Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2022

• Reza Bradrania & Davood Pirayesh Neghab, 2022, "State-dependent Asset Allocation Using Neural Networks," Papers, arXiv.org, number 2211.00871, Nov.
• Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022, "Bayesian Neural Networks for Macroeconomic Analysis," Papers, arXiv.org, number 2211.04752, Nov, revised Apr 2024.
• Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022, "Score-based calibration testing for multivariate forecast distributions," Papers, arXiv.org, number 2211.16362, Nov, revised Dec 2023.
• Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022, "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers, arXiv.org, number 2212.03471, Dec, revised Jul 2023.
• Hematy, Maryam, 2022, "Comparative Study of Phillips Curve under Dual-stickiness Model Considering Heterogeneity in Iran\'s Economy (in Persian)," The Journal of Planning and Budgeting (٠صلنامه برنامه ریزی Ùˆ بودجه), Institute for Management and Planning studies, volume 27, issue 3, pages 27-74, December.
• Viktor Dzis & Olena Dyachynska, 2022, "Simulation Modeling Of Investment Projects In The Service Sector," Three Seas Economic Journal, Publishing house "Baltija Publishing", volume 3, issue 1, DOI: 10.30525/2661-5150/2022-1-9.
• Marianna Stehnei & Inna Irtyshcheva & Halyna Mykhalchynets, 2022, "Development Of The Financial Market: Destabilizing Processes, Their Assessment, And Global Impact," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", volume 8, issue 5, DOI: 10.30525/2256-0742/2022-8-5-176-183.
• Mihail Yanchev, 2022, "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.
• Svitlana Cheremisina & Volodumur Rossokha & Olena Mazurenko & Mykhailo Selinnyi & Olha Tomashevska, 2022, "The Grain Market of Ukraine: Actual State, Current Problems, and Development Prospects," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 172-187.
• Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022, "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 22-04, Jul.
• Massimiliano Marcellino & Dalibor Stevanovic, 2022, "The demand and supply of information about inflation," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 22-06, Aug, revised Nov 2022.
• Tony Chernis & Taylor Webley, 2022, "Nowcasting Canadian GDP with Density Combinations," Discussion Papers, Bank of Canada, number 2022-12, May, DOI: 10.34989/sdp-2022-12.
• James Younker, 2022, "Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models," Discussion Papers, Bank of Canada, number 2022-19, Sep, DOI: 10.34989/sdp-2022-19.
• Hossein Hosseini & Craig Johnston & Craig Logan & Miguel Molico & Xiangjin Shen & Marie-Christine Tremblay, 2022, "Assessing Climate-Related Financial Risk: Guide to Implementation of Methods," Technical Reports, Bank of Canada, number 120, DOI: 10.34989/tr-120.
• Gabriel Bruneau & Thibaut Duprey & Ruben Hipp, 2022, "Forecasting Banks’ Corporate Loan Losses Under Stress: A New Corporate Default Model," Technical Reports, Bank of Canada, number 122, DOI: 10.34989/tr-122.
• James Chapman & Ajit Desai, 2022, "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers, Bank of Canada, number 22-10, Mar, DOI: 10.34989/swp-2022-10.
• Gustavo Silva Araujo & Wagner Piazza Gaglianone, 2022, "Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models," Working Papers Series, Central Bank of Brazil, Research Department, number 561, Jul.
• Emilio Blanco & Fiorella Dogliolo & Lorena Garegnani, 2022, "Nowcasting during the Pandemic: Lessons from Argentina," BCRA Working Paper Series, Central Bank of Argentina, Economic Research Department, number 202299, Mar.
• Celal OZTURK & Cemal IBIS, 2022, "Behavioral Modelling of Non-Maturing and Time Deposits in Liquidity Risk Management," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 16, issue 1, pages 1-26.
• Fructuoso Borrallo & Lucía Cuadro-Sáez & Javier J. Pérez, 2022, "El aumento de los precios de las materias primas alimenticias y su traslación a los precios de consumo en el área del euro," Boletín Económico, Banco de España, issue 3/2022.
• Fructuoso Borrallo & Lucía Cuadro-Sáez & Javier J. Pérez, 2022, "Rising food commodity pricesand their pass-through to euro area consumer prices," Economic Bulletin, Banco de España, issue 3/2022.
• Donato Ceci & Andrea Silvestrini, 2022, "Nowcasting the state of the Italian economy: the role of financial markets," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1362, Feb.
• Arango-Castillo Lenin & Orraca María José & Molina Martínez G. Stefano, 2022, "The Influence of Global Inflation on Emerging Market Economies' Inflation," Working Papers, Banco de México, number 2022-15, Nov.
• María Alejandra Hernández-Montes & Ramón Hernández-Ortega & Jonathan Alexander Muñoz-Martínez, 2022, "Aporte de las expectativas de empresarios al pronóstico de las variables macroeconómicas," Borradores de Economia, Banco de la Republica de Colombia, number 1202, Jun, DOI: 10.32468/be.1202.
• Camilo Eduardo Sánchez-Quinto, 2022, "SRISK: una medida de riesgo sistémico para la banca colombiana 2005-2021," Borradores de Economia, Banco de la Republica de Colombia, number 1207, Sep, DOI: 10.32468/be.1207.
• Youssef Ulgazi & Paul Vertier, 2022, "Forecasting Inflation in France: an Update of MAPI," Working papers, Banque de France, number 869.
• Arthur Stalla-Bourdillon, 2022, "Stock Return Predictability: comparing Macro- and Micro-Approaches," Working papers, Banque de France, number 891.
• Stephen G. Hall & George S. Tavlas & Yongli Wang, 2022, "Forecasting Inflation: The Use of Dynamic Factor Analysis and Nonlinear Combinations," Discussion Papers, Department of Economics, University of Birmingham, number 22-12, Oct.
• Stephen G. Hall & George S. Tavlas & Yongli Wang, 2022, "Drivers and Spillover Effects of Inflation: the United States, the Euro Area, and the United Kingdom," Discussion Papers, Department of Economics, University of Birmingham, number 22-13, Oct.
• Ana Aguilar & María Diego-Fernández & Rocio Elizondo & Jessica Roldán-Peña, 2022, "Term premium dynamics and its determinants: the Mexican case," BIS Working Papers, Bank for International Settlements, number 993, Jan.
• Londhe Sanket Tanaji & Palwe Sushila, 2022, "Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach," Business Systems Research, Sciendo, volume 13, issue 1, pages 35-45, June, DOI: 10.2478/bsrj-2022-0003.
• Aleksei Kipriyanov, 2022, "Comparison of Models for Growth-at-Risk Forecasting," Russian Journal of Money and Finance, Bank of Russia, volume 81, issue 1, pages 23-45, March, DOI: 10.31477/rjmf.202201.23.
• Sergey Ivashchenko, 2022, "Dynamic Stochastic General Equilibrium Model with Multiple Trends and Structural Breaks," Russian Journal of Money and Finance, Bank of Russia, volume 81, issue 1, pages 46-72, March, DOI: 10.31477/rjmf.202201.46.
• Urmat Dzhunkeev, 2022, "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, volume 81, issue 1, pages 73-87, March, DOI: 10.31477/rjmf.202201.73.
• Evgeny Moiseev & Denis Zagorodnev & Alexander Berezinskiy & Roman Tikhonov, 2022, "A Method for Assessing the IT Component of Model Risk and the Economic Capital to Cover It," Russian Journal of Money and Finance, Bank of Russia, volume 81, issue 3, pages 107-127, September.
• Elena Shulyak, 2022, "Macroeconomic Forecasting Using Data from Social Media," Russian Journal of Money and Finance, Bank of Russia, volume 81, issue 4, pages 86-112, December.
• Santiago Etchegaray Alvarez, 2022, "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo, Banco Central del Uruguay, number 2022004.
• Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022, "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, volume 51, issue 1, February, DOI: 10.1111/ecno.12193.
• Aman Ullah & Tao Wang & Weixin Yao, 2022, "Nonlinear modal regression for dependent data with application for predicting COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, volume 185, issue 3, pages 1424-1453, July, DOI: 10.1111/rssa.12849.
• Tae‐Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022, "Forecasting Under Structural Breaks Using Improved Weighted Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, volume 84, issue 6, pages 1485-1501, December, DOI: 10.1111/obes.12512.
• Christian Estmann & Bjørn Bo Sørensen & Benno Ndulu & John Rand, 2022, "Merchandise export diversification strategy for Tanzania: Promoting inclusive growth, economic complexity and structural change," The World Economy, Wiley Blackwell, volume 45, issue 8, pages 2649-2695, August, DOI: 10.1111/twec.13255.
• Juan Carlos Carlo Santos, 2022, "Modelos nowcasting para la estimación del PIB: un análisis por sector económico para Bolivia," Revista de Análisis del BCB, Banco Central de Bolivia, volume 37, issue 1, pages 57-87, July - De.
• Kenneth Sæterhagen Paulsen & Tuva Marie Fastbø & Tobias Ingebrigtsen, 2022, "Aggregate density forecast of models using disaggregate data - A copula approach," Working Paper, Norges Bank, number 2022/5, Apr.
• Ida Nervik Hjelseth & Arvid Raknerud & Bjørn H. Vatne, 2022, "A bankruptcy probability model for assessing credit risk on corporate loans with automated variable selection," Working Paper, Norges Bank, number 2022/7, Jun.
• Barbara Rossi, 2022, "Local projections in unstable environments: How effective is fiscal policy?," Economics Virtual Symposium 2022, Stata Users Group, number 02, Nov.
• Marcus Buckmann & Andreas Joseph, 2022, "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers, Bank of England, number 984, Jun.
• Miguel Ampudia & Filippo Busetto & Fabio Fornari, 2022, "Chronicle of a death foretold: does higher volatility anticipate corporate default?," Bank of England working papers, Bank of England, number 1001, Oct.
• George Filis & Stavros Degiannakis & Zacharias Bragoudakis, 2022, "Forecasting macroeconomic indicators for Eurozone and Greece: How useful are the oil price assumptions?," Working Papers, Bank of Greece, number 296, Apr, DOI: 10.52903/wp2022296.
• Alexandros E. Milionis & Nikolaos G. Galanopoulos & Peter Hatzopoulos & Aliki Sagianou, 2022, "Forecasting actuarial time series: a practical study of the effect of statistical pre-adjustments," Working Papers, Bank of Greece, number 297, May, DOI: 10.52903/wp2022297.
• Stephen G. Hall & George S. Tavlas & Yongli Wang, 2022, "Drivers and spillover effects of inflation: the United States, the euro area, and the United Kingdom," Working Papers, Bank of Greece, number 309, Dec, DOI: 10.52903/wp2022309.
• Takashi Nakazawa, 2022, "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series, Bank of Japan, number 22-E-9, Jul.
• Shang Han Lin & Zhang Xibin, 2022, "Bayesian bandwidth estimation for local linear fitting in nonparametric regression models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 26, issue 1, pages 55-71, February, DOI: 10.1515/snde-2018-0050.
• Segnon Mawuli & Lau Chi Keung & Wilfling Bernd & Gupta Rangan, 2022, "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 26, issue 1, pages 73-98, February, DOI: 10.1515/snde-2019-0009.
• Li Leon & Scrimgeour Frank, 2022, "The co-integration of CDS and bonds in time-varying volatility dynamics: do credit risk swaps lower bond risks?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 26, issue 3, pages 475-497, June, DOI: 10.1515/snde-2019-0141.
• Neto Alberto Ronchi & Candido Osvaldo, 2022, "What does Google say about credit developments in Brazil?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 26, issue 4, pages 499-527, September, DOI: 10.1515/snde-2019-0122.
• Sulaimon Olanrewaju ADEBIYI & Oluwayemisi Temitope SODOLAMU, 2022, "Application Of Autoregressive Integrated Moving Average And Holt Winters Methods For Optimum Sales Forecasting In The Manufacturing Sector," Contemporary Economy Journal, Constantin Brancoveanu University, volume 7, issue 2, pages 161-173.
• Pesaran, M. H. & Pick, A. & Timmermann, A., 2022, "Forecasting with panel data: estimation uncertainty versus parameter heterogeneity," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2219, Mar.
• Mueller, H. & Rauh, C., 2022, "Using Past Violence and Current News to Predict Changes in Violence," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2220, Mar.
• Mueller, H. & Rauh, C., 2022, "Using Past Violence and Current News to Predict Changes in Violence," Janeway Institute Working Papers, Faculty of Economics, University of Cambridge, number 2209, Mar.
• Congressional Budget Office, 2022, "The Accuracy of CBO’s Budget Projections for Fiscal Year 2021," Reports, Congressional Budget Office, number 57614, Jan.
• Congressional Budget Office, 2022, "A Markov-Switching Model of the Unemployment Rate: Working Paper 2022-05," Working Papers, Congressional Budget Office, number 57582, Mar.
• Congressional Budget Office, 2022, "Quantifying the Uncertainty of Long-Term Economic Projections: Working Paper 2022-07," Working Papers, Congressional Budget Office, number 57711, Apr.
• Zhongchen Song & Tom Coupé, 2022, "Predicting Chinese consumption series with Baidu," Working Papers in Economics, University of Canterbury, Department of Economics and Finance, number 22/19, Dec.
• Xu, Yongdeng, 2022, "The Exponential HEAVY Model: An Improved Approach to Volatility Modeling and Forecasting," Cardiff Economics Working Papers, Cardiff University, Cardiff Business School, Economics Section, number E2022/5, Mar.
• Ekaterina Oparina & Caspar Kaiser & Niccolo Gentile & Alexandre Tkatchenko & Andrew E. Clark & Jan-Emmanuel De Neve & Conchita D'Ambrosio, 2022, "Human wellbeing and machine learning," CEP Discussion Papers, Centre for Economic Performance, LSE, number dp1863, Jul.
• Renato Molina & Ivan Rudik, 2022, "The Social Value of Predicting Hurricanes," CESifo Working Paper Series, CESifo, number 10049.
• Peter A. Zadrozny, 2022, "Linear Identification of Linear Rational-Expectations Models by Exogenous Variables Reconciles Lucas and Sims," CESifo Working Paper Series, CESifo, number 10078.
• Klaus Abberger & Michael Graff & Oliver Müller & Boriss Siliverstovs, 2022, "Imputing Monthly Values for Quarterly Time Series. An Application Performed with Swiss Business Cycle Data," CESifo Working Paper Series, CESifo, number 10191.
• Benjamin Born & Zeno Enders & Manuel Menkhoff & Gernot J. Müller & Knut Niemann & Gernot Müller, 2022, "Firm Expectations and News: Micro v Macro," CESifo Working Paper Series, CESifo, number 10192.
• Constantin Bürgi & Julio L. Ortiz, 2022, "Overreaction through Anchoring," CESifo Working Paper Series, CESifo, number 10193.
• Christina Anderl & Guglielmo Maria Caporale, 2022, "Forecasting Inflation with a Zero Lower Bound or Negative Interest Rates: Evidence from Point and Density Forecasts," CESifo Working Paper Series, CESifo, number 9687.
• M. Hashem Pesaran & Andreas Pick & Allan Timmermann, 2022, "Forecasting With Panel Data: Estimation Uncertainty Versus Parameter Heterogeneity," CESifo Working Paper Series, CESifo, number 9690.
• Robert Lehmann & Ida Wikman, 2022, "Quarterly GDP Estimates for the German States," ifo Working Paper Series, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 370.
• Didier Sornette & Florian Ulmann & Alexander Wehrli, 2022, "On the Directional Destabilizing Feedback Effects of Option Hedging," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-34, Apr.
• Dongshuai Zhao & Zhongli Wang & Florian Schweizer-Gamborino & Didier Sornette, 2022, "Polytope Fraud Theory," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-41, May.
• Soros Chitsiripanich & Marc S. Paolella & Pawel Polak & Patrick S. Walker, 2022, "Momentum Without Crashes," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-87, Nov.
• Marc S. Paolella & Pawel Polak, 2022, "Density and Risk Prediction with Non-Gaussian COMFORT Models," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-88, Nov.
• Dan Zhang & Arash Farnoosh & Frédéric Lantz, 2022, "Does something change in the oil market with the COVID-19 crisis?," International Economics, CEPII research center, issue 169, pages 252-268.
• Laurent Ferrara & Joseph Yapi, 2022, "Measuring exchange rate risks during periods of uncertainty," International Economics, CEPII research center, issue 170, pages 202-212.
• Massimiliano Marcellino & Dalibor Stevanovic, 2022, "The demand and supply of information about inflation," CIRANO Working Papers, CIRANO, number 2022s-27, Dec.
• Robert Ambrisko, 2022, "Nowcasting Macroeconomic Variables Using High-Frequency Fiscal Data," Working Papers, Czech National Bank, Research and Statistics Department, number 2022/5, Jun.
• Laura Vanessa Hern√°ndez Cruz, 2022, "Crisis empresarial en Colombia: probabilidad de entrar en proceso de insolvencia 2016-2019," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 20126, May.
• Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022, "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper, CPB Netherlands Bureau for Economic Policy Analysis, number 441, Oct, DOI: 10.34932/01mq-sn15.
• Bai, Yu & Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022, "Macroeconomic Forecasting in a Multi-country Context," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16994, Feb.
• Hasenzagl, Thomas & Pellegrino, Filippo & Reichlin, Lucrezia & Ricco, Giovanni, 2022, "Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17111, Mar.
• Pesaran, M. Hashem & Pick, Andreas & Timmermann, Allan, 2022, "Forecasting with panel data: estimation uncertainty versus parameter heterogeneity," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17123, Mar.
• Inoue, Atsushi & Rossi, Barbara & Wang, Yiru, 2022, "Local Projections in Unstable Environments: How Effective is Fiscal Policy?," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17134, Mar.
• Andreou, Elena & Gagliardini, Patrick & Ghysels, Eric & Rubin, Mirco, 2022, "Three Common Factors," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17225, Apr.
• Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2022, "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17461, Jul.
• Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022, "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17512, Jul.
• Born, Benjamin & Enders, Zeno & Menkhoff, Manuel & Müller, Gernot & Niemann, Knut, 2022, "Firm Expectations and News: Micro v Macro," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17768, Dec.
• Tino Berger & Tore Dubbert, 2022, "Government spending effects on the business cycle in times of crisis," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster, number 10022, Dec.
• Verena Monschang & Bernd Wilfling, 2022, "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster, number 9722, Mar.
• Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022, "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster, number 9922, Jun.
• Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2022, "Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices," Working Papers, Center for Research in Economics and Statistics, number 2023-06, Mar.
• Merlin Stein, 2022, "When are large female-led firms more resilient against shocks? Learnings from Indian enterprises during COVID-19 with diff-in-diff and causal forests," CSAE Working Paper Series, Centre for the Study of African Economies, University of Oxford, number 2022-01, Jan.
• Blazsek, Szabolcs & Escribano, Álvaro, 2022, "Score-driven threshold ice-age models: benchmark models for long-run climate forecasts," UC3M Working papers. Economics, Universidad Carlos III de Madrid. Departamento de Economía, number 34757, May.
• Huboh Samuel RINGMU & Saidou Baba OUMAR, 2022, "Forecasting stock prices in the New York stock exchange," Journal of Economics Bibliography, EconSciences Journals, volume 9, issue 1, pages 1-20, March.
• Lukas Boer, 2022, "Steigende Metallpreise als mögliches Hindernis der Energiewende," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, volume 89, issue 4, pages 47-55.
• Dennis Kant & Andreas Pick & Jasper de Winter, 2022, "Nowcasting GDP using machine learning methods," Working Papers, DNB, number 754, Nov.
• Chahad, Mohammed & Hofmann-Drahonsky, Anna-Camilla & Page, Adrian & Tirpák, Marcel & Meunier, Baptiste, 2022, "What explains recent errors in the inflation projections of Eurosystem and ECB staff?," Economic Bulletin Boxes, European Central Bank, volume 3.
• Hoffreumon, Charles & Labhard, Vincent, 2022, "Cross-country cross-technology digitalisation: a Bayesian hierarchical model perspective," Working Paper Series, European Central Bank, number 2700, Aug.
• Ampudia, Miguel & Busetto, Filippo & Fornari, Fabio, 2022, "Chronicle of a death foretold: does higher volatility anticipate corporate default?," Working Paper Series, European Central Bank, number 2749, Nov.
• Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2022, "Conditional density forecasting: a tempered importance sampling approach," Working Paper Series, European Central Bank, number 2754, Dec.
• Hammadi Zouari, 2022, "On the Effectiveness of Stock Index Futures for Tail Risk Protection," International Journal of Economics and Financial Issues, Econjournals, volume 12, issue 3, pages 38-52, May.
• Bianca Reichert & Adriano Mendon a Souza, 2022, "Can the Heston Model Forecast Energy Generation? A Systematic Literature Review," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 1, pages 289-295.
• Ambya Ambya & Lies Maria Hamzah, 2022, "Indonesian Coal Exports: Dynamic Panel Analysis Approach," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 1, pages 390-395.
• Edwaren Liun & Suparman Suparman & Sriyana Sriyana & Dharu Dewi & Jupiter Sitorus Pane, 2022, "Indonesia s Energy Demand Projection Until 2060," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 2, pages 467-473, March.
• Mustofa Usman & Luvita Loves & Edwin Russel & Muslim Ansori & Warsono Warsono & Widiarti Widiarti & Wamiliana Wamiliana, 2022, "Analysis of Some Energy and Economics Variables by Using VECMX Model in Indonesia," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 2, pages 91-102, March.
• Bharat Kumar Meher & Iqbal Thonse Hawaldar & Santosh Kumar & Abhishek Kumar Gupta, 2022, "Modelling Market Indices, Commodity Market Prices and Stock Prices of Energy Sector using VAR with Variance Decomposition Model," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 4, pages 122-130, July.
• Mustofa Usman & M. Komarudin & Munti Sarida & Wamiliana Wamiliana & Edwin Russel & Mahatma Kufepaksi & Iskandar Ali Alam & Faiz A.M. Elfaki, 2022, "Analysis of Some Variable Energy Companies by Using VAR(p)-GARCH(r,s) Model : Study From Energy Companies of Qatar over the Years 2015 2022," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 5, pages 178-191, September.
• Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022, "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, volume 306, issue PA, DOI: 10.1016/j.apenergy.2021.117983.
• Simionescu, Mihaela, 2022, "Stochastic convergence in per capita energy use in the EU-15 countries. The role of economic growth," Applied Energy, Elsevier, volume 322, issue C, DOI: 10.1016/j.apenergy.2022.119489.
• Wichitaksorn, Nuttanan, 2022, "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, volume 78, issue C, DOI: 10.1016/j.asieco.2021.101421.
• Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022, "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, volume 134, issue C, DOI: 10.1016/j.jedc.2021.104278.
• Broer, Tobias & Kohlhas, Alexandre N. & Mitman, Kurt & Schlafmann, Kathrin, 2022, "On the possibility of Krusell-Smith Equilibria," Journal of Economic Dynamics and Control, Elsevier, volume 141, issue C, DOI: 10.1016/j.jedc.2022.104391.
• Pfarrhofer, Michael, 2022, "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, volume 143, issue C, DOI: 10.1016/j.jedc.2022.104493.
• Gouriéroux, C. & Monfort, A. & Renne, J.-P., 2022, "Required Capital for Long-Run Risks," Journal of Economic Dynamics and Control, Elsevier, volume 144, issue C, DOI: 10.1016/j.jedc.2022.104502.
• Tsuchiya, Yoichi, 2022, "Evaluating the European Central Bank’s uncertainty forecasts," Economic Analysis and Policy, Elsevier, volume 73, issue C, pages 321-330, DOI: 10.1016/j.eap.2021.12.005.
• Spielauer, Martin & Horvath, Thomas & Fink, Marian & Abio, Gemma & Souto, Guadalupe & Patxot, Ció & Istenič, Tanja, 2022, "Measuring the lifecycle impact of welfare state policies in the face of ageing," Economic Analysis and Policy, Elsevier, volume 75, issue C, pages 1-25, DOI: 10.1016/j.eap.2022.05.002.
• Naimoli, Antonio & Gerlach, Richard & Storti, Giuseppe, 2022, "Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators," Economic Modelling, Elsevier, volume 107, issue C, DOI: 10.1016/j.econmod.2021.105701.
• Jang, Hyuna & Kim, Jong-Min & Noh, Hohsuk, 2022, "Vine copula Granger causality in mean," Economic Modelling, Elsevier, volume 109, issue C, DOI: 10.1016/j.econmod.2022.105798.
• Yang, Yanlin & Hu, Xuemei & Jiang, Huifeng, 2022, "Group penalized logistic regressions predict up and down trends for stock prices," The North American Journal of Economics and Finance, Elsevier, volume 59, issue C, DOI: 10.1016/j.najef.2021.101564.
• Salisu, Afees A. & Gupta, Rangan & Pierdzioch, Christian, 2022, "Predictability of tail risks of Canada and the U.S. Over a Century: The role of spillovers and oil tail Risks☆," The North American Journal of Economics and Finance, Elsevier, volume 59, issue C, DOI: 10.1016/j.najef.2021.101620.
• Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022, "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, volume 60, issue C, DOI: 10.1016/j.najef.2022.101669.
• 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.
• María Paula Bonel & Daniel J. Aromí, 2021, "Assessing GDP forecasts from autoregressive models: the impact of model complexity and training dataset," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4440, Nov.
• Pacheco & Riquelme, 2021, "¿Cómo se siente el presidente?," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4500, Nov.
• 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.