Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2022

• Aharon, David Y. & Umar, Zaghum & Aziz, Mukhriz Izraf Azman & Vo, Xuan vinh, 2022, "COVID-19 related media sentiment and the yield curve of G-7 economies," The North American Journal of Economics and Finance, Elsevier, volume 61, issue C, DOI: 10.1016/j.najef.2022.101678.
• Yi, Yongsheng & He, Mengxi & Zhang, Yaojie, 2022, "Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?," The North American Journal of Economics and Finance, Elsevier, volume 62, issue C, DOI: 10.1016/j.najef.2022.101731.
• Nonejad, Nima, 2022, "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, volume 62, issue C, DOI: 10.1016/j.najef.2022.101751.
• Salisu, Afees A. & Ogbonna, Ahamuefula E. & Lasisi, Lukman & Olaniran, Abeeb, 2022, "Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach," The North American Journal of Economics and Finance, Elsevier, volume 62, issue C, DOI: 10.1016/j.najef.2022.101755.
• Diebold, Francis X. & Göbel, Maximilian, 2022, "A benchmark model for fixed-target Arctic sea ice forecasting," Economics Letters, Elsevier, volume 215, issue C, DOI: 10.1016/j.econlet.2022.110478.
• Bognanni, Mark, 2022, "Comment on “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors”," Journal of Econometrics, Elsevier, volume 227, issue 2, pages 498-505, DOI: 10.1016/j.jeconom.2021.10.008.
• Angelico, Cristina & Marcucci, Juri & Miccoli, Marcello & Quarta, Filippo, 2022, "Can we measure inflation expectations using Twitter?," Journal of Econometrics, Elsevier, volume 228, issue 2, pages 259-277, DOI: 10.1016/j.jeconom.2021.12.008.
• Jin, Xin & Maheu, John M. & Yang, Qiao, 2022, "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, volume 228, issue 2, pages 302-321, DOI: 10.1016/j.jeconom.2021.10.010.
• Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022, "On LASSO for predictive regression," Journal of Econometrics, Elsevier, volume 229, issue 2, pages 322-349, DOI: 10.1016/j.jeconom.2021.02.002.
• Chen, Li & Gao, Jiti & Vahid, Farshid, 2022, "Global temperatures and greenhouse gases: A common features approach," Journal of Econometrics, Elsevier, volume 230, issue 2, pages 240-254, DOI: 10.1016/j.jeconom.2021.04.003.
• Zhu, Yinchu & Timmermann, Allan, 2022, "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 329-347, DOI: 10.1016/j.jeconom.2021.10.006.
• Bollerslev, Tim & Medeiros, Marcelo C. & Patton, Andrew J. & Quaedvlieg, Rogier, 2022, "From zero to hero: Realized partial (co)variances," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 348-360, DOI: 10.1016/j.jeconom.2021.04.013.
• Gardner, Ben & Scotti, Chiara & Vega, Clara, 2022, "Words speak as loudly as actions: Central bank communication and the response of equity prices to macroeconomic announcements," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 387-409, DOI: 10.1016/j.jeconom.2021.07.014.
• Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022, "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 500-519, DOI: 10.1016/j.jeconom.2021.04.012.
• Diebold, Francis X. & Rudebusch, Glenn D., 2022, "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, volume 231, issue 2, pages 520-534, DOI: 10.1016/j.jeconom.2020.12.007.
• du Plessis, Emile, 2022, "Multinomial modeling methods: Predicting four decades of international banking crises," Economic Systems, Elsevier, volume 46, issue 2, DOI: 10.1016/j.ecosys.2022.100979.
• Korobilis, Dimitris, 2022, "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, volume 148, issue C, DOI: 10.1016/j.euroecorev.2022.104241.
• Rubesam, Alexandre, 2022, "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, volume 51, issue PB, DOI: 10.1016/j.ememar.2022.100891.

2021

• David E. Allen & Michael McAleer, 2021, "Predicting COVID-19 Cases and Deaths in the USA from Tests and State Populations," Advances in Decision Sciences, Asia University, Taiwan, volume 25, issue 2, pages 1-27, June.
• Martin M. Andreasen & Giovanni Caggiano & Efrem Castelnuovo & Giovanni Pellegrino, 2021, "Why Does Risk Matter More in Recessions than in Expansions?," Economics Working Papers, Department of Economics and Business Economics, Aarhus University, number 2021-12, Sep.
• Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021, "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2021-02, Jan.
• Leopoldo Catania & Alessandra Luati & Pierluigi Vallarino, 2021, "Economic vulnerability is state dependent," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2021-09, Jun.
• Mikkel Bennedsen & Asger Lunde & Neil Shephard & Almut E.D. Veraart, 2021, "Inference and forecasting for continuous-time integer-valued trawl processes and their use in financial economics," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2021-12, Jul.
• Ulrich Hounyo & Kajal Lahiri, 2021, "Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2021-14, Sep.
• Isaac K. Ofori & Christopher Quaidoo & Pamela E. Ofori, 2021, "What Drives Financial Sector Development in Africa? Insights from Machine Learning," Research Africa Network Working Papers, Research Africa Network (RAN), number 21/074, Jan.
• Sarthak Behera & Hyeongwoo Kim & Soohyon Kim, 2021, "Superior Predictability of American Factors of the Won/Dollar Real Exchange Rate," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2021-03, Jul.
• Ivan Korolev, 2021, "How Could Russia Have Developed without the Revolution of 1917?," Annals of Economics and Statistics, GENES, issue 144, pages 75-112, DOI: https://doi.org/10.15609/annaeconst.
• George-Marios Angeletos & Zhen Huo, 2021, "Myopia and Anchoring," American Economic Review, American Economic Association, volume 111, issue 4, pages 1166-1200, April, DOI: 10.1257/aer.20191436.
• Alexandre N. Kohlhas & Ansgar Walther, 2021, "Asymmetric Attention," American Economic Review, American Economic Association, volume 111, issue 9, pages 2879-2925, September, DOI: 10.1257/aer.20191432.
• Barbara Rossi, 2021, "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, volume 59, issue 4, pages 1135-1190, December, DOI: 10.1257/jel.20201479.
• Vesna Karadzic & Bojan Pejovic, 2021, "Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, volume 23, issue 57, pages 517-517.
• Nigel E.N. Chitambo & Darren Lee & Sure Mataramvura, 2021, "A Hybrid Neural Network GARCH Approach to Forecasting Zimbabwean Inflation Volatility," The African Finance Journal, Africagrowth Institute, volume 23, issue 1, pages 56-73.
• Isaac K. Ofori & Christopher Quaidoo & Pamela E. Ofori, 2021, "What Drives Financial Sector Development in Africa? Insights from Machine Learning," Working Papers of the African Governance and Development Institute., African Governance and Development Institute., number 21/074, Jan.
• Arkadiusz Jedrzejewski & Grzegorz Marcjasz & Rafal Weron, 2021, "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/21/04, Mar.
• Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021, "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/21/06, Apr.
• Kin G. Olivares & Cristian Challu & Grzegorz Marcjasz & Rafal Weron & Artur Dubrawski, 2021, "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/21/07, Apr.
• Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafal Weron, 2021, "Erratum to 'Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark' [Appl. Energy 293 (2021) 116983]," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/21/12, Jul.
• Efe Arda & Güray Küçükkocaoğlu, 2021, "Yapay Zeka Yöntemleri İle Hisse Senedi Fiyat Öngörüleri," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 6, issue 2, pages 565-586, DOI: 10.30784/epfad.878664.
• Aloy, Marcel & Laly, Floris & Laurent, Sébastien & Lecourt, Christelle, 2021, "Modeling Time-Varying Conditional Betas. A Comparison of Methods with Application for REITs," LIDAM Reprints LFIN, Université catholique de Louvain, Louvain Finance (LFIN), number 2021021, Jan, DOI: https://doi.org/10.1007/978-3-030-5.
• Nurdaulet Abilov & Aizhan Bolatbayeva, 2021, "Nowcasting GDP growth in Russia with an incomplete dataset: A factor model approach," NAC Analytica Working Paper, NAC Analytica, Nazarbayev University, number 18, Dec, revised Feb 2022.
• Magdalena Cornejo, 2021, "Forecasting crop yields through climate variables using mixed frequency data. The case of Argentine soybeans," Económica, Instituto de Investigaciones Económicas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, volume 67, pages 93-106, January-D.
• Asta Ndongo & Ibrahima Thione Diop, 2021, "Economic and Monetary Integration in ECOWAS Countries: A Panel VAR Approach to Identify Macroeconomic Shocks," World Journal of Applied Economics, WERI-World Economic Research Institute, volume 7, issue 2, pages 61-87, December, DOI: 10.22440/wjae.7.2.3.
• Игсатов О.Р. // Igsatov О.R., 2021, "Анализ эффективности процентного канала в Казахстане // Analyzing effectiveness of the interest rate channel in Kazakhstan," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue 1, pages 4-14.
• Константин Орлов // Konstantin Orlov, 2021, "Построение большой байесовской авторегрессионной модели для Казахстана // Building a Large Bayesian Vector Autoregression Model for Kazakhstan," Working Papers, National Bank of Kazakhstan, number #2021-1.
• Francis X. Diebold & Maximilian Gobel, 2021, "A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting," Papers, arXiv.org, number 2101.10359, Jan, revised Jan 2022.
• Rui Fan & Ji Hyung Lee & Youngki Shin, 2021, "Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach," Papers, arXiv.org, number 2101.11568, Jan, revised Dec 2022.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," Papers, arXiv.org, number 2103.01201, Mar.
• Michael Pfarrhofer, 2021, "Modeling tail risks of inflation using unobserved component quantile regressions," Papers, arXiv.org, number 2103.03632, Mar, revised Oct 2021.
• Davide Fiaschi & Cristina Tealdi, 2021, "A general methodology to measure labour market dynamics," Papers, arXiv.org, number 2104.01097, Apr.
• David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021, "Loss-Based Variational Bayes Prediction," Papers, arXiv.org, number 2104.14054, Apr, revised May 2022.
• David Ardia & Keven Bluteau & Alaa Kassem, 2021, "A Century of Economic Policy Uncertainty Through the French-Canadian Lens," Papers, arXiv.org, number 2106.05240, Jun, revised Oct 2021.
• Davide Fiaschi & Cristina Tealdi, 2021, "Young people between education and the labour market during the COVID-19 pandemic in Italy," Papers, arXiv.org, number 2106.08296, Jun.
• Francesca Micocci & Armando Rungi, 2021, "Predicting Exporters with Machine Learning," Papers, arXiv.org, number 2107.02512, Jul, revised Sep 2022.
• Mikkel Bennedsen & Asger Lunde & Neil Shephard & Almut E. D. Veraart, 2021, "Inference and forecasting for continuous-time integer-valued trawl processes," Papers, arXiv.org, number 2107.03674, Jul, revised Feb 2023.
• Wei Li & Florentina Paraschiv & Georgios Sermpinis, 2021, "A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection," Papers, arXiv.org, number 2107.08808, Jul.
• Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021, "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Papers, arXiv.org, number 2110.03411, Oct.
• Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2021, "Forecasting with a Panel Tobit Model," Papers, arXiv.org, number 2110.14117, Oct, revised Jul 2022.
• Timo Mitze & Teemu Makkonen, 2021, "Can large-scale R&I funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19," Papers, arXiv.org, number 2112.11562, Dec.
• Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021, "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers, Institute of Economic Growth, number 446, Oct.
• Massimo Guidolin & Davide La Cara & Massimiliano Marcellino, 2021, "Boosting the Forecasting Power of Conditional Heteroskedasticity Models to Account for Covid-19 Outbreaks," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 21169.
• Vira Sepeta, 2021, "Evaluation Of The Level Of The Competitiveness And Labor Potential Of Industrial Enterprises By Means Of The Integral Indicator," Green, Blue & Digital Economy Journal, Publishing house "Baltija Publishing", volume 2, issue 1, DOI: 10.30525/2661-5169/2021-1-12.
• Martina Makarieva, 2021, "Yield curve modelling and forecasting in an undeveloped financial market: The case of Bulgaria," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 61-83,84-10.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 21-01, Mar.
• Francois-Michel Boire & Thibaut Duprey & Alexander Ueberfeldt, 2021, "Shaping the future: Policy shocks and the GDP growth distribution," Staff Working Papers, Bank of Canada, number 21-24, May, DOI: 10.34989/swp-2021-24.
• Tatjana Dahlhaus & Julia Schaumburg & Tatevik Sekhposyan, 2021, "Networking the Yield Curve: Implications for Monetary Policy," Staff Working Papers, Bank of Canada, number 21-4, Jan, DOI: 10.34989/swp-2021-4.
• Ugochi Emenogu & Cars Hommes & Mikael Khan, 2021, "Detecting exuberance in house prices across Canadian cities," Staff Analytical Notes, Bank of Canada, number 2021-9, May, DOI: 10.34989/san-2021-9.
• Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner P. Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Yihao Lin, 2021, "Machine Learning and Oil Price Point and Density Forecasting," Working Papers Series, Central Bank of Brazil, Research Department, number 544, Feb.
• Pablo Garcia & Pascal Jacquinot & Crt Lenarcic & Matija Lozej & Kostas Mavromatis, 2021, "Global models for a global pandemic: the impact of COVID-19 on small euro area economies," BCL working papers, Central Bank of Luxembourg, number 156, Oct.
• Emilio Blanco & Laura D’Amato & Fiorella Dogliolo & Lorena Garegnani, 2021, "Nowcast of Macroeconomic Aggregates in Argentina: Comparing the Predictive Capacity of Different Models," BCRA Working Paper Series, Central Bank of Argentina, Economic Research Department, number 202190, Jan.
• Pablo Aguilar, 2021, "La recuperación del consumo en 2021: un análisis a partir de las expectativas de los consumidores," Boletín Económico, Banco de España, issue 3/2021.
• Pablo Aguilar, 2021, "Consumption recovery in 2021: an analysis drawing on consumer expectations," Economic Bulletin, Banco de España, issue 3/2021.
• Corinna Ghirelli & María Gil & Samuel Hurtado & Alberto Urtasun, 2021, "Relación entre las medidas de contención de la pandemia, la movilidad y la actividad económica," Occasional Papers, Banco de España, number 2109, Mar.
• Corinna Ghirelli & María Gil & Samuel Hurtado & Alberto Urtasun, 2021, "The relationship between pandemic containment measures, mobility and economic activity," Occasional Papers, Banco de España, number 2109, Mar.
• Erik Andres-Escayola & Juan Carlos Berganza & Rodolfo Campos & Luis Molina, 2021, "A BVAR toolkit to assess macrofinancial risks in Brazil and Mexico," Occasional Papers, Banco de España, number 2114, Jun.
• Marta Bañbura & Danilo Leiva-León & Jan-Oliver Menz, 2021, "Do inflation expectations improve model-based inflation Forecasts?," Working Papers, Banco de España, number 2138, Oct.
• Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021, "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 609, Mar.
• Cristina Angelico & Juri Marcucci & Marcello Miccoli & Filippo Quarta, 2021, "Can we measure inflation expectations using Twitter?," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1318, Feb.
• Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021, "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1324, Mar.
• Maria Ludovica Drudi & Stefano Nobili, 2021, "A liquidity risk early warning indicator for Italian banks: a machine learning approach," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1337, Jun.
• Ibarra-Ramírez Raúl, 2021, "The Yield Curve as a Predictor of Economic Activity in Mexico: The Role of the Term Premium," Working Papers, Banco de México, number 2021-07, Jun.
• Bátiz-Zuk Enrique & Mohamed Abdulkadir & Sánchez-Cajal Fátima, 2021, "Exploring the sources of loan default clustering using survival analysis with frailty," Working Papers, Banco de México, number 2021-14, Sep.
• Rangel González Erick & Llamosas-Rosas Irving, 2021, "Observing the Evolution of the Informal Sector from Space: A Municipal Approach 2013-2020," Working Papers, Banco de México, number 2021-18, Dec.
• Alejandro Rojas-Bernal & Mauricio Villamizar-Villegas, 2021, "Pricing the exotic: Path-dependent American options with stochastic barriers," Borradores de Economia, Banco de la Republica de Colombia, number 1156, Mar, DOI: https://doi.org/10.32468/be.1156.
• Franky Juliano Galeano-Ramírez & Nicolás Martínez-Cortés & Carlos D. Rojas-Martínez, 2021, "Nowcasting Colombian Economic Activity: DFM and Factor-MIDAS approaches," Borradores de Economia, Banco de la Republica de Colombia, number 1168, Aug, DOI: 10.32468/be.1168.
• Juan C. Méndez-Vizcaíno & Alexander Guarin & César Anzola-Bravo & Anderson Grajales-Olarte, 2021, "Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia," Borradores de Economia, Banco de la Republica de Colombia, number 1178, Oct, DOI: 10.32468/be.1178.
• Marcela De Castro-Valderrama & Santiago Forero-Alvarado & Nicolás Moreno-Arias & Sara Naranjo-Saldarriaga, 2021, "Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts," Borradores de Economia, Banco de la Republica de Colombia, number 1184, Dec, DOI: 10.32468/be.1184.
• Ana María Iregui-Bohórquez & César Anzola-Bravo & Luisa Fernanda Ballén-Rubio & Valeria Bejarano-Salcedo & Eliana González-Molano & Anderson Grajales-Olarte & Alexander Guarín-López & María Alejandra , 2021, "¿Qué nos dicen las encuestas sobre la formación de expectativas de inflación?," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, issue 100, pages 1-95, September.
• Florens Odendahl & Tatevik Sekhposyan & Barbara Rossi, 2021, "Evaluating Forecast Performance with State Dependence," Working Papers, Barcelona School of Economics, number 1295, Oct.
• Andrejs Bessonovs & Olegs Krasnopjorovs, 2021, "Short-term inflation projections model and its assessment in Latvia," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, volume 21, issue 2, pages 184-204.
• Ksenia Mayorova & Nikita Fokin, 2021, "Nowcasting Growth Rates of Russia's Export and Import by Commodity Group," Russian Journal of Money and Finance, Bank of Russia, volume 80, issue 3, pages 34-48, September, DOI: 10.31477/rjmf.202103.34.
• Roman Tikhonov & Aleksey Masyutin & Vadim Anpilogov, 2021, "The Relationship Between the Financial Performance of Banks and the Quality of Credit Scoring Models," Russian Journal of Money and Finance, Bank of Russia, volume 80, issue 2, pages 76-95, June, DOI: 10.31477/rjmf.202102.76.
• Elizaveta Golovanova & Andrey Zubarev, 2021, "Forecasting Aggregate Retail Sales with Google Trends," Russian Journal of Money and Finance, Bank of Russia, volume 80, issue 4, pages 50-73, December, DOI: 10.31477/rjmf.202104.50.
• Denis Shibitov & Mariam Mamedli, 2021, "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series, Bank of Russia, number wps70, Apr.
• Tommaso Proietti & Alessandro Giovannelli, 2021, "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, volume 184, issue 2, pages 683-706, April, DOI: 10.1111/rssa.12645.
• Valentina Aprigliano & Danilo Liberati, 2021, "Using Credit Variables to Date Business Cycle and to Estimate the Probabilities of Recession in Real Time," Manchester School, University of Manchester, volume 89, issue S1, pages 76-96, September, DOI: 10.1111/manc.12292.
• Anibal Emiliano Da Silva Neto & Jesús Gonzalo & Jean‐Yves Pitarakis, 2021, "Uncovering Regimes in Out of Sample Forecast Errors from Predictive Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, volume 83, issue 3, pages 713-741, June, DOI: 10.1111/obes.12418.
• Fabio Busetti & Michele Caivano & Davide Delle Monache, 2021, "Domestic and Global Determinants of Inflation: Evidence from Expectile Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, volume 83, issue 4, pages 982-1001, August, DOI: 10.1111/obes.12428.
• J. James Reade & Carl Singleton & Alasdair Brown, 2021, "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, volume 68, issue 2, pages 261-285, May, DOI: 10.1111/sjpe.12264.
• Joab Dan Valdivia Coria & Juan Carlos Carlo Santos, 2021, "Efectos de la inversión pública y privada en el crecimiento económico de Bolivia," Revista de Análisis del BCB, Banco Central de Bolivia, volume 34, issue 1, pages 55-86, January -.
• Rolando Einar Paz Rodriguez, 2021, "Análisis de las expectativas cambiarias en Bolivia," Revista de Análisis del BCB, Banco Central de Bolivia, volume 35, issue 1, pages 105-128, July - De.
• Eleonora Granziera & Pirkka Jalasjoki & Maritta Paloviita, 2021, "The Bias and Efficiency of the ECB Inflation Projections: a State Dependent Analysis," Working Paper, Norges Bank, number 2021/1, Apr.
• Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2021, "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Paper, Norges Bank, number 2021/3, Jun.
• Andreas Joseph & Eleni Kalamara & George Kapetanios & Galina Potjagailo & Chiranjit Chakraborty, 2021, "Forecasting UK inflation bottom up," Bank of England working papers, Bank of England, number 915, Mar.
• Nikoleta Anesti & Eleni Kalamara & George Kapetanios, 2021, "Forecasting UK GDP growth with large survey panels," Bank of England working papers, Bank of England, number 923, May.
• A. Akhileshwari & B. Kishore Babu & Rachana Saxena, 2021, "Forecasting of Net Asset Value of selected Environmental, Social and Governance (ESG) Mutual Funds in India using ARIMA Model," Acta Universitatis Bohemiae Meridionalis, University of South Bohemia in Ceske Budejovice, Faculty of Economics, volume 24, issue 3, pages 95-106, DOI: 10.32725/acta.2021.014.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021, "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers, Bank of Israel, number 2021.06, Mar.
• Takuji Kawamoto & Takashi Nakazawa & Yui Kishaba & Kohei Matsumura & Jouchi Nakajima, 2021, "Supplementary Paper Series for the "Assessment" (2): Estimating Effects of Expansionary Monetary Policy since the Introduction of Quantitative and Qualitative Monetary Easing (QQE) Using the Macroeconomic Model (Q-JEM)," Bank of Japan Working Paper Series, Bank of Japan, number 21-E-4, Apr.
• Takuji Kawamoto & Jouchi Nakajima & Tomoaki Mikami, 2021, "Supplementary Paper Series for the "Assessment" (3): Inflation-Overshooting Commitment:An Analysis Using a Macroeconomic Model," Bank of Japan Working Paper Series, Bank of Japan, number 21-E-9, Jul.
• Jouchi Nakajima & Hiroaki Yamagata & Tatsushi Okuda & Shinnosuke Katsuki & Takeshi Shinohara, 2021, "Extracting Firms' Short-Term Inflation Expectations from the Economy Watchers Survey Using Text Analysis," Bank of Japan Working Paper Series, Bank of Japan, number 21-E-12, Oct.
• Becker William & Paruolo Paolo & Saltelli Andrea, 2021, "Variable Selection in Regression Models Using Global Sensitivity Analysis," Journal of Time Series Econometrics, De Gruyter, volume 13, issue 2, pages 187-233, July, DOI: 10.1515/jtse-2018-0025.
• Reif Magnus, 2021, "Macroeconomic uncertainty and forecasting macroeconomic aggregates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 25, issue 2, pages 1-20, April, DOI: 10.1515/snde-2019-0073.
• Lahiri Kajal & Yang Liu, 2021, "Construction of leading economic index for recession prediction using vine copulas," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 25, issue 4, pages 193-212, September, DOI: 10.1515/snde-2019-0033.
• Maurin Baillif & Matthieu de Lapparent & Evanthia Kazagli, 2021, "A Hybrid Approach to Real Estate Price Definition: A Case Study in Western Switzerland," Revue économique, Presses de Sciences-Po, volume 72, issue 6, pages 1055-1077.
• Mueller, H. & Rauh, C., 2021, "The Hard Problem of Prediction for Conflict Prevention," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2103, Jan.
• Ding, Y., 2021, "Augmented Real-Time GARCH: A Joint Model for Returns, Volatility and Volatility of Volatility," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2112, Feb.
• Klusak, P. & Agarwala, M. & Burke, M. & Kraemer, M. & Mohaddes, K., 2021, "Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2127, Mar.
• Ding, Y., 2021, "Conditional Heteroskedasticity in the Volatility of Asset Returns," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2179, Nov.
• Ba Chu & Shafiullah Qureshi, 2021, "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers, Carleton University, Department of Economics, number 21-12, Oct.
• Olatunji Abdul Shobande & Oladimeji Tomiwa Shodipe, 2021, "Monetary Policy Interdependency in Fisher Effect: A Comparative Evidence," Journal of Central Banking Theory and Practice, Central bank of Montenegro, volume 10, issue 1, pages 203-226.
• Carlos Castro-Iragorri & Juan Felipe Peña & Cristhian Rodríguez, 2021, "A Segmented and Observable Yield Curve for Colombia," Journal of Central Banking Theory and Practice, Central bank of Montenegro, volume 10, issue 2, pages 179-200.
• Nenad Milojević & Srdjan Redzepagic, 2021, "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, volume 10, issue 3, pages 41-57.
• Congressional Budget Office, 2021, "CBO’s Economic Forecasting Record: 2021 Update," Reports, Congressional Budget Office, number 57579, Dec.
• Filip Stanek, 2021, "Optimal Out-of-Sample Forecast Evaluation under Stationarity," CERGE-EI Working Papers, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp712, Nov.
• Felix Haase & Matthias Neuenkirch, 2021, "Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US," CESifo Working Paper Series, CESifo, number 8828.
• Simon Blöthner & Mario Larch, 2021, "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series, CESifo, number 9233.
• Kajal Lahiri & Cheng Yang, 2021, "Boosting Tax Revenues with Mixed-Frequency Data in the Aftermath of Covid-19: The Case of New York," CESifo Working Paper Series, CESifo, number 9365.
• Jorge Miguel Bravo & Mercedes Ayuso & Robert Holzmann & Edward Palmer, 2021, "Intergenerational Actuarial Fairness when Longevity Increases: Amending the Retirement Age," CESifo Working Paper Series, CESifo, number 9408.
• Kajal Lahiri & Junyan Zhang & Yongchen Zhao, 2021, "Inefficiency in Social Security Trust Funds Forecasts," CESifo Working Paper Series, CESifo, number 9415.
• Mototsugu Shintani & Kozo Ueda, 2021, "Identifying the Source of Information Rigidities in the Expectations Formation Process," CARF F-Series, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, number CARF-F-516, Jun.
• Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2021, "A penalized two-pass regression to predict stock returns with time-varying risk premia," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-09, Jan.
• Matteo Garzoli & Alberto Plazzi & Rossen I. Valkanov, 2021, "Backcasting, Nowcasting, and Forecasting Residential Repeat-Sales Returns: Big Data meets Mixed Frequency," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-21, Mar.
• David Solo & Didier Sornette & Florian Ulmann, 2021, "Dynamical Internal Cost of Capital Driven by Cash Flow Growth," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-24, Mar.
• Alexander Wehrli & Didier Sornette, 2021, "Excess financial volatility explained by endogenous excitations revealed by EM calibrations of a generalized Hawkes point process," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-35, Apr.
• Eric Jondeau & Alexandre Pauli, 2021, "Disasters, Large Drawdowns, and Long-term Asset Management," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-37, Jun.
• Pawel Polak & Urban Ulrych, 2021, "Dynamic Currency Hedging with Ambiguity," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-60, Aug.
• Dongshuai Zhao, CFA & Didier Sornette, 2021, "Bubbles for Fama from Sornette," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-94, Dec.
• Damir Filipović & Amir Khalilzadeh, 2021, "Machine Learning for Predicting Stock Return Volatility," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-95, Dec.
• Christian Urom & Gideon Ndubuisi & Jude Ozor, 2021, "Economic activity, and financial and commodity markets’ shocks: An analysis of implied volatility indexes," International Economics, CEPII research center, issue 165, pages 51-66.
• Hugo Couture & Dalibor Stevanovic, 2021, "Analyse du marché du travail à l’aide des données de Google Trends," CIRANO Project Reports, CIRANO, number 2021rp-15, Aug.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," CIRANO Working Papers, CIRANO, number 2021s-09, Mar.
• Miroslav Plasil, 2021, "Designing Macro-Financial Scenarios: The New CNB Framework and Satellite Models for Property Prices and Credit," Research and Policy Notes, Czech National Bank, Research and Statistics Department, number 2021/01, Sep.
• Michal Franta & Jan Libich, 2021, "Holding the Economy by the Tail: Analysis of Short- and Long-run Macroeconomic Risks," Working Papers, Czech National Bank, Research and Statistics Department, number 2021/3, Sep.
• Ricardo Crisóstomo, 2021, "Estimación de probabilidades representativas del mundo real: importancia de los sesgos conductuales," CNMV Documentos de Trabajo, CNMV- Comisión Nacional del Mercado de Valores - Departamento de Estudios y Estadísticas, number CNMV Documentos de Trabaj.
• Javier Ojea-Ferreiro, 2021, "Deconstrucción del riesgo sistémico: Un método de prueba de resistencia inversa," CNMV Documentos de Trabajo, CNMV- Comisión Nacional del Mercado de Valores - Departamento de Estudios y Estadísticas, number CNMV Documentos de Trabaj.
• Ricardo Crisóstomo, 2021, "Estimating real word probabilities: a forward-looking behavioral framework," CNMV Working Papers, CNMV- Spanish Securities Markets Commission - Research and Statistics Department, number CNMV Working Papers no. 7.
• Javier Ojea-Ferreiro, 2021, "Deconstructing systemic risk: A reverse stress testing approach," CNMV Working Papers, CNMV- Spanish Securities Markets Commission - Research and Statistics Department, number CNMV Working Papers no. 7.
• C Castro-Iragorri & J RamÔøΩrez, 2021, "Forecasting Dynamic Term Structure Models with Autoencoders," Documentos de Trabajo, Universidad del Rosario, number 19431, Jul.
• Pavel Vidal Alejandro & Gilberto Ram�rez & Lya Paola Sierra, 2021, "Un análisis regional de los choques monetarios y externos: el caso del Valle del Cauca en Colombia," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, volume 40, issue 82, pages 57-81.
• Ana María Iregui-Bohórquez & C�sar Anzola-Bravo & Luisa Fernanda Ball�n-Rubio & Valeria Bejarano-Salcedo & Eliana Gonz�lez-Molano & Anderson Grajales-Olarte & Alexander Guar�n-L�pez & Mar�a Alejandra , 2021, "¿Qué nos dicen las encuestas sobre la formación de expectativas de inflación?," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, issue 100, pages 1-95.
• Katherine Coronel-Pangol & Gustavo Flores-S�nchez & Jorge Campoverde-Campoverde & Armando Romero-Galarza, 2021, "Aproximación predictiva al riesgo crediticio comercial en empresas alimenticias ecuatorianas," Estudios Gerenciales, Universidad Icesi, volume 37, issue 160, pages 413-424, DOI: 10.18046/j.estger.2021.160.4022.
• Alexander Correa, 2021, "Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 95, pages 105-134.
• Benchimol, Jonathan & Bounader, Lahcen, 2021, "Optimal Monetary Policy Under Bounded Rationality," Dynare Working Papers, CEPREMAP, number 67, Mar.
• Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021, "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15854, Feb.
• Marcellino, Massimiliano & Stevanovic, Dalibor & Goulet Coulombe, Philippe, 2021, "Can Machine Learning Catch the COVID-19 Recession?," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15867, Mar.
• Engel, Charles & Wu, Steve Pak Yeung, 2021, "Forecasting the U.S. Dollar in the 21st Century," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15915, Mar.
• Timmermann, Allan & Zhu, Yinchu, 2021, "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15917, Mar.
• Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea & Mertens, Elmar, 2021, "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15964, Mar.
• Mckibbin, Warwick & Fernando, Roshen & Liu, Weifeng, 2021, "Global Economic Impacts of Climate Shocks, Climate Policy and Changes in Climate Risk Assessment," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16154, May.
• , & Stein, Tobias, 2021, "Equity premium predictability over the business cycle," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16357, Sep.
• Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021, "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16496, Aug.
• Mckibbin, Warwick & Jaumotte, Florence & Liu, Weifeng, 2021, "Mitigating Climate Change: Growth-Friendly Policies to Achieve Net Zero Emissions by 2050," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16553, Sep.
• Rubio-Ramírez, Juan Francisco & Petrella, Ivan & Antolin-Diaz, Juan, 2021, "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16613, Oct.
• Broer, Tobias & Kohlhas, Alexandre & Mitman, Kurt & Schlafmann, Kathrin, 2021, "On the Possibility of Krusell-Smith Equilibria," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16667, Oct.
• Schorfheide, Frank & Song, Dongho, 2021, "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16760, Nov.
• 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," GRU Working Paper Series, City University of Hong Kong, Department of Economics and Finance, Global Research Unit, number GRU_2021_017, May.
• Sarlin, Peter & von Schweinitz, Gregor, 2021, "Optimizing Policymakers’ Loss Functions In Crisis Prediction: Before, Within Or After?," Macroeconomic Dynamics, Cambridge University Press, volume 25, issue 1, pages 100-123, January.
• Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021, "Can Machine Learning Catch The Covid-19 Recession?," National Institute Economic Review, National Institute of Economic and Social Research, volume 256, issue , pages 71-109, May.
• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312, Nov.
• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312R, Nov, revised Mar 2022.
• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312R2, Nov, revised Oct 2022.
• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312R3, Nov, revised Jan 2023.
• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312R4, Nov, revised Jun 2023.
• Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2021, "Industrial Output Growth Forecast: A Machine Learning Approach Based on Cross-Validation," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot GmbH, Berlin, volume 67, issue 4, pages 337-351, DOI: 10.3790/aeq.67.4.337.
• Lukas Boer & Andrea Pescatori & Martin Stuermer, 2021, "Energy Transition Metals," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1976.
• Bastien Lextrait, 2021, "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2021-25.
• Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2021, "Using machine learning and big data to analyse the business cycle," Economic Bulletin Articles, European Central Bank, volume 5.
• Alogoskoufis, Spyros & Dunz, Nepomuk & Emambakhsh, Tina & Hennig, Tristan & Kaijser, Michiel & Kouratzoglou, Charalampos & Muñoz, Manuel A. & Parisi, Laura & Salleo, Carmelo, 2021, "ECB’s economy-wide climate stress test," Occasional Paper Series, European Central Bank, number 281, Sep.
• Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2021, "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Working Paper Series, European Central Bank, number 2510, Jan.
• Le Mezo, Helena & Ferrari Minesso, Massimo, 2021, "Text-based recession probabilities," Working Paper Series, European Central Bank, number 2516, Jan.
• Dalhaus, Tatjana & Schaumburg, Julia & Sekhposyan, Tatevik, 2021, "Networking the yield curve: implications for monetary policy," Working Paper Series, European Central Bank, number 2532, Mar.
• Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2021, "Economic predictions with big data: the illusion of sparsity," Working Paper Series, European Central Bank, number 2542, Apr.
• Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021, "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series, European Central Bank, number 2543, May.
• Bobeica, Elena & Hartwig, Benny, 2021, "The COVID-19 shock and challenges for time series models," Working Paper Series, European Central Bank, number 2558, May.
• Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021, "The time-varying evolution of inflation risks," Working Paper Series, European Central Bank, number 2600, Oct.
• Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021, "A mixed frequency BVAR for the euro area labour market," Working Paper Series, European Central Bank, number 2601, Oct.
• Garcia, Pablo & Jacquinot, Pascal & Lenarčič, Črt & Lozej, Matija & Mavromatis, Kostas, 2021, "Global models for a global pandemic: the impact of COVID-19 on small euro area economies," Working Paper Series, European Central Bank, number 2603, Oct.
• Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021, "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series, European Central Bank, number 2604, Oct.
• Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021, "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series, European Central Bank, number 2614, Nov.
• Sokol, Andrej, 2021, "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series, European Central Bank, number 2624, Dec.
• Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021, "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, volume 11, issue 1, pages 49-60.
• Ernie Hendrawaty & Rialdi Azhar & Fajrin Satria Dwi Kesumah & Sari Indah Oktanti Sembiring & Mega Metalia, 2021, "Modelling and Forecasting Crude Oil Prices during COVID-19 Pandemic," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 2, pages 149-154.
• Santiago Gall n & Jorge Barrientos, 2021, "Forecasting the Colombian Electricity Spot Price under a Functional Approach," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 2, pages 67-74.