Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2020

• Jyldyz Djumalieva & Stef Garasto & Cath Sleeman, 2020, "Evaluating a new earnings indicator. Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts?," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2020-19, Dec.
• Nicolas Woloszko, 2020, "Adaptive Trees: a new approach to economic forecasting," OECD Economics Department Working Papers, OECD Publishing, number 1593, Jan, DOI: 10.1787/5569a0aa-en.
• Nicolas Woloszko, 2020, "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers, OECD Publishing, number 1634, Dec, DOI: 10.1787/6b9c7518-en.
• Raphaela Hyee & Herwig Immervoll & Rodrigo Fernandez & Jongmi Lee, 2020, "How reliable are social safety nets?: Value and accessibility in situations of acute economic need," OECD Social, Employment and Migration Working Papers, OECD Publishing, number 252, Dec, DOI: 10.1787/65a269a3-en.
• Jan Sebo & Daniela Dankova & Ivan Kralik, 2020, "Projecting A Life-Cycle Income - A Simulation Model For The Slovak Pension Benefit Statement," OLSZTYN ECONOMIC JOURNAL, University of Warmia and Mazury in Olsztyn, Faculty of Economic Sciences, volume 15, issue 4, pages 271-284, December, DOI: https://doi.org/10.31648/oej.6380.
• Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020, "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," SocArXiv, Center for Open Science, number 2bgqs, Oct, DOI: 10.31219/osf.io/2bgqs.
• Fabrizio Cipollini & Giampiero M Gallo & Alessandro Palandri, 2020, "Realized Variance Modeling: Decoupling Forecasting from Estimation," Journal of Financial Econometrics, Oxford University Press, volume 18, issue 3, pages 532-555.
• Axel Bücher & Peter N Posch & Philipp Schmidtke, 2020, "Using the Extremal Index for Value-at-Risk Backtesting," Journal of Financial Econometrics, Oxford University Press, volume 18, issue 3, pages 556-584.
• Elena Andreou & Patrick Gagliardini & Eric Ghysels & Mirco Rubin, 2020, "Mixed-Frequency Macro–Finance Factor Models: Theory and Applications," Journal of Financial Econometrics, Oxford University Press, volume 18, issue 3, pages 585-628.
• Matthew W Clance & Riza Demirer & Rangan Gupta & Clement Kweku Kyei, 2020, "Predicting firm-level volatility in the United States: the role of monetary policy uncertainty," Economics and Business Letters, Oviedo University Press, volume 9, issue 3, pages 167-177.
• Sander Barendse & Andrew J. Patton, 2020, "Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter," Economics Series Working Papers, University of Oxford, Department of Economics, number 909, May.
• Cevallos-Valdiviezo, Holger & Rodríguez-Cristiansen, Ariana & Valdiviezo-Valenzuela, Patricia & Arévalo-Avecillas, Danny & Padilla-Lozano, Carmen, 2020, "Predicción del nivel de cosecha de camarón blanco: el caso de una pequeña camaronera en la parroquia Tenguel del cantón Guayaquil, Ecuador || Prediction of white shrimp harvest: the case of a small shrimp farm in Tenguel, Guayaquil-Ecuador," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 30, issue 1, pages 227-257, December, DOI: 10.46661/revmetodoscuanteconempresa.
• Harris Ntantanis & Lawrence Pohlman, 2020, "Market implied GDP," Journal of Asset Management, Palgrave Macmillan, volume 21, issue 7, pages 636-646, December, DOI: 10.1057/s41260-020-00176-z.
• Andrés Berenguer & Luis Gandarias & Álvaro Arévalo, 2020, "Singular spectrum analysis for modelling the hard-to-model risk factors," Risk Management, Palgrave Macmillan, volume 22, issue 3, pages 178-191, September, DOI: 10.1057/s41283-020-00060-5.
• Glyn Wittwer & Kym Anderson, 2020, "A model of global beverage markets," Departmental Working Papers, The Australian National University, Arndt-Corden Department of Economics, number 2020-05.
• Roberto S. Mariano & Suleyman Ozmucur, 2020, "Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)Abstract: We study how the separation of time and risk preferences relates to a behavioral property that generalizes ," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 20-029, Aug.
• Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020, "Robust Forecasting," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 20-038, Nov.
• Frank Schorfheide & Dongho Song, 2020, "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 20-039, Jul.
• Tadeusz Kufel, 2020, "ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 15, issue 2, pages 181-204, June, DOI: 10.24136/eq.2020.009.
• Michael Hanias & Stefanos Tsakonas & Lykourgos Magafas & Eleftherios I. Thalassinos & Loukas Zachilas, 2020, "Deterministic chaos and forecasting in Amazon’s share prices," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 15, issue 2, pages 253-273, June, DOI: 10.24136/eq.2020.012.
• Erika Onuferova & Veronika Cabinova & Tunde Dzurov Vargova, 2020, "Analysis of modern methods for increasing and managing the financial prosperity of businesses in the context of performance: a case study of the tourism sector in Slovakia," Oeconomia Copernicana, Institute of Economic Research, volume 11, issue 1, pages 95-116, March, DOI: 10.24136/oc.2020.004.
• Angelo Gabrielle Santos, 2020, "Forecasting Residential electricity demand in the Philippines using an Error Correction Model," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, volume 57, issue 1, pages 121-151, June.
• Manuel M. F. Martins & Fabio Verona, 2020, "Forecasting Inflation with the New Keynesian Phillips Curve: Frequency Matters," CEF.UP Working Papers, Universidade do Porto, Faculdade de Economia do Porto, number 2001, Apr.
• Korobilis, Dimitris & Koop, Gary, 2020, "Bayesian dynamic variable selection in high dimensions," MPRA Paper, University Library of Munich, Germany, number 100164, May.
• Razzak, Weshah, 2020, "The Dynamic of COVID-19 New Infections under Different Stringent Policies," MPRA Paper, University Library of Munich, Germany, number 100451, May.
• González Laxe, Fernando & Da Rocha Alvarez, Jose Maria & Armesto Pina, José Francisco & Sanchez-Fernandez, Patricio & Lago-Peñas, Santiago, 2020, "Economía de Galicia tras el COVID-19: prospectiva de escenarios
  [Economy of Galicia after COVID-covid-19: prospective forecastings]," MPRA Paper, University Library of Munich, Germany, number 100483, May, revised May 2020.
• Degiannakis, Stavros & Filis, George, 2020, "Oil price assumptions for macroeconomic policy," MPRA Paper, University Library of Munich, Germany, number 100705, May.
• Hernández, Juan R., 2020, "Covered Interest Parity: A Stochastic Volatility Approach to Estimate the Neutral Band," MPRA Paper, University Library of Munich, Germany, number 100744.
• Bakker, Bas & Ghazanchyan, Manuk & Ho, Alex & Nanda, Vibha, 2020, "The Lack of Convergence of Latin-America Compared with CESEE: Is Low Investment to Blame?," MPRA Paper, University Library of Munich, Germany, number 101287, Jun.
• Ahumada, Hildegart & Espina, Santos & Navajas, Fernando H., 2020, "COVID-19 with uncertain phases: estimation issues with an illustration for Argentina," MPRA Paper, University Library of Munich, Germany, number 101466, Jun.
• Van, Germinal, 2020, "Property Rights and Economic Growth in Africa: An Econometric Analysis," MPRA Paper, University Library of Munich, Germany, number 101681, Jul.
• Glocker, Christian & Kaniovski, Serguei, 2020, "Structural modeling and forecasting using a cluster of dynamic factor models," MPRA Paper, University Library of Munich, Germany, number 101874, Jul.
• Sucarrat, Genaro, 2020, "Identification of Volatility Proxies as Expectations of Squared Financial Return," MPRA Paper, University Library of Munich, Germany, number 101953, Jul.
• Yang, Bill Huajian & Yang, Jenny & Yang, Haoji, 2020, "Modeling Portfolio Loss by Interval Distributions," MPRA Paper, University Library of Munich, Germany, number 102219, Jul.
• Fantazzini, Dean, 2020, "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," MPRA Paper, University Library of Munich, Germany, number 102315, Aug.
• Fantazzini, Dean, 2020, "Discussing copulas with Sergey Aivazian: a memoir," MPRA Paper, University Library of Munich, Germany, number 102317, Aug.
• Polterovich, Victor & Denisova, Irina & Shakleina, Marina & Bogatova, Irina & Vartanov, Sergey & Turdyeva, Natalya & Chubarova, Tatiana, 2020, "Социально-Экономические Детерминанты Болезни Паркинсона Для Развитых И Развивающихся Стран
  [Socioeconomic determinants of Parkinson's disease for developed and developing countries]," MPRA Paper, University Library of Munich, Germany, number 103126, Sep.
• Van, Germinal G., 2020, "Modeling and Forecasting Economic Growth in Sub-Saharan Africa in the Post-Covid Era," MPRA Paper, University Library of Munich, Germany, number 103153, Sep.
• Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020, "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper, University Library of Munich, Germany, number 103250, Oct, revised 01 Oct 2020.
• Poblete-Cazenave, Miguel & Pachauri, Shonali, 2020, "A simulation-based estimation model of household electricity demand and appliance ownership," MPRA Paper, University Library of Munich, Germany, number 103403, Jul.
• Fokin, Nikita & Haritonova, Marina, 2020, "Сравнительный Анализ Прогнозных Моделей Российского Ввп В Условиях Наличия Структурных Сдвигов
  [Comparative analysis of the forecasting models for Russia’s GDP under the structural breaks]," MPRA Paper, University Library of Munich, Germany, number 103412.
• Fantazzini, Dean & Kolodin, Nikita, 2020, "Does the hashrate affect the bitcoin price?," MPRA Paper, University Library of Munich, Germany, number 103812.
• Soh, Ann-Ni, 2020, "A Review on the Leading Indicator Approach towards Economic Forecasting," MPRA Paper, University Library of Munich, Germany, number 103854, Oct.
• Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020, "Prediction for the 2020 United States Presidential Election using Linear Regression Model," MPRA Paper, University Library of Munich, Germany, number 103890, Sep, revised 20 Oct 2020.
• Cerqua, Augusto & Letta, Marco, 2020, "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper, University Library of Munich, Germany, number 104404, Nov.
• Olkhov, Victor, 2020, "Business Cycles as Collective Risk Fluctuations," MPRA Paper, University Library of Munich, Germany, number 104598, Dec.
• Li, Chenxing & Maheu, John M, 2020, "A Multivariate GARCH-Jump Mixture Model," MPRA Paper, University Library of Munich, Germany, number 104770, Dec.
• Pincheira, Pablo & Hardy, Nicolas, 2020, "The Mean Squared Prediction Error Paradox: A summary," MPRA Paper, University Library of Munich, Germany, number 105020, Dec.
• Fajar, Muhammad & Prasetyo, Octavia Rizky & Nonalisa, Septiarida & Wahyudi, Wahyudi, 2020, "Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)," MPRA Paper, University Library of Munich, Germany, number 105042, Nov, revised 30 Nov 2020.
• Pincheira, Pablo & Jarsun, Nabil, 2020, "Summary of the Paper Entitled: Forecasting Fuel Prices with the Chilean Exchange Rate," MPRA Paper, University Library of Munich, Germany, number 105056, Dec.
• Olalude, Gbenga Adelekan & Olayinka, Hammed Abiola & Ankeli, Uchechi Constance, 2020, "Modelling and forecasting inflation rate in Nigeria using ARIMA models," MPRA Paper, University Library of Munich, Germany, number 105342, revised Dec 2020.
• Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2020, "On an integer-valued stochastic intensity model for time series of counts," MPRA Paper, University Library of Munich, Germany, number 105406, Jan.
• Pennoni, Fulvia & Bartolucci, Francesco & Forte, Gianfranco & Ametrano, Ferdinando, 2020, "Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model," MPRA Paper, University Library of Munich, Germany, number 106150.
• Tinoco, Marcos, 2020, "Modelando la volatilidad del diferencial TED: Una evaluación de pronósticos de modelos con heterocedasticidad condicional
  [Modeling the volatility of the TED spread: An assessment of model forecasts with conditional heteroscedasticity]," MPRA Paper, University Library of Munich, Germany, number 108086, Oct.
• Maiorova, Ksenia & Fokin, Nikita, 2020, "Наукастинг Темпов Роста Стоимостных Объемов Экспорта И Импорта По Товарным Группам
  [Nowcasting the growth rates of the export and import by commodity groups]," MPRA Paper, University Library of Munich, Germany, number 109557, Jun.
• Nguyen, Phong Thanh, 2020, "Application Machine Learning in Construction Management," MPRA Paper, University Library of Munich, Germany, number 109899, Dec, revised 01 Aug 2021.
• Vîntu, Denis, 2020, "Relegating - The GDP Structural Modelling Strategy, The Dynamics in Time-Series Data: Short-Run Shocks, Disequilibrium Shocks and Innovative Shocks to Nuisance," MPRA Paper, University Library of Munich, Germany, number 112857, Oct, revised 30 Sep 2020.
• Jackson, Emerson Abraham, 2020, "Understanding SLL / US$ exchange rate dynamics in Sierra Leone using Box-Jenkins ARIMA approach," MPRA Paper, University Library of Munich, Germany, number 97965, Jan, revised 03 Jan 2020.
• Cerulli, Giovanni, 2020, "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper, University Library of Munich, Germany, number 99111, Mar.
• Adeniyi, Isaac Adeola, 2020, "Bayesian Generalized Linear Mixed Effects Models Using Normal-Independent Distributions: Formulation and Applications," MPRA Paper, University Library of Munich, Germany, number 99165, Mar.
• Chu, Amanda M.Y. & Lv, Zhihui & Wagner, Niklas F. & Wong, Wing-Keung, 2020, "Linear and Nonlinear Growth Determinants: The Case of Mongolia and its Connection to China," MPRA Paper, University Library of Munich, Germany, number 99185, Mar.
• Laliotis, Ioannis, 2020, "The Covid-19 pandemic in Greece," MPRA Paper, University Library of Munich, Germany, number 99754, Apr.
• Matthew W. Clance & Riza Demirer & Rangan Gupta & Clement Kweku Kyei, 2020, "Predicting Firm-Level Volatility in the United States: The Role of Monetary Policy Uncertainty," Working Papers, University of Pretoria, Department of Economics, number 202007, Jan.
• Riza Demirer & Rangan Gupta & Christian Pierdzioch & Syed Jawad Hussain Shahzad, 2020, "A Note on Oil Price Shocks and the Forecastability of Gold Realized Volatility," Working Papers, University of Pretoria, Department of Economics, number 202010, Jan.
• Matteo Bonato & Rangan Gupta & Christian Pierdzioch, 2020, "Do Oil-Price Shocks Predict the Realized Variance of U.S. REITs?," Working Papers, University of Pretoria, Department of Economics, number 2020100, Nov.
• Afees A. Salisu & Juncal Cunado & Rangan Gupta, 2020, "Geopolitical Risks and Historical Exchange Rate Volatility of the BRICS," Working Papers, University of Pretoria, Department of Economics, number 2020105, Nov.
• Edmond Berisha & David Gabauer & Rangan Gupta & Chi Keung Marco Lau, 2020, "Time-Varying Influence of Household Debt on Inequality in United Kingdom," Working Papers, University of Pretoria, Department of Economics, number 202017, Feb.
• Edmond Berisha & David Gabauer & Rangan Gupta & Jacobus Nel, 2020, "Time-Varying Predictability of Financial Stress on Inequality in United Kingdom," Working Papers, University of Pretoria, Department of Economics, number 202030, Apr.
• Christos Bouras & Christina Christou & Rangan Gupta & Keagile Lesame, 2020, "Forecasting State- and MSA-Level Housing Returns of the US: The Role of Mortgage Default Risks," Working Papers, University of Pretoria, Department of Economics, number 202037, May.
• Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020, "The Role of Global Economic Conditions in Forecasting Gold Market Volatility: Evidence from a GARCH-MIDAS Approach," Working Papers, University of Pretoria, Department of Economics, number 202043, May.
• Riza Demirer & Rangan Gupta & Christian Pierdzioch & Syed Jawad Hussain Shahzad, 2020, "The Predictive Power of Oil Price Shocks on Realized Volatility of Oil: A Note," Working Papers, University of Pretoria, Department of Economics, number 202044, May.
• Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020, "Forecasting Power of Infectious Diseases-Related Uncertainty for Gold Realized Volatility," Working Papers, University of Pretoria, Department of Economics, number 202049, May.
• Afees A. Salisu & Rangan Gupta & Riza Demirer, 2020, "A Note on Uncertainty due to Infectious Diseases and Output Growth of the United States: A Mixed-Frequency Forecasting Experiment," Working Papers, University of Pretoria, Department of Economics, number 202050, May.
• Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020, "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers, University of Pretoria, Department of Economics, number 202051, May.
• Mehmet Balcilar & Edmond Berisha & Rangan Gupta & Christian Pierdzioch, 2020, "Time-Varying Evidence of Predictability of Financial Stress in the United States over a Century: The Role of Inequality," Working Papers, University of Pretoria, Department of Economics, number 202054, Jun.
• Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2020, "Point and Density Forecasting of Macroeconomic and Financial Uncertainties of the United States," Working Papers, University of Pretoria, Department of Economics, number 202058, Jun.
• Hardik A. Marfatia & Christophe Andre & Rangan Gupta, 2020, "Predicting Housing Market Sentiment: The Role of Financial, Macroeconomic and Real Estate Uncertainties," Working Papers, University of Pretoria, Department of Economics, number 202061, Jun.
• Afees A. Salisu & Juncal Cunado & Kazeem Isah & Rangan Gupta, 2020, "Oil Price and Exchange Rate Behaviour of the BRICS for Over a Century," Working Papers, University of Pretoria, Department of Economics, number 202064, Jul.
• Xin Sheng & Rangan Gupta & Qiang Ji, 2020, "Forecasting Charge-Off Rates with a Panel Tobit Model: The Role of Uncertainty," Working Papers, University of Pretoria, Department of Economics, number 202092, Oct.
• Rangan Gupta & Christian Pierdzioch & Afees A. Salisu, 2020, "Oil-Price Uncertainty and the U.K. Unemployment Rate: A Forecasting Experiment with Random Forests Using 150 Years of Data," Working Papers, University of Pretoria, Department of Economics, number 202095, Oct.
• Elie Bouri & Rangan Gupta & Anandamayee Majumdar & Sowmya Subramaniam, 2020, "Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates," Working Papers, University of Pretoria, Department of Economics, number 202098, Oct.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2020, "Uncertainty due to Infectious Diseases and Forecastability of the Realized Variance of US REITs: A Note," Working Papers, University of Pretoria, Department of Economics, number 202099, Oct.
• Tomáš Jeøábek, 2020, "The Efficiency of GARCH Models in Realizing Value at Risk Estimates," ACTA VSFS, University of Finance and Administration, volume 14, issue 1, pages 32-50.
• António Rua & Carlos Melo Gouveia & Nuno Lourenço, 2020, "Forecasting tourism with targeted predictors in a data-rich environment," Working Papers, Banco de Portugal, Economics and Research Department, number w202005.
• Nuttanan Wichitaksorn, 2020, "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers, Puey Ungphakorn Institute for Economic Research, number 146, Dec.
• Steven Lehrer & Tian Xie, 2020, "The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success," Working Paper, Economics Department, Queen's University, number 1449, Oct.
• Ramona Serrano Bautista & Leovardo Mata Mata, 2020, "A conditional heteroscedastic VaR approach with alternative distributions," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., volume 17, issue 2, pages 81-98, Julio-Dic.
• Castellares, Renzo & Cornejo, Gerson, 2020, "A Leading Indicator for Employment using Big Data," Working Papers, Banco Central de Reserva del Perú, number 2020-009, Jun.
• J. James Reade & Carl Singleton & Leighton Vaughan Williams, 2020, "Betting markets for English Premier League results and scorelines: evaluating a forecasting model," Economics Discussion Papers, Department of Economics, University of Reading, number em-dp2020-03, Mar.
• Andrew Clark, 2020, "A Pound Centric look at the Pound vs. Krona Exchange Rate Movement from 1844 to 1965," Economics Discussion Papers, Department of Economics, University of Reading, number em-dp2020-22, Oct.
• Michael P. Clements, 2020, "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance, Henley Business School, University of Reading, number icma-dp2020-01, Jan.
• Michael P. Clements, 2020, "Individual Forecaster Perceptions of the Persistence of Shocks to GDP," ICMA Centre Discussion Papers in Finance, Henley Business School, University of Reading, number icma-dp2020-02, Jan.
• Bejarano-Salcedo, Valeria & Cárdenas-Cárdenas, Julián Alonso & Julio-Román, Juan Manuel & Caicedo-García, Edgar, 2020, "Entendiendo, Modelando y Pronosticando los efectos de "El Niño" sobre los precios de los alimentos: el caso colombiano," Working papers, Red Investigadores de Economía, number 50, Jun.
• Avela, Aleksi & Lehmus, Markku, 2020, "It’s in the News: Developing a Real Time Index for Economic Uncertainty Based on Finnish News Titles," ETLA Working Papers, The Research Institute of the Finnish Economy, number 84, Dec.
• Yuri Balagula, 2020, "Forecasting daily spot prices in the Russian electricity market with the ARFIMA model," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 57, pages 89-101.
• Dean Fantazzini, 2020, "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 59, pages 33-54.
• Diana Petrova & Pavel Trunin, 2020, "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 59, pages 71-87.
• Ivan Stankevich, 2020, "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 59, pages 113-127.
• Agata Lozinskaia & Anastasiia Redkina & Evgeniia Shenkman, 2020, "Electricity consumption forecasting for integrated power system with seasonal patterns," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 60, pages 5-25.
• Necmettin Alpay Kocak, 2020, "Analysis of the Relationship between Household and Real Sector Expectations in Turkey (Türkiye’de Hanehalkı ve Reel Sektör Beklentileri Arasındaki İlişkinin Analizi)," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 11, issue 4, pages 989-1000.
• Kriti Mahajan & Anand Srinivasan, 2020, "Inflation Forecasting In Emerging Markets: A Machine Learning Approach," Working Papers, Centre for Advanced Financial Research and Learning (CAFRAL), number 022296, Feb.
• Yann Bilodeau, 2020, "Deep limit order book events dynamics," Working Papers, HEC Montreal, Canada Research Chair in Risk Management, number 20-4, Dec.
• S.A Hoseeini Ebrahimabad & Kh Jahangiri & M Ghaemi Asl & H Heydari, 2020, "Investigation of the volatility spillover effect and dynamic conditional correlations in Tehran Stock Exchange using wavelet-based Bayesian conditional variance heteroscedasticity," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, volume 7, issue 1, pages 149-184.
• Leila Eghbali & Reza Ranjpour & Seyed Kamal Sadeghi, 2020, "Granger Causality Analysis of Energy Consumption and Value Added in Industrial Sub-Sectors of Iran: A Bootstrap Panel Approach," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, volume 7, issue 3, pages 99-130.
• Yue Qiu & Tian Xie & Jun Yu, 2020, "Forecast combinations in machine learning," Economics and Statistics Working Papers, Singapore Management University, School of Economics, number 13-2020, May.
• Nikola RADIVOJEVIĆ & Luka FILIPOVI & Тomislav D. BRZAKOVIĆ, 2020, "A New Semiparametric Mirrored Historical Simulation Value-At-Risk Model," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 1, pages 5-21, March.
• Dalia STREIMIKIENE & Rizwan Raheem AHMED & Saghir Pervaiz GHAURI & Muhammad AQIL & Justas STREIMIKIS, 2020, "Forecasting and the Causal Relationship of Sectorial Energy Consumptions and GDP of Pakistan by using AR, ARIMA, and Toda-Yamamoto Wald Models," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 131-148, July.
• Krzysztof DRACHAL, 2020, "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 18-34, July.
• Costas Siriopoulos & Maria Skaperda, 2020, "Investing in mutual funds: are you paying for performance or for the ties of the manager?," Bulletin of Applied Economics, Risk Market Journals, volume 7, issue 2, pages 153-164.
• Balazs Pager & Zsuzsanna Zsibókb, 2020, "Regionalizing National-Level Growth Projections in the Visegrad Countries – The Issue Of Ex-Post Rescaling," Romanian Journal of Regional Science, Romanian Regional Science Association, volume 14, issue 1, pages 1-24, JUNE.
• Tommaso Proietti & Alessandro Giovannelli, 2020, "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper, Tor Vergata University, CEIS, number 482, May, revised 12 May 2020.
• Alessandro Giovannelli & Tommaso Proietti & Ambra Citton & Ottavio Ricchi & Cristian Tegami & Cristina Tinti, 2020, "Nowcasting GDP and its Components in a Data-rich Environment: the Merits of the Indirect Approach," CEIS Research Paper, Tor Vergata University, CEIS, number 489, May, revised 30 May 2020.
• Robert Wrathall & Rod Falvey & Gulasekaran Rajaguru, 2020, "Do (Australian) jockeys have hot hands?," Australian Journal of Management, Australian School of Business, volume 45, issue 2, pages 223-239, May, DOI: 10.1177/0312896219883675.
• P. K. Viswanathan & Suresh Srinivasan & N. Hariharan, 2020, "Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms," Journal of Emerging Market Finance, Institute for Financial Management and Research, volume 19, issue 2, pages 226-261, August, DOI: 10.1177/0972652720913478.
• Mar Delgado-Téllez & Javier J. Pérez, 2020, "Institutional and Economic Determinants of Regional Public Debt in Spain," Public Finance Review, , volume 48, issue 2, pages 212-249, March, DOI: 10.1177/1091142120901672.
• Yu. Beketnova M. & Ю. Бекетнова М., 2020, "Синтез социально-экономических карт и визуализация девиантной деятельности объектов финансового мониторинга // Synthesis of Socio-Economic Maps and Visualization of Deviant Activity Measures of Financial Monitoring of Entities," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, volume 24, issue 4, pages 6-17.
• Yu. Beketnova M. & Ю. Бекетнова М., 2020, "Анализ возможностей автоматизации выявления недобросовестных микрофинансовых организаций на основе методов машинного обучения // Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Meth," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, volume 24, issue 6, pages 38-50.
• Andreas Psimopoulos, 2020, "Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, volume 18, issue 1, pages 40-99.
• Martínez Vázquez, David Conaly & Pérez Avila, Héctor, 2020, "Proyección Markoviana de riesgos hidrometeorológicos para el cálculo actuarial en México al 2020 / Markovian projection of hydrometeorological risks for actuarial calculation in Mexico up to 2020," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, volume 10, issue 2, pages 163-194, julio-dic.
• Henry Nasses & Rodrigo De Losso, 2020, "Behavior Biases in Macroeconomic Forecasting," Working Papers, Department of Economics, University of São Paulo (FEA-USP), number 2020_23, Nov.
• Bernard Fingleton, 2020, "Exploring Brexit with dynamic spatial panel models: some possible outcomes for employment across the EU regions," The Annals of Regional Science, Springer;Western Regional Science Association, volume 64, issue 2, pages 455-491, April, DOI: 10.1007/s00168-019-00913-2.
• Joanna Bruzda, 2020, "Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, volume 28, issue 1, pages 309-336, March, DOI: 10.1007/s10100-018-0591-2.
• Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020, "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, volume 2, issue 3, pages 241-258, December, DOI: 10.1007/s42521-020-00024-0.
• Rangan Gupta & Hylton Hollander & Rudi Steinbach, 2020, "Forecasting output growth using a DSGE-based decomposition of the South African yield curve," Empirical Economics, Springer, volume 58, issue 1, pages 351-378, January, DOI: 10.1007/s00181-018-1607-4.
• Nima Nonejad, 2020, "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, volume 58, issue 1, pages 313-349, January, DOI: 10.1007/s00181-019-01643-2.
• Christian Glocker & Philipp Wegmueller, 2020, "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, volume 58, issue 1, pages 73-105, January, DOI: 10.1007/s00181-019-01666-9.
• M. Chudý & S. Karmakar & W. B. Wu, 2020, "Long-term prediction intervals of economic time series," Empirical Economics, Springer, volume 58, issue 1, pages 191-222, January, DOI: 10.1007/s00181-019-01689-2.
• Boriss Siliverstovs, 2020, "Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts," Empirical Economics, Springer, volume 58, issue 1, pages 7-27, January, DOI: 10.1007/s00181-019-01704-6.
• Marcus P. A. Cobb, 2020, "Aggregate density forecasting from disaggregate components using Bayesian VARs," Empirical Economics, Springer, volume 58, issue 1, pages 287-312, January, DOI: 10.1007/s00181-019-01720-6.
• Gabe Jacob de Bondt & Arne Gieseck & Zivile Zekaite, 2020, "Thick modelling income and wealth effects: a forecast application to euro area private consumption," Empirical Economics, Springer, volume 58, issue 1, pages 257-286, January, DOI: 10.1007/s00181-019-01738-w.
• Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020, "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, volume 58, issue 1, pages 139-167, January, DOI: 10.1007/s00181-019-01788-0.
• João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020, "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, volume 58, issue 1, pages 29-54, January, DOI: 10.1007/s00181-019-01810-5.
• Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020, "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, volume 58, issue 1, pages 107-137, January, DOI: 10.1007/s00181-019-01814-1.
• Christian Pierdzioch & Marian Risse, 2020, "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, volume 58, issue 3, pages 1167-1184, March, DOI: 10.1007/s00181-018-1558-9.
• Kyle E. Binder & Mohsen Pourahmadi & James W. Mjelde, 2020, "The role of temporal dependence in factor selection and forecasting oil prices," Empirical Economics, Springer, volume 58, issue 3, pages 1185-1223, March, DOI: 10.1007/s00181-018-1574-9.
• Aitor Ciarreta & Peru Muniain & Ainhoa Zarraga, 2020, "Realized volatility and jump testing in the Japanese electricity spot market," Empirical Economics, Springer, volume 58, issue 3, pages 1143-1166, March, DOI: 10.1007/s00181-018-1577-6.
• Rodrigo Herrera & Adam Clements, 2020, "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, volume 58, issue 4, pages 1575-1601, April, DOI: 10.1007/s00181-018-1600-y.
• João F. Caldeira, 2020, "Investigating the expectation hypothesis and the risk premium dynamics: new evidence for Brazil," Empirical Economics, Springer, volume 59, issue 1, pages 395-412, July, DOI: 10.1007/s00181-019-01629-0.
• Holger Stichnoth, 2020, "Short-run fertility effects of parental leave benefits: evidence from a structural model," Empirical Economics, Springer, volume 59, issue 1, pages 143-168, July, DOI: 10.1007/s00181-019-01673-w.
• Marcos Álvarez-Díaz, 2020, "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods," Empirical Economics, Springer, volume 59, issue 3, pages 1285-1305, September, DOI: 10.1007/s00181-019-01665-w.
• Yana Petrova, 2020, "On cointegration between the insurance market and economic activity," Empirical Economics, Springer, volume 59, issue 3, pages 1127-1138, September, DOI: 10.1007/s00181-019-01669-6.
• Wali Ullah, 2020, "The arbitrage-free generalized Nelson–Siegel term structure model: Does a good in-sample fit imply better out-of-sample forecasts?," Empirical Economics, Springer, volume 59, issue 3, pages 1243-1284, September, DOI: 10.1007/s00181-019-01710-8.
• Kenneth R. Szulczyk & Changyong Zhang, 2020, "Switching-regime regression for modeling and predicting a stock market return," Empirical Economics, Springer, volume 59, issue 5, pages 2385-2403, November, DOI: 10.1007/s00181-019-01763-9.
• Marián Vávra, 2020, "Assessing distributional properties of forecast errors for fan-chart modelling," Empirical Economics, Springer, volume 59, issue 6, pages 2841-2858, December, DOI: 10.1007/s00181-019-01726-0.
• Lixiong Yang, 2020, "State-dependent biases and the quality of China’s preliminary GDP announcements," Empirical Economics, Springer, volume 59, issue 6, pages 2663-2687, December, DOI: 10.1007/s00181-019-01751-z.
• Dean Fantazzini & Stephan Zimin, 2020, "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, volume 47, issue 1, pages 19-69, March, DOI: 10.1007/s40812-019-00136-8.
• Unn Lindholm & Marcus Mossfeldt & Pär Stockhammar, 2020, "Forecasting inflation in Sweden," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, volume 37, issue 1, pages 39-68, April, DOI: 10.1007/s40888-019-00161-9.
• Maurizio Bovi, 2020, "A time-varying expectations formation mechanism," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, volume 37, issue 1, pages 69-103, April, DOI: 10.1007/s40888-019-00171-7.
• Maud H. Korte & Gertjan S. Verhoeven & Arianne M. J. Elissen & Silke F. Metzelthin & Dirk Ruwaard & Misja C. Mikkers, 2020, "Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), volume 21, issue 8, pages 1121-1129, November, DOI: 10.1007/s10198-020-01213-9.
• Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020, "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, volume 24, issue 2, pages 66-103, June.
• Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020, "Targeting predictors in random forest regression," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2020-03, May.
• Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2020, "A statistical model of the global carbon budget," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2020-18, Dec.
• Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020, "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2020-19, Dec.
• Sarthak Behera & Hyeongwoo Kim & Soohyon Kim, 2020, "Forecasting the US Dollar-Korean Won Exchange Rate: A Factor-Augmented Model Approach," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2020-02, May.
• Glyn Wittwer & Kym Anderson, 2020, "A Model of Global Beverage Markets," Wine Economics Research Centre Working Papers, University of Adelaide, Wine Economics Research Centre, number 2019-05, Feb.
• Pedro Bordalo & Nicola Gennaioli & Yueran Ma & Andrei Shleifer, 2020, "Overreaction in Macroeconomic Expectations," American Economic Review, American Economic Association, volume 110, issue 9, pages 2748-2782, September, DOI: 10.1257/aer.20181219.
• Raffaella Giacomini & Vasiliki Skreta & Javier Turen, 2020, "Heterogeneity, Inattention, and Bayesian Updates," American Economic Journal: Macroeconomics, American Economic Association, volume 12, issue 1, pages 282-309, January, DOI: 10.1257/mac.20180235.
• Stefano DellaVigna & Nicholas Otis & Eva Vivalt, 2020, "Forecasting the Results of Experiments: Piloting an Elicitation Strategy," AEA Papers and Proceedings, American Economic Association, volume 110, pages 75-79, May, DOI: 10.1257/pandp.20201080.
• Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2020, "A first-stage test for instrumental variables quantile regression," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4304, Nov.
• J. Daniel Aromí & Martín Llada, 2020, "Forecasting inflation with twitter," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4308, Nov.
• Emilio Blanco & Laura D’Amato & Fiorella Dogliolo & Lorena Garegnani, 2020, "Nowcasting Macroeconomic Aggregates in Argentina: Comparing the predictive ability of different models," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4335, Nov.
• Maria Morena Rivera, 2020, "Estimación de Demanda de Milanesas de Cerdo y estudio de viabilidad financiera del proyecto: “Elaboración y comercialización de milanesas de cerdo para Piamontesa SA”," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4398, Nov.
• Tamar Mdivnishvili & Shalva Mkhatrishvili & Davit Tutberidze, 2020, "Cash Flow at Risk Assessment for the Banking Sector of Georgia," NBG Working Papers, National Bank of Georgia, number 03/2020, Jul.
• Olivier Damette & Claude Diebolt & Stephane Goutte & Umberto Triacca, 2020, "Cliometrics of Climate Change: A Natural Experiment on the Little Ice Age," Working Papers, Association Française de Cliométrie (AFC), number 02-20.
• Enrico D’Elia & Francesca Faedda & Giacomo Giannone, 2020, "Un modello statistico per il monitoraggio delle entrate tributarie (MoME)," Working Papers, Ministry of Economy and Finance, Department of Finance, number wp2020-5, May.
• Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2020, "Beating the naive: Combining LASSO with naive intraday electricity price forecasts," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/20/01, Feb.
• Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020, "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/20/02, Feb.
• Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020, "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/20/08, May.
• Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020, "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/20/17, Dec.
• Gürcan Aygün, 2020, "Parasal Değişkenler ve Çıktı İlişkisinin Türkiye İçin Tarihsel Ayrıştırma Yöntemi İle Analizi," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 5, issue 2, pages 228-241, DOI: 10.30784/epfad.737602.
• Ceyda Yerdelen Kaygın & Abdulkadir Barut, 2020, "Çifte Kayıtlı Hisse Senetlerinin Fiyatlarını Etkileyen İçsel Faktörlerin Dinamik Panel Veri Analizi İle Belirlenmesi," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 5, issue 3, pages 805-821, DOI: 10.30784/epfad.773057.
• Crăciun Ionuț-Marius, 2020, "Study On The Evolution Of The Number Of People With Disabilities In Romania," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, volume 1, issue 48, pages 71-86, August.
• Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020, "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2020-04, Oct.
• Adolfo Rodríguez-Vargas, 2020, "Forecasting Costa Rican Inflation with Machine Learning Methods," Documentos de Trabajo, Banco Central de Costa Rica, number 2002, Mar.
• Oscar Claveria & Ivana Lolic & Enric Monte & Salvador Torra & Petar Soric, 2020, "“Economic determinants of employment sentiment: A socio-demographic analysis for the euro area”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 2012001, Jan, revised Jan 2020.
• Ji Hyung Lee & Youngki Shin, 2020, "Complete Subset Averaging for Quantile Regressions," Papers, arXiv.org, number 2003.03299, Mar, revised Jul 2021.
• Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020, "Targeting predictors in random forest regression," Papers, arXiv.org, number 2004.01411, Apr, revised Nov 2020.
• Wei-Zhen Li & Jin-Rui Zhai & Zhi-Qiang Jiang & Gang-Jin Wang & Wei-Xing Zhou, 2020, "Predicting tail events in a RIA-EVT-Copula framework," Papers, arXiv.org, number 2004.03190, Apr, revised Apr 2020.
• Pietro Battiston & Simona Gamba, 2020, "COVID-19: $R_0$ is lower where outbreak is larger," Papers, arXiv.org, number 2004.07827, Apr.
• Dimitris Korobilis, 2020, "High-dimensional macroeconomic forecasting using message passing algorithms," Papers, arXiv.org, number 2004.11485, Apr.
• Fabrizio Cipollini & Giampiero M. Gallo & Alessandro Palandri, 2020, "A dynamic conditional approach to portfolio weights forecasting," Papers, arXiv.org, number 2004.12400, Apr.
• Andreas Dibiasi & Samad Sarferaz, 2020, "Measuring Macroeconomic Uncertainty: The Labor Channel of Uncertainty from a Cross-Country Perspective," Papers, arXiv.org, number 2006.09007, Jun, revised Dec 2020.
• Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2020, "A Model of the Fed's View on Inflation," Papers, arXiv.org, number 2006.14110, Jun.
• Laurent Ferrara & Anna Simoni, 2020, "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers, arXiv.org, number 2007.00273, Jul, revised Sep 2022.
• Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020, "Deep Dynamic Factor Models," Papers, arXiv.org, number 2007.11887, Jul, revised May 2023.
• Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020, "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers, arXiv.org, number 2007.13566, Jul, revised Dec 2022.
• Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020, "Macroeconomic Data Transformations Matter," Papers, arXiv.org, number 2008.01714, Aug, revised Mar 2021.
• Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020, "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers, arXiv.org, number 2008.12477, Aug.
• Florian Huber & Gary Koop & Luca Onorante & Michael Pfarrhofer & Josef Schreiner, 2020, "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs," Papers, arXiv.org, number 2008.12706, Aug, revised Dec 2020.