Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2020

• 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.
• Maren Hein & Peter Kurz & Winfried J. Steiner, 2020, "Analyzing the capabilities of the HB logit model for choice-based conjoint analysis: a simulation study," Journal of Business Economics, Springer, volume 90, issue 1, pages 1-36, February, DOI: 10.1007/s11573-019-00927-4.
• Christian Lohmann & Thorsten Ohliger, 2020, "Bankruptcy prediction and the discriminatory power of annual reports: empirical evidence from financially distressed German companies," Journal of Business Economics, Springer, volume 90, issue 1, pages 137-172, February, DOI: 10.1007/s11573-019-00938-1.
• Yongchen Zhao, 2020, "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 16, issue 2, pages 77-97, November, DOI: 10.1007/s41549-020-00046-y.
• Kristian Jönsson, 2020, "Machine Learning and Nowcasts of Swedish GDP," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 16, issue 2, pages 123-134, November, DOI: 10.1007/s41549-020-00049-9.
• Saakshi & Sohini Sahu & Siddhartha Chattopadhyay, 2020, "Epidemiology of inflation expectations and internet search: an analysis for India," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, volume 15, issue 3, pages 649-671, July, DOI: 10.1007/s11403-019-00255-4.
• 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.
• 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.
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