Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2022

• Thi Huyen Tran & Robert Ślepaczuk, 2022, "Quantile regression analysis to predict GDP distribution using data from the US and UK," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2022-30.
• Abay,Kibrom A. & Yonzan,Nishant & Kurdi,Sikandra Smith & Hirfrfot,Kibrom Tafere, 2022, "Revisiting Poverty Trends and the Role of Social Protection Systems in Africa during theCOVID-19 Pandemic," Policy Research Working Paper Series, The World Bank, number 10172, Sep.
• Kevin Moran & Dalibor Stevanovic & Adam Kader Touré, 2022, "Macroeconomic uncertainty and the COVID‐19 pandemic: Measure and impacts on the Canadian economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, volume 55, issue S1, pages 379-405, February, DOI: 10.1111/caje.12551.
• Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2022, "A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., volume 27, issue 1, pages 384-400, January, DOI: 10.1002/ijfe.2158.
• Mehmet Balcilar & Edmond Berisha & Oğuzhan Çepni & Rangan Gupta, 2022, "The predictive power of the term spread on inequality in the United Kingdom: An empirical analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., volume 27, issue 2, pages 1979-1988, April, DOI: 10.1002/ijfe.2254.
• Malte Knüppel & Fabian Krüger, 2022, "Forecast uncertainty, disagreement, and the linear pool," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 1, pages 23-41, January, DOI: 10.1002/jae.2834.
• Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022, "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 3, pages 461-476, April, DOI: 10.1002/jae.2887.
• Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022, "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 3, pages 583-602, April, DOI: 10.1002/jae.2889.
• Michael P. Clements, 2022, "Individual forecaster perceptions of the persistence of shocks to GDP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 3, pages 640-656, April, DOI: 10.1002/jae.2884.
• Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022, "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 4, pages 700-721, June, DOI: 10.1002/jae.2896.
• Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022, "Nowcasting tail risk to economic activity at a weekly frequency," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 5, pages 843-866, August, DOI: 10.1002/jae.2903.
• Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022, "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 5, pages 920-964, August, DOI: 10.1002/jae.2910.
• Tae‐Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022, "Optimal forecast under structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 5, pages 965-987, August, DOI: 10.1002/jae.2908.
• Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022, "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 6, pages 1230-1255, September, DOI: 10.1002/jae.2923.
• Ana Beatriz Galvão & Michael Owyang, 2022, "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 7, pages 1314-1333, November, DOI: 10.1002/jae.2931.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2022, "Forecasting realized volatility of international REITs: The role of realized skewness and realized kurtosis," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 2, pages 303-315, March, DOI: 10.1002/for.2813.
• Barbara Jarmulska, 2022, "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 3, pages 455-490, April, DOI: 10.1002/for.2806.
• Yongchen Zhao, 2022, "Uncertainty and disagreement of inflation expectations: Evidence from household‐level qualitative survey responses," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 4, pages 810-828, July, DOI: 10.1002/for.2834.
• Philip Hans Franses & Max Welz, 2022, "Evaluating heterogeneous forecasts for vintages of macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 4, pages 829-839, July, DOI: 10.1002/for.2835.
• Stavros Degiannakis, 2022, "Stock market as a nowcasting indicator for real investment," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 5, pages 911-919, August, DOI: 10.1002/for.2838.
• Caroline Jardet & Baptiste Meunier, 2022, "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 6, pages 1181-1200, September, DOI: 10.1002/for.2858.
• Robert A. Hill & Paulo M. M. Rodrigues, 2022, "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 7, pages 1356-1371, November, DOI: 10.1002/for.2877.
• Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar, 2022, "Uncertainty and predictability of real housing returns in the United Kingdom: A regional analysis," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 7, pages 1525-1556, November, DOI: 10.1002/for.2878.
• Alexander Glas & Matthias Hartmann, 2022, "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, volume 13, issue 3, pages 979-1022, July, DOI: 10.3982/QE1703.
• Afees A. Salisu & Rangan Gupta & Riza Demirer, 2022, "A Note On Uncertainty Due To Infectious Diseases And Output Growth Of The United States: A Mixed-Frequency Forecasting Experiment," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., volume 17, issue 02, pages 1-9, June, DOI: 10.1142/S2010495222500099.
• Tayyab Raza Fraz & Samreen Fatima, 2022, "Modeling And Forecasting Volatility Of Stock Market Using Family Of Garch Models: Evidence From Cpec Linked Countries," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., volume 22, issue 01, pages 1-15, March, DOI: 10.1142/S219456592250004X.
• Massimo Guidolin & Alexei G. Orlov, 2022, "Can Investors Benefit from Hedge Fund Strategies? Utility-Based, Out-of-Sample Evidence," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., volume 12, issue 03, pages 1-61, September, DOI: 10.1142/S2010139222500070.
• Hua Chen & Domenico Tarzia & Giovanni Vittorino & Andros Gregoriou, 2022, "Volatility Spillovers During the Chinese Stock Market Crisis: A MEM-Based Approach," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., volume 25, issue 04, pages 1-30, December, DOI: 10.1142/S021909152250031X.
• Lucija Benko & Karlo Krstanović & Luka Sovulj, 2022, "Procjena učinaka pandemije koronavirusa na turističke dolaske i noćenja u Republici Hrvatskoj te na vrijednost CROBEXturist indeksa Zagrebačke burze," EFZG Working Papers Series, Faculty of Economics and Business, University of Zagreb, number 2201, Mar.
• Dreher, Sandra & Eichfelder, Sebastian & Noth, Felix, 2022, "Does IFRS information on tax loss carryforwards and negative performance improve predictions of earnings and cash flows?," arqus Discussion Papers in Quantitative Tax Research, arqus - Arbeitskreis Quantitative Steuerlehre, number 276.
• Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2022, "Addressing COVID-19 outliers in BVARs with stochastic volatility," Discussion Papers, Deutsche Bundesbank, number 13/2022.
• Berger, Tino & Ochsner, Christian, 2022, "Robust real-time estimates of the German output gap based on a multivariate trend-cycle decomposition," Discussion Papers, Deutsche Bundesbank, number 35/2022.
• Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022, "Score-based calibration testing for multivariate forecast distributions," Discussion Papers, Deutsche Bundesbank, number 50/2022.
• Hartwig, Benny, 2022, "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers, Deutsche Bundesbank, number 52/2022.
• Zadrozny, Peter A., 2022, "Linear identification of linear rational-expectations models by exogenous variables reconciles Lucas and Sims," CFS Working Paper Series, Center for Financial Studies (CFS), number 682.
• Benchimol, Jonathan & El-Shagi, Makram & Saadon, Yossi, 2022, "Do expert experience and characteristics affect inflation forecasts?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 201, pages 205-226.
• Hauber, Philipp, 2022, "Real-time nowcasting with sparse factor models," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 251551.
• Dang, Hai-Anh H. & Lanjouw, Peter F., 2022, "Measuring Poverty Dynamics with Synthetic Panels Based on Repeated Cross-Sections," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1213.
• Heinisch, Katja & Holtemöller, Oliver & Lindner, Axel & Sardone, Alessandro & Zeddies, Götz, 2022, "Wirtschaftswachstum, Staatsfinanzen und Treibhausgas-Emissionen in der mittleren Frist," Konjunktur aktuell, Halle Institute for Economic Research (IWH), volume 10, issue 4, pages 146-151.
• Duan, Fang, 2022, "Forecasting risk measures based on structural breaks in the correlation matrix," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 945, DOI: 10.4419/96973106.
• Prüser, Jan & Blagov, Boris, 2022, "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 960, DOI: 10.4419/96973124.
• Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022, "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 964, DOI: 10.4419/96973128.
• du Plessis, Emile & Fritsche, Ulrich, 2022, "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," WiSo-HH Working Paper Series, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory, number 67.
• Zahner, Johannes & Baumgärtner, Martin, 2022, "Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks," VfS Annual Conference 2022 (Basel): Big Data in Economics, Verein für Socialpolitik / German Economic Association, number 264019.
• Stamer, Vincent, 2022, "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics, Verein für Socialpolitik / German Economic Association, number 264096.
• Axenbeck, Janna & Breithaupt, Patrick, 2022, "Measuring the digitalisation of firms: A novel text mining approach," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 22-065.
• Janusz Opi³a, 2022, "On Employing of Extended Characteristic Surface Model for Forecasting of Demand in Tourism," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, volume 20, issue 5, pages 621-639.
• Iqbal Jebril & P. Dhanaraj & Ghaida Muttashar Abdulsahib & SatheeshKumar Palanisamy & T.Prabhu & Osamah Ibrahim Khalaf, 2022, "Analysis of Electrically Couple SRR EBG Structure for Sub 6 GHz Wireless Applications," Advances in Decision Sciences, Asia University, Taiwan, volume 26, issue Special, pages 102-123, December.
• Mikkel Bennedsen & Eric Hillebrand & Sebastian Jensen, 2022, "A Neural Network Approach to the Environmental Kuznets Curve," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2022-09, May.
• Sarthak Behera & Hyeongwoo Kim & Soohyon Kim, 2022, "Superior Predictability of American Factors of the Won/Dollar Real Exchange Rate," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2022-03, Jul.
• Christian Gourieroux & Joann Jasiak, 2022, "Long Run Predictions," Annals of Economics and Statistics, GENES, issue 145, pages 75-90, DOI: https://doi.org/10.2307/48655902.
• Paul Beaudry & Tim Willems, 2022, "On the Macroeconomic Consequences of Over-Optimism," American Economic Journal: Macroeconomics, American Economic Association, volume 14, issue 1, pages 38-59, January, DOI: 10.1257/mac.20190332.
• Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2022, "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from The Lasso Regularization and Inferential Techniques," Working Papers of the African Governance and Development Institute., African Governance and Development Institute., number 22/061, Jan.
• Bekir Tamer Gökalp, 2022, "Kripto Para Piyasasının Borsa İstanbul Endeksleri Üzerindeki Etkileri," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 7, issue 2, pages 481-499, DOI: 10.30784/epfad.1081705.
• Ilya V. Naumov & Sergey S. Krasnykh & Yulia S. Otmakhova, 2022, "Scenario forecasting of the socio-economic consequences of the COVID-19 pandemic in Russian regions," R-Economy, Ural Federal University, Graduate School of Economics and Management, volume 8, issue 1, pages 5-20, DOI: https://doi.org/10.15826/recon.2022.
• Iania, Leonardo & Algieri, Bernardina & Leccadito, Arturo, 2022, "Forecasting total energy’s CO2 emissions," LIDAM Discussion Papers LFIN, Université catholique de Louvain, Louvain Finance (LFIN), number 2022003, May.
• Darío Ezequiel Díaz, 2022, "El Progreso social, el individualismo y el enfoque de capacidades: El rol de las estructuras sociales, grupos e instituciones," Revista de Economía Política de Buenos Aires, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET), volume 16, issue 25, pages 9-36, October, DOI: https://doi.org/10.56503/repba.Nro..
• Nuno Goncalves, 2022, "Most probable or more prudent? Analysing CFP's macroeconomic projections, 2015-2019," CFP Occasional Papers, Portuguese Public Finance Council, number 02/2022, Apr.
• Lafond, François & Farmer, J. Doyne & Mungo, Luca & Astudillo-Estévez, Pablo, 2022, "Reconstructing production networks using machine learning," INET Oxford Working Papers, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, number 2022-02, Jan, revised Jan 2023.
• Kaiser, Caspar & Oparina, Ekaterina & Gentile, Niccolò & Tkatchenko, Alexandre & Clark, Andrew E. & De Neve, Jan-Emmanuel & D’Ambrosio, Conchita, 2022, "Human Wellbeing and Machine Learning," INET Oxford Working Papers, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, number 2022-11, Jun.
• Surbhi Bhatia & Manish K. Singh, 2022, "Fifty years since Altman (1968): Performance of financial distress prediction models," Working Papers, xKDR, number 12, Jun.
• Alexander David & Pietro Veronesi, 2022, "A Survey of Alternative Measures of Macroeconomic Uncertainty: Which Measures Forecast Real Variables and Explain Fluctuations in Asset Volatilities Better?," Annual Review of Financial Economics, Annual Reviews, volume 14, issue 1, pages 439-463, November, DOI: 10.1146/annurev-financial-111720-09.
• Kerry Loaiza-Marín, 2022, "Nowcasting the Costa Rican Quarterly Output Growth," Documentos de Trabajo, Banco Central de Costa Rica, number 2107, Feb.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022, "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 202207, Jul, revised Jul 2022.
• Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2022, "Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices," Papers, arXiv.org, number 2201.05556, Jan, revised Mar 2023.
• Davide Fiaschi & Cristina Tealdi, 2022, "The attachment of adult women to the Italian labour market in the shadow of COVID-19," Papers, arXiv.org, number 2202.13317, Feb, revised May 2023.
• Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022, "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers, arXiv.org, number 2202.13793, Feb.
• Francis X. Diebold & Glenn D. Rudebusch & Maximilian Goebel & Philippe Goulet Coulombe & Boyuan Zhang, 2022, "When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," Papers, arXiv.org, number 2203.04040, Mar, revised May 2023.
• Alain Hecq & Joao Issler & Elisa Voisin, 2022, "A short term credibility index for central banks under inflation targeting: an application to Brazil," Papers, arXiv.org, number 2205.00924, May, revised Jul 2022.
• Ekaterina Oparina & Caspar Kaiser & Niccol`o Gentile & Alexandre Tkatchenko & Andrew E. Clark & Jan-Emmanuel De Neve & Conchita D'Ambrosio, 2022, "Human Wellbeing and Machine Learning," Papers, arXiv.org, number 2206.00574, Jun.
• Dimitris Korobilis, 2022, "A new algorithm for structural restrictions in Bayesian vector autoregressions," Papers, arXiv.org, number 2206.06892, Jun.
• Francis X. Diebold & Maximilian Goebel & 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," Papers, arXiv.org, number 2206.10721, Jun, revised Jun 2023.
• Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022, "Distributional neural networks for electricity price forecasting," Papers, arXiv.org, number 2207.02832, Jul, revised Dec 2022.
• Gaetan Bakalli & St'ephane Guerrier & Olivier Scaillet, 2022, "A penalized two-pass regression to predict stock returns with time-varying risk premia," Papers, arXiv.org, number 2208.00972, Aug.
• James T. E. Chapman & Ajit Desai, 2022, "Macroeconomic Predictions using Payments Data and Machine Learning," Papers, arXiv.org, number 2209.00948, Sep.
• 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.
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• 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.
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• 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.
• Congressional Budget Office, 2022, "The Accuracy of CBO’s Budget Projections for Fiscal Year 2021," Reports, Congressional Budget Office, number 57614, Jan.
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• Congressional Budget Office, 2022, "Quantifying the Uncertainty of Long-Term Economic Projections: Working Paper 2022-07," Working Papers, Congressional Budget Office, number 57711, Apr.
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• 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.
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• 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.
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• M. Hashem Pesaran & Andreas Pick & Allan Timmermann, 2022, "Forecasting With Panel Data: Estimation Uncertainty Versus Parameter Heterogeneity," CESifo Working Paper Series, CESifo, number 9690.
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• 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.
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• Laurent Ferrara & Joseph Yapi, 2022, "Measuring exchange rate risks during periods of uncertainty," International Economics, CEPII research center, issue 170, pages 202-212.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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