Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2021

• Abdullah, Muhammad & Gul, Zarro & Waseem, Faiza & Islam, Tanweer, 2021, "The State of Pakistan’s Economy and the Ineffectiveness of Monetary Policy," MPRA Paper, University Library of Munich, Germany, number 112678.
• Bradrania, Reza & Pirayesh Neghab, Davood, 2021, "State-dependent asset allocation using neural networks," MPRA Paper, University Library of Munich, Germany, number 115254, Feb.
• Fasano, Augusto & Rebaudo, Giovanni & Durante, Daniele & Petrone, Sonia, 2021, "A closed-form filter for binary time series," MPRA Paper, University Library of Munich, Germany, number 122349.
• Xin Sheng & Rangan Gupta & Afees A. Salisu & Elie Bouri, 2021, "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Working Papers, University of Pretoria, Department of Economics, number 202101, Jan.
• Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar, 2021, "Uncertainty and Predictability of Real Housing Returns in the United Kingdom: A Regional Analysis," Working Papers, University of Pretoria, Department of Economics, number 202102, Jan.
• Afees A. Salisu & Umar Bida Ndako & Rangan Gupta, 2021, "Forecasting US Output Growth with Large Information Sets," Working Papers, University of Pretoria, Department of Economics, number 202103, Jan.
• Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021, "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers, University of Pretoria, Department of Economics, number 202111, Feb.
• Riza Demirer & Rangan Gupta & He Li & Yu You, 2021, "Financial Vulnerability and Volatility in Emerging Stock Markets: Evidence from GARCH-MIDAS Models," Working Papers, University of Pretoria, Department of Economics, number 202112, Feb.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021, "Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis," Working Papers, University of Pretoria, Department of Economics, number 202114, Feb.
• Afees A. Salisu & Rangan Gupta & Qiang Ji, 2021, "Forecasting Oil Price over 150 Years: The Role of Tail Risks," Working Papers, University of Pretoria, Department of Economics, number 202120, Mar.
• Afees A. Salisu & Rangan Gupta & Riza Demirer, 2021, "Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model," Working Papers, University of Pretoria, Department of Economics, number 202121, Mar.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta, 2021, "Geopolitical Risk and Forecastability of Tail Risk in the Oil Market: Evidence from Over a Century of Monthly Data," Working Papers, University of Pretoria, Department of Economics, number 202122, Mar.
• Afees A. Salisu & Rangan Gupta & Christian Pierdzioch, 2021, "Predictability of Tail Risks of Canada and the U.S. Over a Century: The Role of Spillovers and Oil Tail Risks," Working Papers, University of Pretoria, Department of Economics, number 202127, Apr.
• Afees A. Salisu & Rangan Gupta & Sayar Karmakar & Sonali Das, 2021, "Forecasting Output Growth of Advanced Economies Over Eight Centuries: The Role of Gold Market Volatility as a Proxy of Global Uncertainty," Working Papers, University of Pretoria, Department of Economics, number 202133, May.
• Rangan Gupta & Christian Pierdzioch, 2021, "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers," Working Papers, University of Pretoria, Department of Economics, number 202135, May.
• Rangan Gupta & Christian Pierdzioch, 2021, "Uncertainty, Spillovers, and Forecasts of the Realized Variance of Gold Returns," Working Papers, University of Pretoria, Department of Economics, number 202137, May.
• Afees A. Salisu & Rangan Gupta, 2021, "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers, University of Pretoria, Department of Economics, number 202144, Jun.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta, 2021, "Oil Tail Risks and the Forecastability of the Realized Variance of Oil-Price: Evidence from Over 150 Years of Data," Working Papers, University of Pretoria, Department of Economics, number 202146, Jun.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & David Gabauer, 2021, "Forecasting Stock-Market Tail Risk and Connectedness in Advanced Economies Over a Century: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios," Working Papers, University of Pretoria, Department of Economics, number 202161, Sep.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & Renee van Eyden, 2021, "Climate Risks and U.S. Stock-Market Tail Risks: A Forecasting Experiment Using over a Century of Data," Working Papers, University of Pretoria, Department of Economics, number 202165, Sep.
• Sayar Karmakar & Riza Demirer & Rangan Gupta, 2021, "Bitcoin Mining Activity and Volatility Dynamics in the Power Market," Working Papers, University of Pretoria, Department of Economics, number 202166, Sep.
• Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risks and Forecastability of the Realized Volatility of Gold and Other Metal Prices," Working Papers, University of Pretoria, Department of Economics, number 202172, Oct.
• Jiqian Wang & Rangan Gupta & Oguzhan Cepni & Feng Ma, 2021, "Forecasting International REITs Volatility: The Role of Oil-Price Uncertainty," Working Papers, University of Pretoria, Department of Economics, number 202173, Oct.
• Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Working Papers, University of Pretoria, Department of Economics, number 202175, Oct.
• Rangan Gupta & Christian Pierdzioch, 2021, "Forecasting the Realized Variance of Oil-Price Returns: A Disaggregated Analysis of the Role of Uncertainty and Geopolitical Risk," Working Papers, University of Pretoria, Department of Economics, number 202176, Oct.
• Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risk and the Volatility of Agricultural Commodity Price Fluctuations: A Forecasting Experiment," Working Papers, University of Pretoria, Department of Economics, number 202177, Nov.
• Ruipeng Liu & Mawuli Segnon & Rangan Gupta & Elie Bouri, 2021, "Conventional and Unconventional Monetary Policy Rate Uncertainty and Stock Market Volatility: A Forecasting Perspective," Working Papers, University of Pretoria, Department of Economics, number 202178, Nov.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021, "El Nino, La Nina, and Forecastability of the Realized Variance of Agricultural Commodity Prices: Evidence from a Machine Learning Approach," Working Papers, University of Pretoria, Department of Economics, number 202179, Nov.
• Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risks and Forecasting Stock-Market Returns in Advanced Economies Over a Century," Working Papers, University of Pretoria, Department of Economics, number 202183, Nov.
• Paulina Ziembińska, 2021, "Quality of Tests of Expectation Formation for Revised Data," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, volume 13, issue 4, pages 405-453, December.
• Bhumjai Tangsawasdirat & Suranan Tanpoonkiat & Burasakorn Tangsatchanan, 2021, "Credit Risk Database: Credit Scoring Models for Thai SMEs," PIER Discussion Papers, Puey Ungphakorn Institute for Economic Research, number 168, Nov.
• Eliud Silva & Corey Sparks, 2021, "Hierarchical forecasts of Diabetes mortality in Mexico by marginalization and sex to establish resource allocation," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., volume 18, issue 2, pages 82-98, Julio-Dic.
• Luke Hartigan & Michelle Wright, 2021, "Financial Conditions and Downside Risk to Economic Activity in Australia," RBA Research Discussion Papers, Reserve Bank of Australia, number rdp2021-03, Mar, DOI: 10.47688/rdp2021-03.
• Pérez Forero, Fernando, 2021, "Predicción de variables macroeconómicas en el Perú a través un modelo BVAR con media cambiante en el tiempo," Working Papers, Banco Central de Reserva del Perú, number 2021-001, Mar.
• Gazi Salah Uddin & Ou Tang & Maziar Sahamkhadam & Farhad Taghizadeh-Hesary & Muhammad Yahya & Pontus Cerin & Jakob Rehme, 2021, "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers, Asian Development Bank Institute, number 1212, Jan.
• Karen Poghosyan & Ruben Poghosyan, 2021, "On the applicability of dynamic factor models for forecasting real GDP growth in Armenia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 61, pages 28-46.
• Robert Garafutdinov, 2021, "Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 62, pages 85-100.
• Nikita Fokin, 2021, "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 63, pages 5-29.
• Necati Alp ERİLLİ, 2021, "Use of Trimean in Theil-Sen Regression Analysis," Bulletin of Economic Theory and Analysis, BETA Journals, volume 6, issue 1, pages 15-26.
• Necmettin Alpay Kocak, 2021, "Analysis of Relationship between the Consumer and Producer Prices in Turkey using Alternative Estimation Methods (Türkiye'de Tüketici ve Üretici Fiyatları Arasındaki İlişkinin Alternatif Tahmin Yöntem," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 12, issue 1, pages 33-47.
• Kaveh A. Adli & Ugur Sener, 2021, "Forecasting of the U.S. Steel Prices with LVAR and VEC Models," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 12, issue 3, pages 509-522.
• Juan de Lucio, 2021, "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
• Miguel Ángel Mendoza-González, 2021, "Apertura comercial, choques productivos y externalidades con ciclos espacio-tiempo en el crecimiento económico por entidad federativa en México, 1980-2018," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 50, pages 105-124.
• Sailesh Bhaghoe & Gavin Ooft, 2021, "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise, number 176, Mar.
• Anitha Rao & Mark Wiendling & Paul Ridgeway & Liz Kennedy & Harris A. Eyre & Paulo Pinho, 2021, "Bridging the Gap Between Medicine and Insurance: How to Leverage Data, Artificial Intelligence, and Neuroinformatics for Insurance and Financial Risk Management," Journal of Financial Transformation, Capco Institute, volume 54, pages 142-147.
• Sung Wook Hong & Seong-hwan Min, 2021, "Market Analysis of Key Manufacturing Segments Using News Data," Research Papers, Korea Institute for Industrial Economics and Trade, number 21/8, May.
• Solmaz Sadeghpour & Hassan Heidari & Seyyed Jamaleddin Mohseni Zenozi, 2021, "Study the effect of the monetary and financial shocks in the real sector of Iran's economy with considering of gharz-al -hasanah deposits in the context of a DSGE model," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, volume 8, issue 1, pages 89-114.
• Hwee Kwan Chow & Daniel Han, 2021, "Forecast Pooling or Information Pooling During Crises? MIDAS Forecasting of GDP in a Small Open Economy," Economics and Statistics Working Papers, Singapore Management University, School of Economics, number 6-2021, Jul.
• Weiwei ZHANG & Tiezhu SUN & Yechi MA & Zilong WANG, 2021, "New Evidence on the Information Content of Implied Volatility of S&P 500: Model-Free versus Model-Based," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 1, pages 109-121, December.
• Fatemeh SALIMIANRAD & Vali BORIMNEJAD & Sahar DEHYUORI, 2021, "Investigating the Relationship between Natural Capital and Sustainable Economic Growth using the General Equilibrium Model," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 3, pages 120-139, June.
• Kirill Dragun & Kris Boudt & Orimar Sauri & Steven Vanduffel, 2021, "Beta-Adjusted Covariance Estimation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium, Ghent University, Faculty of Economics and Business Administration, number 21/1010, Feb.
• George Varghese & Vinodh Madhavan, 2021, "Nonlinearity in Global Crude Oil Benchmarks: Disentangling the Effect of Time Aggregation," Journal of Emerging Market Finance, Institute for Financial Management and Research, volume 20, issue 3, pages 290-307, December, DOI: 10.1177/09726527211043013.
• Tomas Havranek & Ayaz Zeynalov, 2021, "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , volume 27, issue 1, pages 129-148, February, DOI: 10.1177/1354816619879584.
• Reyes Zárate, Francisco J & León López, Iván, 2021, "Estimaciones de riesgo ajustadas por distribución: una aplicación para portafolios de inversión integrados por activos nacionales / Distribution-Adjusted Risk Estimates: An Application to Domestic Assets Investment Portfolios," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, volume 11, issue 2, pages 117-146, julio-dic.
• Wojciech Charemza & Michał Lewandowski & Łukasz Woźny, 2021, "Efficiency in rewarding academic journal publications. The case of Poland," KAE Working Papers, Warsaw School of Economics, Collegium of Economic Analysis, number 2021-062, Feb, DOI: 10.33119/kaewps2021062.
• Alejandro Rodriguez Arana, 2021, "La expansion de Covid-19 en Mexico en 2020: un enfoque desde la econometria de series de tiempo," Sobre México. Revista de Economía, Sobre México. Temas en economía, volume 1, issue 3, pages 34-66.
• F. Benedetto & L. Mastroeni & P. Vellucci, 2021, "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, volume 299, issue 1, pages 1235-1252, April, DOI: 10.1007/s10479-019-03319-7.
• Chrysovalantis Gaganis & Panagiota Papadimitri & Menelaos Tasiou, 2021, "A multicriteria decision support tool for modelling bank credit ratings," Annals of Operations Research, Springer, volume 306, issue 1, pages 27-56, November, DOI: 10.1007/s10479-020-03516-9.
• Eric Séverin & David Veganzones, 2021, "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, volume 306, issue 1, pages 247-272, November, DOI: 10.1007/s10479-021-04183-0.
• Yuzhi Cai & Thanaset Chevapatrakul & Danilo V. Mascia, 2021, "How is price explosivity triggered in the cryptocurrency markets?," Annals of Operations Research, Springer, volume 307, issue 1, pages 37-51, December, DOI: 10.1007/s10479-021-04298-4.
• Christos Agiakloglou & Apostolos Tsimpanos, 2021, "Evaluating information criteria for selecting spatial processes," The Annals of Regional Science, Springer;Western Regional Science Association, volume 66, issue 3, pages 677-697, June, DOI: 10.1007/s00168-020-01033-y.
• David Volkmann, 2021, "Explaining S&P500 option returns: an implied risk-adjusted approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, volume 29, issue 2, pages 665-685, June, DOI: 10.1007/s10100-019-00666-5.
• Michal Mešťan & Ivan Králik & Matej Žofaj & Nikola Karkošiaková & Audrius Kabašinskas, 2021, "Projections of pension benefits in supplementary pension saving scheme in Slovakia," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, volume 29, issue 2, pages 687-712, June, DOI: 10.1007/s10100-019-00669-2.
• Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021, "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 44, issue 2, pages 863-882, December, DOI: 10.1007/s10203-021-00318-x.
• Paolo Angelis & Roberto Marchis & Mario Marino & Antonio Luciano Martire & Immacolata Oliva, 2021, "Betting on bitcoin: a profitable trading between directional and shielding strategies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 44, issue 2, pages 883-903, December, DOI: 10.1007/s10203-021-00324-z.
• Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021, "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 44, issue 2, pages 1063-1085, December, DOI: 10.1007/s10203-021-00354-7.
• Marcel Aloy & Floris Laly & Sébastien Laurent & Christelle Lecourt, 2021, "Modeling Time-Varying Conditional Betas. A Comparison of Methods with Application for REITs," Dynamic Modeling and Econometrics in Economics and Finance, Springer, in: Gilles Dufrénot & Takashi Matsuki, "Recent Econometric Techniques for Macroeconomic and Financial Data", DOI: 10.1007/978-3-030-54252-8_9.
• Alain Kabundi & Asithandile Mbelu, 2021, "Estimating a time-varying financial conditions index for South Africa," Empirical Economics, Springer, volume 60, issue 4, pages 1817-1844, April, DOI: 10.1007/s00181-020-01844-0.
• Yongchen Zhao, 2021, "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, volume 61, issue 1, pages 173-199, July, DOI: 10.1007/s00181-020-01864-w.
• Jack Fosten & Daniel Gutknecht, 2021, "Horizon confidence sets," Empirical Economics, Springer, volume 61, issue 2, pages 667-692, August, DOI: 10.1007/s00181-020-01891-7.
• Serdar Neslihanoglu & Stelios Bekiros & John McColl & Duncan Lee, 2021, "Multivariate time-varying parameter modelling for stock markets," Empirical Economics, Springer, volume 61, issue 2, pages 947-972, August, DOI: 10.1007/s00181-020-01896-2.
• Georges Tsafack & James Cataldo, 2021, "Backtesting and estimation error: value-at-risk overviolation rate," Empirical Economics, Springer, volume 61, issue 3, pages 1351-1396, September, DOI: 10.1007/s00181-020-01905-4.
• Edmond Berisha & David Gabauer & Rangan Gupta & Chi Keung Marco Lau, 2021, "Time-varying influence of household debt on inequality in United Kingdom," Empirical Economics, Springer, volume 61, issue 4, pages 1917-1933, October, DOI: 10.1007/s00181-020-01940-1.
• Angela Capolongo & Claudia Pacella, 2021, "Forecasting inflation in the euro area: countries matter!," Empirical Economics, Springer, volume 61, issue 5, pages 2477-2499, November, DOI: 10.1007/s00181-020-01959-4.
• Nima Nonejad, 2021, "Crude oil price point forecasts of the Norwegian GDP growth rate," Empirical Economics, Springer, volume 61, issue 5, pages 2913-2930, November, DOI: 10.1007/s00181-020-01964-7.
• Ahmet Akca & Ethem Çanakoğlu, 2021, "Adaptive stochastic risk estimation of firm operating profit," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, volume 48, issue 3, pages 463-504, September, DOI: 10.1007/s40812-021-00184-z.
• David Y. Aharon & Zaghum Umar & Xuan Vinh Vo, 2021, "Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 7, issue 1, pages 1-25, December, DOI: 10.1186/s40854-021-00274-w.
• Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021, "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, volume 60, issue C, pages 56-73, DOI: 10.1016/j.jempfin.2020.11.003.
• Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021, "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, volume 62, issue C, pages 179-201, DOI: 10.1016/j.jempfin.2021.03.003.
• Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021, "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, volume 62, issue C, pages 46-61, DOI: 10.1016/j.jempfin.2021.01.007.
• Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021, "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, volume 63, issue C, pages 252-269, DOI: 10.1016/j.jempfin.2021.07.009.
• Han, Yang & Jiao, Anqi & Ma, Jun, 2021, "The predictive power of Nelson–Siegel factor loadings for the real economy," Journal of Empirical Finance, Elsevier, volume 64, issue C, pages 95-127, DOI: 10.1016/j.jempfin.2021.04.008.
• Algieri, Bernardina & Leccadito, Arturo & Tunaru, Diana, 2021, "Risk premia in electricity derivatives markets," Energy Economics, Elsevier, volume 100, issue C, DOI: 10.1016/j.eneco.2021.105300.
• Lyócsa, Štefan & Todorova, Neda, 2021, "What drives volatility of the U.S. oil and gas firms?," Energy Economics, Elsevier, volume 100, issue C, DOI: 10.1016/j.eneco.2021.105367.
• Patra, Saswat, 2021, "Revisiting value-at-risk and expected shortfall in oil markets under structural breaks: The role of fat-tailed distributions," Energy Economics, Elsevier, volume 101, issue C, DOI: 10.1016/j.eneco.2021.105452.
• Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021, "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, volume 102, issue C, DOI: 10.1016/j.eneco.2021.105494.
• Özen, Kadir & Yıldırım, Dilem, 2021, "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, volume 103, issue C, DOI: 10.1016/j.eneco.2021.105573.
• Nonejad, Nima, 2021, "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, volume 104, issue C, DOI: 10.1016/j.eneco.2021.105635.
• Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021, "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, volume 104, issue C, DOI: 10.1016/j.eneco.2021.105689.
• Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021, "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, volume 93, issue C, DOI: 10.1016/j.eneco.2019.104481.
• Dai, Zhifeng & Zhou, Huiting & Kang, Jie & Wen, Fenghua, 2021, "The skewness of oil price returns and equity premium predictability," Energy Economics, Elsevier, volume 94, issue C, DOI: 10.1016/j.eneco.2020.105069.
• Uniejewski, Bartosz & Weron, Rafał, 2021, "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, volume 95, issue C, DOI: 10.1016/j.eneco.2021.105121.
• Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021, "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, volume 96, issue C, DOI: 10.1016/j.eneco.2021.105118.
• He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021, "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, volume 97, issue C, DOI: 10.1016/j.eneco.2021.105189.
• Dai, Zhifeng & Kang, Jie, 2021, "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, volume 97, issue C, DOI: 10.1016/j.eneco.2021.105205.
• Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan, 2021, "Geopolitical risk and forecastability of tail risk in the oil market: Evidence from over a century of monthly data," Energy, Elsevier, volume 235, issue C, DOI: 10.1016/j.energy.2021.121333.
• Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021, "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, volume 78, issue C, DOI: 10.1016/j.irfa.2021.101899.
• Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2021, "VCRIX — A volatility index for crypto-currencies," International Review of Financial Analysis, Elsevier, volume 78, issue C, DOI: 10.1016/j.irfa.2021.101915.
• Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021, "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, volume 38, issue C, DOI: 10.1016/j.frl.2020.101490.
• Chen, Wen & Minney, Aaron & Toscas, Peter & Koo, Bonsoo & Zhu, Zili & Pantelous, Athanasios A., 2021, "Personalised drawdown strategies and partial annuitisation to mitigate longevity risk," Finance Research Letters, Elsevier, volume 39, issue C, DOI: 10.1016/j.frl.2020.101644.
• Nonejad, Nima, 2021, "Predicting equity premium by conditioning on macroeconomic variables: A prediction selection strategy using the price of crude oil," Finance Research Letters, Elsevier, volume 41, issue C, DOI: 10.1016/j.frl.2020.101792.
• Ly, Kim Tien, 2021, "A COVID-19 forecasting system using adaptive neuro-fuzzy inference," Finance Research Letters, Elsevier, volume 41, issue C, DOI: 10.1016/j.frl.2020.101844.
• Chatziantoniou, Ioannis & Degiannakis, Stavros & Delis, Panagiotis & Filis, George, 2021, "Forecasting oil price volatility using spillover effects from uncertainty indices," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2020.101885.
• Bouri, Elie & Gupta, Rangan & Majumdar, Anandamayee & Subramaniam, Sowmya, 2021, "Time-varying risk aversion and forecastability of the US term structure of interest rates," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2021.101924.
• Bouri, Elie & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021, "Forecasting power of infectious diseases-related uncertainty for gold realized variance," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2021.101936.
• Guidolin, Massimo & Pedio, Manuela, 2021, "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2021.101943.
• Umar, Zaghum & Riaz, Yasir & Zaremba, Adam, 2021, "Patterns of Spillover in Energy, Agricultural, and Metal Markets: A Connectedness Analysis for Years 1780-2020," Finance Research Letters, Elsevier, volume 43, issue C, DOI: 10.1016/j.frl.2021.101999.
• Hoang, Khoa & Cannavan, Damien & Huang, Ronghong & Peng, Xiaowen, 2021, "Predicting stock returns with implied cost of capital: A partial least squares approach," Journal of Financial Markets, Elsevier, volume 53, issue C, DOI: 10.1016/j.finmar.2020.100576.
• Knill, April M. & Lee, Bong Soo & Ang, James, 2021, "Leveling of the playing field and corporate financing patterns around the world," Global Finance Journal, Elsevier, volume 47, issue C, DOI: 10.1016/j.gfj.2020.100515.
• Battiston, Pietro & Gamba, Simona, 2021, "COVID-19: R0 is lower where outbreak is larger," Health Policy, Elsevier, volume 125, issue 2, pages 141-147, DOI: 10.1016/j.healthpol.2020.10.017.
• Buonanno, Paolo & Puca, Marcello, 2021, "Using newspaper obituaries to “nowcast” daily mortality: Evidence from the Italian COVID-19 hot-spots," Health Policy, Elsevier, volume 125, issue 4, pages 535-540, DOI: 10.1016/j.healthpol.2021.01.006.
• Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021, "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, volume 100, issue C, pages 296-308, DOI: 10.1016/j.insmatheco.2021.06.004.
• Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021, "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, volume 100, issue C, pages 30-58, DOI: 10.1016/j.insmatheco.2021.04.007.
• Taylor, Greg, 2021, "A special Tweedie sub-family with application to loss reserving prediction error," Insurance: Mathematics and Economics, Elsevier, volume 101, issue PB, pages 262-288, DOI: 10.1016/j.insmatheco.2021.08.002.
• Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021, "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, volume 96, issue C, pages 31-52, DOI: 10.1016/j.insmatheco.2020.10.003.
• Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021, "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, volume 99, issue C, pages 200-221, DOI: 10.1016/j.insmatheco.2021.03.025.
• Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021, "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, volume 99, issue C, pages 9-24, DOI: 10.1016/j.insmatheco.2021.01.002.
• Urom, Christian & Ndubuisi, Gideon & Ozor, Jude, 2021, "Economic activity, and financial and commodity markets’ shocks: An analysis of implied volatility indexes," International Economics, Elsevier, volume 165, issue C, pages 51-66, DOI: 10.1016/j.inteco.2020.11.005.
• Bessler, Wolfgang & Taushanov, Georgi & Wolff, Dominik, 2021, "Optimal asset allocation strategies for international equity portfolios: A comparison of country versus industry optimization," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 72, issue C, DOI: 10.1016/j.intfin.2021.101343.
• Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021, "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 73, issue C, DOI: 10.1016/j.intfin.2021.101353.
• Breen, John David & Hu, Liang, 2021, "The predictive content of oil price and volatility: New evidence on exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 75, issue C, DOI: 10.1016/j.intfin.2021.101454.
• Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021, "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, volume 37, issue 1, pages 360-377, DOI: 10.1016/j.ijforecast.2020.06.006.
• Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo, 2021, "Realized volatility forecasting: Robustness to measurement errors," International Journal of Forecasting, Elsevier, volume 37, issue 1, pages 44-57, DOI: 10.1016/j.ijforecast.2020.02.009.
• Costantini, Mauro & Kunst, Robert M., 2021, "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 445-460, DOI: 10.1016/j.ijforecast.2020.06.010.
• Clements, Michael P., 2021, "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 634-646, DOI: 10.1016/j.ijforecast.2020.08.003.
• Greenwood-Nimmo, Matthew & Nguyen, Viet Hoang & Shin, Yongcheol, 2021, "Measuring the Connectedness of the Global Economy," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 899-919, DOI: 10.1016/j.ijforecast.2020.10.003.
• Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021, "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 941-948, DOI: 10.1016/j.ijforecast.2020.10.005.
• Ganics, Gergely & Odendahl, Florens, 2021, "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 971-999, DOI: 10.1016/j.ijforecast.2020.11.001.
• Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021, "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, volume 37, issue 3, pages 1247-1260, DOI: 10.1016/j.ijforecast.2021.02.007.
• Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas, 2021, "Predicting benchmarked US state employment data in real time," International Journal of Forecasting, Elsevier, volume 37, issue 3, pages 1261-1275, DOI: 10.1016/j.ijforecast.2021.02.006.
• Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021, "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, volume 37, issue 4, pages 1338-1354, DOI: 10.1016/j.ijforecast.2021.05.005.
• Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021, "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, volume 37, issue 4, pages 1376-1398, DOI: 10.1016/j.ijforecast.2021.04.003.
• Blasques, Francisco & Hoogerkamp, Meindert Heres & Koopman, Siem Jan & van de Werve, Ilka, 2021, "Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data," International Journal of Forecasting, Elsevier, volume 37, issue 4, pages 1426-1441, DOI: 10.1016/j.ijforecast.2021.01.026.
• Sucarrat, Genaro, 2021, "Identification of volatility proxies as expectations of squared financial returns," International Journal of Forecasting, Elsevier, volume 37, issue 4, pages 1677-1690, DOI: 10.1016/j.ijforecast.2021.03.008.
• Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021, "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, volume 57, issue C, DOI: 10.1016/j.japwor.2021.101056.
• Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021, "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, volume 125, issue C, DOI: 10.1016/j.jbankfin.2021.106046.
• Sopitpongstorn, Nithi & Silvapulle, Param & Gao, Jiti & Fenech, Jean-Pierre, 2021, "Local logit regression for loan recovery rate," Journal of Banking & Finance, Elsevier, volume 126, issue C, DOI: 10.1016/j.jbankfin.2021.106093.
• Clements, Adam & Preve, Daniel P.A., 2021, "A Practical Guide to harnessing the HAR volatility model," Journal of Banking & Finance, Elsevier, volume 133, issue C, DOI: 10.1016/j.jbankfin.2021.106285.
• Jang, Tae-Seok & Sacht, Stephen, 2021, "Forecast heuristics, consumer expectations, and New-Keynesian macroeconomics: A Horse race," Journal of Economic Behavior & Organization, Elsevier, volume 182, issue C, pages 493-511, DOI: 10.1016/j.jebo.2019.01.017.
• Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021, "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, volume 183, issue C, pages 681-699, DOI: 10.1016/j.jebo.2021.01.014.
• Noussair, Charles N. & Popescu, Andreea Victoria, 2021, "Comovement and return predictability in asset markets: An experiment with two Lucas trees," Journal of Economic Behavior & Organization, Elsevier, volume 185, issue C, pages 671-687, DOI: 10.1016/j.jebo.2021.03.012.
• Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021, "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, volume 187, issue C, pages 290-314, DOI: 10.1016/j.jebo.2021.04.037.
• Huisman, Ronald & Van der Sar, Nico L. & Zwinkels, Remco C.J., 2021, "Volatility expectations and disagreement," Journal of Economic Behavior & Organization, Elsevier, volume 188, issue C, pages 379-393, DOI: 10.1016/j.jebo.2021.05.020.
• Makarewicz, Tomasz, 2021, "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, volume 190, issue C, pages 626-673, DOI: 10.1016/j.jebo.2021.07.008.
• Al Mabsali, Yousuf Khamis & Hayward, Robert & Eliwa, Yasser, 2021, "Managerial tools used to meet or beat analyst forecasts: Evidence from the UK," Journal of International Accounting, Auditing and Taxation, Elsevier, volume 43, issue C, DOI: 10.1016/j.intaccaudtax.2021.100383.
• Fernald, John G. & Hsu, Eric & Spiegel, Mark M., 2021, "Is China fudging its GDP figures? Evidence from trading partner data," Journal of International Money and Finance, Elsevier, volume 110, issue C, DOI: 10.1016/j.jimonfin.2020.102262.
• Fernald, John G. & Hsu, Eric & Spiegel, Mark M., 2021, "Reprint: Is China fudging its GDP figures? Evidence from trading partner data," Journal of International Money and Finance, Elsevier, volume 114, issue C, DOI: 10.1016/j.jimonfin.2021.102406.
• Biolsi, Christopher, 2021, "Labor productivity forecasts based on a Beveridge–Nelson filter: Is there statistical evidence for a slowdown?," Journal of Macroeconomics, Elsevier, volume 69, issue C, DOI: 10.1016/j.jmacro.2021.103321.
• Pozo, Veronica F. & Bachmeier, Lance J. & Schroeder, Ted C., 2021, "Are there price asymmetries in the U.S. beef market?," Journal of Commodity Markets, Elsevier, volume 21, issue C, DOI: 10.1016/j.jcomm.2020.100127.
• Nonejad, Nima, 2021, "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, volume 23, issue C, DOI: 10.1016/j.jcomm.2021.100167.
• Wolters, Jannik & Huchzermeier, Arnd, 2021, "Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment," Journal of Retailing, Elsevier, volume 97, issue 4, pages 726-745, DOI: 10.1016/j.jretai.2021.01.003.
• Mishra, Shekhar & Mishra, Sibanjan, 2021, "Are Indian sectoral indices oil shock prone? An empirical evaluation," Resources Policy, Elsevier, volume 70, issue C, DOI: 10.1016/j.resourpol.2020.101889.
• Pincheira, Pablo & Hardy, Nicolás, 2021, "Forecasting aluminum prices with commodity currencies," Resources Policy, Elsevier, volume 73, issue C, DOI: 10.1016/j.resourpol.2021.102066.
• Dai, Zhifeng & Kang, Jie & Hu, Yangli, 2021, "Efficient predictability of oil price: The role of number of IPOs and U.S. dollar index," Resources Policy, Elsevier, volume 74, issue C, DOI: 10.1016/j.resourpol.2021.102297.
• Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021, "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, volume 74, issue C, DOI: 10.1016/j.resourpol.2021.102319.
• Rojas-Bernal, Alejandro & Villamizar-Villegas, Mauricio, 2021, "Pricing the exotic: Path-dependent American options with stochastic barriers," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 2, issue 1, DOI: 10.1016/j.latcb.2021.100025.
• Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021, "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, volume 117, issue C, pages 507-520, DOI: 10.1016/j.jmoneco.2020.03.004.
• Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021, "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, volume 117, issue C, pages 798-815, DOI: 10.1016/j.jmoneco.2020.06.001.
• Brownlees, Christian & Souza, André B.M., 2021, "Backtesting global Growth-at-Risk," Journal of Monetary Economics, Elsevier, volume 118, issue C, pages 312-330, DOI: 10.1016/j.jmoneco.2020.11.003.
• Tang, Wenjin & Ding, Saijie & Chen, Hao, 2021, "Economic uncertainty and its spillover networks: Evidence from the Asia-Pacific countries," Pacific-Basin Finance Journal, Elsevier, volume 67, issue C, DOI: 10.1016/j.pacfin.2021.101539.
• Umar, Zaghum & Yousaf, Imran & Aharon, David Y., 2021, "The relationship between yield curve components and equity sectorial indices: Evidence from China," Pacific-Basin Finance Journal, Elsevier, volume 68, issue C, DOI: 10.1016/j.pacfin.2021.101591.
• Pamp, Oliver & Lebacher, Michael & Thurner, Paul W. & Ziegler, Eva, 2021, "Explaining destinations and volumes of international arms transfers: A novel network Heckman selection model," European Journal of Political Economy, Elsevier, volume 69, issue C, DOI: 10.1016/j.ejpoleco.2021.102033.
• Jiang, Minqi & Liu, Jiapeng & Zhang, Lu, 2021, "An extended regularized Kalman filter based on Genetic Algorithm: Application to dynamic asset pricing models," The Quarterly Review of Economics and Finance, Elsevier, volume 79, issue C, pages 28-44, DOI: 10.1016/j.qref.2020.12.005.
• Fassas, Athanasios P. & Siriopoulos, Costas, 2021, "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, volume 79, issue C, pages 303-329, DOI: 10.1016/j.qref.2020.07.004.
• Borgards, Oliver & Czudaj, Robert L., 2021, "Features of overreactions in the cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, volume 80, issue C, pages 31-48, DOI: 10.1016/j.qref.2021.01.010.
• Chaikumbung, Mayula, 2021, "Institutions and consumer preferences for renewable energy: A meta-regression analysis," Renewable and Sustainable Energy Reviews, Elsevier, volume 146, issue C, DOI: 10.1016/j.rser.2021.111143.
• Andreana, Gianmarco & Gualini, Andrea & Martini, Gianmaria & Porta, Flavio & Scotti, Davide, 2021, "The disruptive impact of COVID-19 on air transportation: An ITS econometric analysis," Research in Transportation Economics, Elsevier, volume 90, issue C, DOI: 10.1016/j.retrec.2021.101042.
• Plíhal, Tomáš & Lyócsa, Štefan, 2021, "Modeling realized volatility of the EUR/USD exchange rate: Does implied volatility really matter?," International Review of Economics & Finance, Elsevier, volume 71, issue C, pages 811-829, DOI: 10.1016/j.iref.2020.10.001.
• Chen, Shun & Ge, Lei, 2021, "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, volume 71, issue C, pages 936-942, DOI: 10.1016/j.iref.2020.07.010.
• Zhou, Xuemei & Liu, Qiang & Guo, Shuxin, 2021, "Do overnight returns explain firm-specific investor sentiment in China?," International Review of Economics & Finance, Elsevier, volume 76, issue C, pages 451-477, DOI: 10.1016/j.iref.2021.06.003.
• Bloise, Francesco & Tancioni, Massimiliano, 2021, "Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?," Structural Change and Economic Dynamics, Elsevier, volume 56, issue C, pages 310-329, DOI: 10.1016/j.strueco.2021.01.001.
• Balcilar, Mehmet & Berisha, Edmond & Gupta, Rangan & Pierdzioch, Christian, 2021, "Time-varying evidence of predictability of financial stress in the United States over a century: The role of inequality," Structural Change and Economic Dynamics, Elsevier, volume 57, issue C, pages 87-92, DOI: 10.1016/j.strueco.2021.02.002.
• Akaev, Askar & Devezas, Tessaleno & Ichkitidze, Yuri & Sarygulov, Askar, 2021, "Forecasting the labor intensity and labor income share for G7 countries in the digital age," Technological Forecasting and Social Change, Elsevier, volume 167, issue C, DOI: 10.1016/j.techfore.2021.120675.
• Chen, Ji & Huang, Jiayan & Su, Weihua & Štreimikienė, Dalia & Baležentis, Tomas, 2021, "The challenges of COVID-19 control policies for sustainable development of business: Evidence from service industries," Technology in Society, Elsevier, volume 66, issue C, DOI: 10.1016/j.techsoc.2021.101643.
• Gutiérrez-Romero, Roxana & Ahamed, Mostak, 2021, "COVID-19 response needs to broaden financial inclusion to curb the rise in poverty," World Development, Elsevier, volume 138, issue C, DOI: 10.1016/j.worlddev.2020.105229.
• K. Peren Arin & Kevin Devereux & Mieszko Mazur, 2021, "Taxes and firm investment," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-08, Jan.
• Hilde C. Bjørnland & Roberto Casarin & Marco Lorusso & Francesco Ravazzolo, 2021, "Oil and fiscal policy regimes," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-10, Jan.
• Patrycja Klusak & Matthew Agarwala & Matt Burke & Moritz Kraemer & Kamiar Mohaddes, 2021, "Rising temperatures, falling ratings: The effect of climate change on sovereign creditworthiness," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-34, Mar.
• Roshen Fernando & Weifeng Liu & Warwick J McKibbin, 2021, "Global economic impacts of climate shocks, climate policy and changes in climate risk assessment," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-37, Apr.
• Mototsugu Shintani & Kozo Ueda, 2021, "Identifying the source of information rigidities in the expectations formation process," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-48, Jun.
• Laura Coroneo & Fabrizio Iacone & Alessia Paccagnini & Paulo Santos Monteiro, 2021, "Testing the predictive accuracy of COVID-19 forecasts," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-52, Jul.
• Hanol Lee & Jong-Wha Lee, 2021, "Why East Asian students perform better in mathematics than their peers: An investigation using a machine learning approach," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-66, Jul.
• Florence Jaumotte & Weifeng Liu & Warwick J. McKibbin, 2021, "Mitigating climate change: Growth-friendly policies to achieve net zero emissions by 2050," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-75, Aug.
• Tino Berger & Paul David Boll & James Morley & Benjamin Wong, 2021, "Cyclical signals from the labor market," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-91, Oct.
• Daniel Goller & Michael C. Knaus & Michael Lechner & Gabriel Okasa, 2021, "Predicting match outcomes in football by an Ordered Forest estimator," Chapters, Edward Elgar Publishing, chapter 22, in: Ruud H. Koning & Stefan Kesenne, "A Modern Guide to Sports Economics".
• Taniya Ghosh & Sakshi Agarwal, 2021, "Do Machine Learning Models Hold the Key to Better Money Demand Forecasting?," International Symposia in Economic Theory and Econometrics, Emerald Group Publishing Limited, "Environmental, Social, and Governance Perspectives on Economic Development in Asia", DOI: 10.1108/S1571-03862021000029A017.
• Ramona Serrano Bautista & José Antonio Núñez Mora, 2021, "Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, volume 26, issue 52, pages 197-221, November, DOI: 10.1108/JEFAS-03-2021-0009.
• Knut Lehre Seip & Dan Zhang, 2021, "The GDP, the US treasury yield and the federal funds rate: who follows whom, when and why?," Journal of Financial Economic Policy, Emerald Group Publishing Limited, volume 14, issue 2, pages 187-206, October, DOI: 10.1108/JFEP-11-2020-0241.
• Nikolai Dokuchaev, 2021, "On statistical indistinguishability of complete and incomplete market models," Studies in Economics and Finance, Emerald Group Publishing Limited, volume 38, issue 1, pages 114-125, February, DOI: 10.1108/SEF-01-2020-0023.
• Houmera Bibi Sabera Nunkoo & Preethee Nunkoo Gonpot & Noor-Ul-Hacq Sookia & T.V. Ramanathan, 2021, "Autoregressive conditional duration models for high frequency financial data: an empirical study on mid cap exchange traded funds," Studies in Economics and Finance, Emerald Group Publishing Limited, volume 39, issue 1, pages 150-173, October, DOI: 10.1108/SEF-04-2021-0146.
• Ramiro Bautista Espinosa & Diana Terrazas Santamaría, 2021, "La viabilidad de invertir en almacenamiento de energía solar en México: un enfoque de opciones reales," Serie documentos de trabajo del Centro de Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, number 2021-09, Nov.
• Patrycja Klusak & Matthew Agarwala & Matt Burke & Moritz Kraemer & Kamiar Mohaddes, 2021, "Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2110, Apr.