Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2021

• 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.
• Christian Stummer & Lars Lüpke & Markus Günther, 2021, "Beaming market simulation to the future by combining agent-based modeling with scenario analysis," Journal of Business Economics, Springer, volume 91, issue 9, pages 1469-1497, November, DOI: 10.1007/s11573-021-01046-9.
• Martin Pažický, 2021, "Predicting Recessions in Germany Using the German and the US Yield Curve," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 17, issue 3, pages 263-291, December, DOI: 10.1007/s41549-021-00061-7.
• 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.
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• 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.
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• 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.
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• Halina Swieboda & Mateusz Kuczabski & Ryszard Szpyra & Tomasz Zawadzki & Tomasz Walecki & Pawel Stobiecki, 2021, "Social Control in the Face of Digital Propaganda," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2 - Part , pages 1057-1069.
• Ilona Urych, 2021, "The Use of Models for the Diagnosis of Defence Potential in the Strategic Management of Activities by Civilians to Strengthen National Security," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2 - Part , pages 830-840.
• Joanna Kowalik, 2021, "The “UMO” as an Example of Scientific Project Implementation," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2 - Part , pages 1055-1068.
• Tomasz Lukaszewski, 2021, "Buildings Thermal Retrofit Investement under ESCO Formula - A Case Study from Poland," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2 - Part , pages 1081-1093.
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