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.12.004.
• Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023, "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, volume 237, issue 2, DOI: 10.1016/j.jeconom.2022.09.008.
• Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023, "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, volume 26, issue C, pages 3-16, DOI: 10.1016/j.ecosta.2022.03.008.
• Hallin, Marc & Trucíos, Carlos, 2023, "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, volume 27, issue C, pages 1-15, DOI: 10.1016/j.ecosta.2021.04.006.
• Crucitti, Francesca & Lazarou, Nicholas-Joseph & Monfort, Philippe & Salotti, Simone, 2023, "Where does the EU cohesion policy produce its benefits? A model analysis of the international spillovers generated by the policy," Economic Systems, Elsevier, volume 47, issue 3, DOI: 10.1016/j.ecosys.2023.101076.
• Borrotti, Matteo & Rabasco, Michele & Santoro, Alessandro, 2023, "Using accounting information to predict aggressive tax location decisions by European groups," Economic Systems, Elsevier, volume 47, issue 3, DOI: 10.1016/j.ecosys.2023.101090.
• Zakharenko, Roman, 2023, "Pricing shared vehicles," Economics of Transportation, Elsevier, volume 33, issue C, DOI: 10.1016/j.ecotra.2022.100296.
• Dibiasi, Andreas & Sarferaz, Samad, 2023, "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, volume 153, issue C, DOI: 10.1016/j.euroecorev.2023.104383.
• Khalfaoui, Rabeh & Hammoudeh, Shawkat & Rehman, Mohd Ziaur, 2023, "Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network," Emerging Markets Review, Elsevier, volume 54, issue C, DOI: 10.1016/j.ememar.2023.101002.
• Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023, "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, volume 70, issue C, pages 144-164, DOI: 10.1016/j.jempfin.2022.12.001.
• Nonejad, Nima, 2023, "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, volume 70, issue C, pages 91-122, DOI: 10.1016/j.jempfin.2022.11.009.
• Ferrer Fernández, María & Henry, Ólan & Pybis, Sam & Stamatogiannis, Michalis P., 2023, "Can we forecast better in periods of low uncertainty? The role of technical indicators," Journal of Empirical Finance, Elsevier, volume 71, issue C, pages 1-12, DOI: 10.1016/j.jempfin.2022.12.014.
• Yu, Deshui & Huang, Difang & Chen, Li, 2023, "Stock return predictability and cyclical movements in valuation ratios," Journal of Empirical Finance, Elsevier, volume 72, issue C, pages 36-53, DOI: 10.1016/j.jempfin.2023.02.004.
• Wang, Keli & Liu, Xiaoquan & Ye, Wuyi, 2023, "Intraday VaR: A copula-based approach," Journal of Empirical Finance, Elsevier, volume 74, issue C, DOI: 10.1016/j.jempfin.2023.101419.
• Souropanis, Ioannis & Vivian, Andrew, 2023, "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, volume 74, issue C, DOI: 10.1016/j.jempfin.2023.101432.
• Degiannakis, Stavros & Filis, George, 2023, "Oil price assumptions for macroeconomic policy," Energy Economics, Elsevier, volume 117, issue C, DOI: 10.1016/j.eneco.2022.106425.
• Blazsek, Szabolcs & Escribano, Alvaro, 2023, "Score-driven threshold ice-age models: Benchmark models for long-run climate forecasts," Energy Economics, Elsevier, volume 118, issue C, DOI: 10.1016/j.eneco.2023.106522.
• Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023, "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, volume 119, issue C, DOI: 10.1016/j.eneco.2023.106521.
• Qu, Hui & Li, Guo, 2023, "Multi-perspective investor attention and oil futures volatility forecasting," Energy Economics, Elsevier, volume 119, issue C, DOI: 10.1016/j.eneco.2023.106531.
• Li, Jingpeng & Umar, Muhammad & Huo, Jiale, 2023, "The spillover effect between Chinese crude oil futures market and Chinese green energy stock market," Energy Economics, Elsevier, volume 119, issue C, DOI: 10.1016/j.eneco.2023.106568.
• Sohag, Kazi & Hassan, M. Kabir & Bakhteyev, Stepan & Mariev, Oleg, 2023, "Do green and dirty investments hedge each other?," Energy Economics, Elsevier, volume 120, issue C, DOI: 10.1016/j.eneco.2023.106573.
• Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023, "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, volume 120, issue C, DOI: 10.1016/j.eneco.2023.106602.
• Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2023, "Asymmetry and interdependence when evaluating U.S. Energy Information Administration forecasts," Energy Economics, Elsevier, volume 121, issue C, DOI: 10.1016/j.eneco.2023.106620.
• Abdollahi, Hooman, 2023, "Oil price volatility and new evidence from news and Twitter," Energy Economics, Elsevier, volume 122, issue C, DOI: 10.1016/j.eneco.2023.106711.
• Liu, Yue & Sun, Huaping & Meng, Bo & Jin, Shunlin & Chen, Bin, 2023, "How to purchase carbon emission right optimally for energy-consuming enterprises? Analysis based on optimal stopping model," Energy Economics, Elsevier, volume 124, issue C, DOI: 10.1016/j.eneco.2023.106758.
• Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023, "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, volume 124, issue C, DOI: 10.1016/j.eneco.2023.106833.
• Tian, Guangning & Peng, Yuchao & Meng, Yuhao, 2023, "Forecasting crude oil prices in the COVID-19 era: Can machine learn better?," Energy Economics, Elsevier, volume 125, issue C, DOI: 10.1016/j.eneco.2023.106788.
• Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023, "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, volume 125, issue C, DOI: 10.1016/j.eneco.2023.106843.
• Nonejad, Nima, 2023, "Modeling the out-of-sample predictive relationship between equity premium, returns on the price of crude oil and economic policy uncertainty using multivariate time-varying dimension models," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106964.
• Bennedsen, Mikkel & Hillebrand, Eric & Jensen, Sebastian, 2023, "A neural network approach to the environmental Kuznets curve," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106985.
• Li, Yan & Huynh, Luu Duc Toan & Xu, Yongan & Liang, Hao, 2023, "The forecast ability of a belief-based momentum indicator in full-day, daytime, and nighttime volatilities of Chinese oil futures," Energy Economics, Elsevier, volume 127, issue PB, DOI: 10.1016/j.eneco.2023.107064.
• Wang, Cheng & Bouri, Elie & Xu, Yahua & Zhang, Dingsheng, 2023, "Intraday and overnight tail risks and return predictability in the crude oil market: Evidence from oil-related regular news and extreme shocks," Energy Economics, Elsevier, volume 127, issue PB, DOI: 10.1016/j.eneco.2023.107121.
• Panarello, Demetrio & Gatto, Andrea, 2023, "Decarbonising Europe – EU citizens’ perception of renewable energy transition amidst the European Green Deal," Energy Policy, Elsevier, volume 172, issue C, DOI: 10.1016/j.enpol.2022.113272.
• Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023, "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, volume 262, issue PB, DOI: 10.1016/j.energy.2022.125589.
• Kuang, Wei, 2023, "The equity-oil hedge: A comparison between volatility and alternative risk frameworks," Energy, Elsevier, volume 271, issue C, DOI: 10.1016/j.energy.2023.127045.
• Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023, "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, volume 85, issue C, DOI: 10.1016/j.irfa.2022.102463.
• Zhang, Ning & Su, Xiaoman & Qi, Shuyuan, 2023, "An empirical investigation of multiperiod tail risk forecasting models," International Review of Financial Analysis, Elsevier, volume 86, issue C, DOI: 10.1016/j.irfa.2023.102498.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2023, "Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI," International Review of Financial Analysis, Elsevier, volume 87, issue C, DOI: 10.1016/j.irfa.2023.102558.
• Gao, Jun & Gao, Xiang & Gu, Chen, 2023, "Forecasting European stock volatility: The role of the UK," International Review of Financial Analysis, Elsevier, volume 89, issue C, DOI: 10.1016/j.irfa.2023.102728.
• Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023, "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, volume 89, issue C, DOI: 10.1016/j.irfa.2023.102770.
• Achakzai, Muhammad Atif Khan & Peng, Juan, 2023, "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, volume 89, issue C, DOI: 10.1016/j.irfa.2023.102827.
• Citterio, Alberto & King, Timothy, 2023, "The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress," Finance Research Letters, Elsevier, volume 51, issue C, DOI: 10.1016/j.frl.2022.103411.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2023, "Do travel uncertainty and invasion rhetoric spur Metaverse financial asset? – Gauging the role of media influence," Finance Research Letters, Elsevier, volume 51, issue C, DOI: 10.1016/j.frl.2022.103434.
• Yamani, Ehab, 2023, "The informational role of fund flow in the profitable predictability of mutual funds," Finance Research Letters, Elsevier, volume 51, issue C, DOI: 10.1016/j.frl.2022.103445.
• Díaz-Mendoza, Ana Carmen & Pardo, Ángel, 2023, "Water and traditional asset classes," Finance Research Letters, Elsevier, volume 52, issue C, DOI: 10.1016/j.frl.2022.103394.
• Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023, "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, volume 54, issue C, DOI: 10.1016/j.frl.2023.103727.
• Gupta, Rangan & Nel, Jacobus & Salisu, Afees A. & Ji, Qiang, 2023, "Predictability of economic slowdowns in advanced countries over eight centuries: The role of climate risks," Finance Research Letters, Elsevier, volume 54, issue C, DOI: 10.1016/j.frl.2023.103795.
• Kawakami, Tabito, 2023, "Quantile prediction for Bitcoin returns using financial assets’ realized measures," Finance Research Letters, Elsevier, volume 55, issue PA, DOI: 10.1016/j.frl.2023.103843.
• Gao, Shang & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2023, "Forecasting stock market volatility: The sum of the parts is more than the whole," Finance Research Letters, Elsevier, volume 55, issue PA, DOI: 10.1016/j.frl.2023.103849.
• Korkusuz, Burak & Kambouroudis, Dimos & McMillan, David G., 2023, "Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets," Finance Research Letters, Elsevier, volume 55, issue PB, DOI: 10.1016/j.frl.2023.103992.
• Hartkopf, Jan Patrick & Reh, Laura, 2023, "Challenging golden standards in EWMA smoothing parameter calibration based on realized covariance measures," Finance Research Letters, Elsevier, volume 56, issue C, DOI: 10.1016/j.frl.2023.104129.
• Zhang, Jiaming & Zou, Yang & Xiang, Yitian & Guo, Songlin, 2023, "Climate change and Japanese economic policy uncertainty: Asymmetric analysis," Finance Research Letters, Elsevier, volume 56, issue C, DOI: 10.1016/j.frl.2023.104165.
• Hou, Yunfei & Hu, Changsheng, 2023, "Understanding the role of aggregate analyst attention in resolving stock market uncertainty," Finance Research Letters, Elsevier, volume 57, issue C, DOI: 10.1016/j.frl.2023.104183.
• Xu, Yongan & Duong, Duy & Xu, Hualong, 2023, "Attention! Predicting crude oil prices from the perspective of extreme weather," Finance Research Letters, Elsevier, volume 57, issue C, DOI: 10.1016/j.frl.2023.104190.
• Jiang, Wei & Tang, Wanqing & Liu, Xiao, 2023, "Forecasting realized volatility of Chinese crude oil futures with a new secondary decomposition ensemble learning approach," Finance Research Letters, Elsevier, volume 57, issue C, DOI: 10.1016/j.frl.2023.104254.
• 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.