Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

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.
• Knut Are Aastveit & Jamie Cross & Herman K. Djik, 2021, "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 03/2021, 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," 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.
• Ding, Y., 2021, "Conditional Heteroskedasticity in the Volatility of Asset Returns," Janeway Institute Working Papers, Faculty of Economics, University of Cambridge, number 2111, 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.
• Chaido Dritsaki & Dimitrios Niklis & Pavlos Stamatiou, 2021, "Oil Consumption Forecasting using ARIMA Models: An Empirical Study for Greece," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 4, pages 214-224.
• Mohamed Defaf & Mohamed Tkiouat, 2021, "A Review on Prospective Energy Models: The Moroccan Case," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 4, pages 14-23.
• Saring Suhendro & Mega Matalia & Sari Indah Oktanti Sembiring, 2021, "Public Sector Policy of Estimating Model for Renewable Energy," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 5, pages 609-613.
• Bharat Kumar Meher & Iqbal Thonse Hawaldar & Mathew Thomas Gil & Deebom Zorle Dum, 2021, "Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 6, pages 489-502.
• Thu, Le Ha & Leon-Gonzalez, Roberto, 2021, "Forecasting macroeconomic variables in emerging economies," Journal of Asian Economics, Elsevier, volume 77, issue C, DOI: 10.1016/j.asieco.2021.101403.
• Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021, "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, volume 127, issue C, DOI: 10.1016/j.jedc.2021.104119.
• Gelfer, Sacha, 2021, "Evaluating the forecasting power of an open-economy DSGE model when estimated in a data-Rich environment," Journal of Economic Dynamics and Control, Elsevier, volume 129, issue C, DOI: 10.1016/j.jedc.2021.104177.
• Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021, "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, volume 69, issue C, pages 208-224, DOI: 10.1016/j.eap.2020.12.008.
• Gangopadhyay, Kausik & Mondal, Debasis, 2021, "Productivity, relative sectoral prices, and total factor productivity: Theory and evidence," Economic Modelling, Elsevier, volume 100, issue C, DOI: 10.1016/j.econmod.2021.105509.
• Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021, "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, volume 101, issue C, DOI: 10.1016/j.econmod.2021.105522.
• Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021, "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, volume 101, issue C, DOI: 10.1016/j.econmod.2021.105531.
• Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021, "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, volume 102, issue C, DOI: 10.1016/j.econmod.2021.105556.
• Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021, "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, volume 103, issue C, DOI: 10.1016/j.econmod.2021.105614.
• Lucey, Brian & Ren, Boru, 2021, "Does news tone help forecast oil?," Economic Modelling, Elsevier, volume 104, issue C, DOI: 10.1016/j.econmod.2021.105635.
• Cross, Jamie L. & Hou, Chenghan & Trinh, Kelly, 2021, "Returns, volatility and the cryptocurrency bubble of 2017–18," Economic Modelling, Elsevier, volume 104, issue C, DOI: 10.1016/j.econmod.2021.105643.
• Casarin, Roberto & Costantini, Mauro & Paradiso, Antonio, 2021, "On the role of dependence in sticky price and sticky information Phillips curve: Modelling and forecasting," Economic Modelling, Elsevier, volume 105, issue C, DOI: 10.1016/j.econmod.2021.105644.
• Lourenço, Nuno & Gouveia, Carlos Melo & Rua, António, 2021, "Forecasting tourism with targeted predictors in a data-rich environment," Economic Modelling, Elsevier, volume 96, issue C, pages 445-454, DOI: 10.1016/j.econmod.2020.03.030.
• Frömmel, Michael & Midiliç, Murat, 2021, "Daily currency interventions in an emerging market: Incorporating reserve accumulation to the reaction function," Economic Modelling, Elsevier, volume 97, issue C, pages 461-476, DOI: 10.1016/j.econmod.2020.09.020.
• Mora-Valencia, Andrés & Rodríguez-Raga, Santiago & Vanegas, Esteban, 2021, "Skew index: Descriptive analysis, predictive power, and short-term forecast," The North American Journal of Economics and Finance, Elsevier, volume 56, issue C, DOI: 10.1016/j.najef.2020.101356.
• Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021, "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, volume 56, issue C, DOI: 10.1016/j.najef.2021.101383.
• Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021, "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101421.
• Figà-Talamanca, Gianna & Focardi, Sergio & Patacca, Marco, 2021, "Regime switches and commonalities of the cryptocurrencies asset class," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101425.
• Pérez-Rodríguez, Jorge V. & Andrada-Félix, Julián & Rachinger, Heiko, 2021, "Testing the forward volatility unbiasedness hypothesis in exchange rates under long-range dependence," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101438.
• Christou, Christina & Gupta, Rangan & Jawadi, Fredj, 2021, "Does inequality help in forecasting equity premium in a panel of G7 countries?," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101456.
• Shu, Min & Song, Ruiqiang & Zhu, Wei, 2021, "The ‘COVID’ crash of the 2020 U.S. Stock market," The North American Journal of Economics and Finance, Elsevier, volume 58, issue C, DOI: 10.1016/j.najef.2021.101497.
• Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021, "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, volume 58, issue C, DOI: 10.1016/j.najef.2021.101540.
• Lyu, Yifei & Nie, Jun & Yang, Shu-Kuei X., 2021, "Forecasting US economic growth in downturns using cross-country data," Economics Letters, Elsevier, volume 198, issue C, DOI: 10.1016/j.econlet.2020.109668.
• Aguilar, Pablo & Ghirelli, Corinna & Pacce, Matías & Urtasun, Alberto, 2021, "Can news help measure economic sentiment? An application in COVID-19 times," Economics Letters, Elsevier, volume 199, issue C, DOI: 10.1016/j.econlet.2021.109730.
• Qiu, Yue, 2021, "Complete subset least squares support vector regression," Economics Letters, Elsevier, volume 200, issue C, DOI: 10.1016/j.econlet.2021.109737.
• Li, Yiyun & Law, Keith K.F., 2021, "Systematic risk in pairs trading and dynamic parameterization," Economics Letters, Elsevier, volume 202, issue C, DOI: 10.1016/j.econlet.2021.109842.
• Fang, Ming & Taylor, Stephen, 2021, "A machine learning based asset pricing factor model comparison on anomaly portfolios," Economics Letters, Elsevier, volume 204, issue C, DOI: 10.1016/j.econlet.2021.109919.
• Ardia, David & Bluteau, Keven & Kassem, Alaa, 2021, "A century of Economic Policy Uncertainty through the French–Canadian lens," Economics Letters, Elsevier, volume 205, issue C, DOI: 10.1016/j.econlet.2021.109938.
• Du, Zaichao & Pei, Pei, 2021, "A simple and robust counterfactual impact evaluation," Economics Letters, Elsevier, volume 207, issue C, DOI: 10.1016/j.econlet.2021.110015.
• Karmakar, Sayar & Demirer, Riza & Gupta, Rangan, 2021, "Bitcoin mining activity and volatility dynamics in the power market," Economics Letters, Elsevier, volume 209, issue C, DOI: 10.1016/j.econlet.2021.110111.
• Hayashi, Fumio & Tachi, Yuta, 2021, "The nowcast revision analysis extended," Economics Letters, Elsevier, volume 209, issue C, DOI: 10.1016/j.econlet.2021.110112.
• Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021, "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, volume 220, issue 1, pages 2-22, DOI: 10.1016/j.jeconom.2020.08.010.
• Korolev, Ivan, 2021, "Identification and estimation of the SEIRD epidemic model for COVID-19," Journal of Econometrics, Elsevier, volume 220, issue 1, pages 63-85, DOI: 10.1016/j.jeconom.2020.07.038.
• Perera, Indeewara & Silvapulle, Mervyn J., 2021, "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, volume 221, issue 1, pages 1-24, DOI: 10.1016/j.jeconom.2020.01.022.
• Pathak, Parag A. & Shi, Peng, 2021, "How well do structural demand models work? Counterfactual predictions in school choice," Journal of Econometrics, Elsevier, volume 222, issue 1, pages 161-195, DOI: 10.1016/j.jeconom.2020.07.031.
• Mogliani, Matteo & Simoni, Anna, 2021, "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, volume 222, issue 1, pages 833-860, DOI: 10.1016/j.jeconom.2020.07.022.
• Sun, Yuying & Hong, Yongmiao & Lee, Tae-Hwy & Wang, Shouyang & Zhang, Xinyu, 2021, "Time-varying model averaging," Journal of Econometrics, Elsevier, volume 222, issue 2, pages 974-992, DOI: 10.1016/j.jeconom.2020.02.006.
• Liao, Jun & Zou, Guohua & Gao, Yan & Zhang, Xinyu, 2021, "Model averaging prediction for time series models with a diverging number of parameters," Journal of Econometrics, Elsevier, volume 223, issue 1, pages 190-221, DOI: 10.1016/j.jeconom.2020.10.004.
• Su, Jiun-Hua, 2021, "Model selection in utility-maximizing binary prediction," Journal of Econometrics, Elsevier, volume 223, issue 1, pages 96-124, DOI: 10.1016/j.jeconom.2020.07.052.
• Ahsan, Md. Nazmul & Dufour, Jean-Marie, 2021, "Simple estimators and inference for higher-order stochastic volatility models," Journal of Econometrics, Elsevier, volume 224, issue 1, pages 181-197, DOI: 10.1016/j.jeconom.2021.03.008.
• Andersen, Torben G. & Varneskov, Rasmus T., 2021, "Consistent inference for predictive regressions in persistent economic systems," Journal of Econometrics, Elsevier, volume 224, issue 1, pages 215-244, DOI: 10.1016/j.jeconom.2020.04.051.
• Hecq, Alain & Voisin, Elisa, 2021, "Forecasting bubbles with mixed causal-noncausal autoregressive models," Econometrics and Statistics, Elsevier, volume 20, issue C, pages 29-45, DOI: 10.1016/j.ecosta.2020.03.007.
• Hauzenberger, Niko, 2021, "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, volume 20, issue C, pages 87-108, DOI: 10.1016/j.ecosta.2021.06.001.
• Vázquez, Jesús & Aguilar, Pablo, 2021, "Adaptive learning with term structure information," European Economic Review, Elsevier, volume 134, issue C, DOI: 10.1016/j.euroecorev.2021.103689.
• Eckert, Florian & Hyndman, Rob J. & Panagiotelis, Anastasios, 2021, "Forecasting Swiss exports using Bayesian forecast reconciliation," European Journal of Operational Research, Elsevier, volume 291, issue 2, pages 693-710, DOI: 10.1016/j.ejor.2020.09.046.
• Oscar Claveria, 2021, "Uncertainty indicators based on expectations of business and consumer surveys," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, volume 48, issue 2, pages 483-505, May, DOI: 10.1007/s10663-020-09479-1.
• Eduard Baitinger & Samuel Flegel, 2021, "The better turbulence index? Forecasting adverse financial markets regimes with persistent homology," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, volume 35, issue 3, pages 277-308, September, DOI: 10.1007/s11408-020-00377-x.
• Alfred Michael Dockery & Mark N. Harris & Nicholas Holyoak & Ranjodh B. Singh, 2021, "A methodology for projecting sparse populations and its application to remote Indigenous communities," Journal of Geographical Systems, Springer, volume 23, issue 1, pages 37-61, January, DOI: 10.1007/s10109-020-00329-z.
• Niall Newsham & Francisco Rowe, 2021, "Projecting the demographic impact of Syrian migration in a rapidly ageing society, Germany," Journal of Geographical Systems, Springer, volume 23, issue 2, pages 231-261, April, DOI: 10.1007/s10109-018-00290-y.
• Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021, "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, volume 62, issue 2, pages 283-317, February, DOI: 10.1007/s11146-020-09745-w.
• Qingjing Zhang & Taufiq Choudhry & Jing-Ming Kuo & Xiaoquan Liu, 2021, "Does liquidity drive stock market returns? The role of investor risk aversion," Review of Quantitative Finance and Accounting, Springer, volume 57, issue 3, pages 929-958, October, DOI: 10.1007/s11156-021-00966-5.
• Cem Cakmakli & Verda Ozturk, 2021, "Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing," Koç University-TUSIAD Economic Research Forum Working Papers, Koc University-TUSIAD Economic Research Forum, number 2110, Jul.
• Magdalena Cornejo, 2021, "Forecasting crop yields through climate variables using mixed frequency data. The case of Argentine soybeans," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, volume 67, pages 93-106, January-D.
• Alexander Correa, 2021, "Forecasting Tourist Arrivals to Colombia from Google Trends Search Criteria," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 95, pages 105-134, July-Dece, DOI: 10.17533/udea.le.n95a343462.
• Jamal Bouoiyour, Refk Selmi, 2021, "The financial costs of terrorism: evidence from Germany," European Journal of Comparative Economics, Cattaneo University (LIUC), volume 18, issue 1, pages 87-104, June.
• Muhammad Ejaz & Javed Iqbal, 2021, "Estimation and Forecasting of Industrial Production Index," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, volume 26, issue 1, pages 1-30, Jan-June.
• Muhammad Zubair Mumtaz, 2021, "Predicting Stock Indices Trends using Neuro-fuzzy Systems in COVID-19," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, volume 26, issue 2, pages 1-18, July-Dec.
• Boriss Siliverstovs, 2021, "Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth," Working Papers, Latvijas Banka, number 2021/01, Feb.
• Andreï Kostyrka & Dmitry Igorevich Malakhov,, 2021, "The good, the bad, and the asymmetric: Evidence from a new conditional density model," DEM Discussion Paper Series, Department of Economics at the University of Luxembourg, number 21-09.
• Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021, "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers, National Bank of the Republic of North Macedonia, number 2021-03.
• Martin Baumgaertner & Johannes Zahner, 2021, "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202130.
• Erik Heilmann & Janosch Henze & Heike Wetzel, 2021, "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202135.
• Weshah Razzak, 2021, "The Ownership of Oil, Democracy, and Iraq's Past, Present and Future," Discussion Papers, School of Economics and Finance, Massey University, New Zealand, number 2102.
• Yoonseok Lee & Donggyu Sul, 2021, "Depth-Weighted Forecast Combination: Application to COVID-19 Cases," Center for Policy Research Working Papers, Center for Policy Research, Maxwell School, Syracuse University, number 238, Feb.
• João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021, "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, volume 57, issue 15, pages 4312-4329, December, DOI: 10.1080/1540496X.2020.1808458.
• Kadir Özen & Dilem Yıldırım, 2021, "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers, ERC - Economic Research Center, Middle East Technical University, number 2101, Apr, revised Apr 2021.
• Jean-David Fermanian & Dominique Guégan, 2021, "Fair learning with bagging," Documents de travail du Centre d'Economie de la Sorbonne, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, number 21034, Nov.
• George Athanasopoulos & Nikolaos Kourentzes, 2021, "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 10/21.
• Sium Bodha Hannadige & Jiti Gao & Mervyn J Silvapulle & Param Silvapulle, 2021, "Time Series Forecasting Using a Mixture of Stationary and Nonstationary Predictors," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 6/21.
• David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021, "Loss-Based Variational Bayes Prediction," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 8/21.
• Andres Algaba & Samuel Borms & Kris Boudt & Brecht Verbeken, 2021, "Daily news sentiment and monthly surveys: A mixed–frequency dynamic factor model for nowcasting consumer confidence," Working Paper Research, National Bank of Belgium, number 396, Feb.