Research classified by Journal of Economic Literature (JEL) codes

---

Top JEL
/ C: Mathematical and Quantitative Methods
/ / C4: Econometric and Statistical Methods: Special Topics
/ / / C45: Neural Networks and Related Topics

2021

• Nguyen Vo & Robert Ślepaczuk, 2021, "Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-25.
• Sergio Castellano Gómez & Robert Ślepaczuk, 2021, "Robust optimisation in algorithmic investment strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-27.
• Garg, Karan, 2021, "Machines and Markets : Assessing the Impact of Algorithmic Trading on Financial Market Efficiency," Warwick-Monash Economics Student Papers, Warwick Monash Economics Student Papers, number 11.
• Hinterlang, Natascha & Tänzer, Alina, 2021, "Optimal monetary policy using reinforcement learning," Discussion Papers, Deutsche Bundesbank, number 51/2021.
• Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021, "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2021-015.
• Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021, "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series, Queen's University Belfast, Queen's Business School, number 2021/04, DOI: 10.2139/ssrn.3701000.
• Anese, Gianluca & Corazza, Marco & Costola, Michele & Pelizzon, Loriana, 2021, "Impact of public news sentiment on stock market index return and volatility," SAFE Working Paper Series, Leibniz Institute for Financial Research SAFE, number 322.
• Dörr, Julian Oliver & Kinne, Jan & Lenz, David & Licht, Georg & Winker, Peter, 2021, "An integrated data framework for policy guidance in times of dynamic economic shocks," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 21-062.

2020

• Bo Cowgill & Fabrizio Dell'Acqua & Sandra Matz, 2020, "The Managerial Effects of Algorithmic Fairness Activism," AEA Papers and Proceedings, American Economic Association, volume 110, pages 85-90, May, DOI: 10.1257/pandp.20201035.
• Ashesh Rambachan & Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan, 2020, "An Economic Perspective on Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, volume 110, pages 91-95, May, DOI: 10.1257/pandp.20201036.
• Bo Cowgill & Megan T. Stevenson, 2020, "Algorithmic Social Engineering," AEA Papers and Proceedings, American Economic Association, volume 110, pages 96-100, May, DOI: 10.1257/pandp.20201037.
• Martha J. Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2020, "How Well Do Automated Linking Methods Perform? Lessons from US Historical Data," Journal of Economic Literature, American Economic Association, volume 58, issue 4, pages 997-1044, December, DOI: 10.1257/jel.20191526.
• Ержан И.С. // Erzhan I.S., 2020, "Использование моделей machine learning при прогнозировании инфляции // Using machine learning models in inflation forecasting," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue 1, pages 39-48.
• Adolfo Rodríguez-Vargas, 2020, "Forecasting Costa Rican Inflation with Machine Learning Methods," Documentos de Trabajo, Banco Central de Costa Rica, number 2002, Mar.
• Hannes Wallimann & David Imhof & Martin Huber, 2020, "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers, arXiv.org, number 2004.05629, Apr.
• Mykola Babiak & Jozef Barunik, 2020, "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers, arXiv.org, number 2009.03394, Sep, revised Feb 2026.
• Xinwen Ni & Wolfgang Karl Hardle & Taojun Xie, 2020, "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," Papers, arXiv.org, number 2009.12121, Sep, revised Aug 2021.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2020, "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Papers, arXiv.org, number 2011.07920, Nov, revised Feb 2022.
• Bo Cowgill & Fabrizio Dell'Acqua & Sandra Matz, 2020, "The Managerial Effects of Algorithmic Fairness Activism," Papers, arXiv.org, number 2012.02393, Dec.
• Anton Gerunov, 2020, "Classification algorithms for modeling economic choice," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 45-67.
• Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020, "Financial frictions and the wealth distribution," Working Papers, Banco de España, number 2013, Jun.
• Carlos León & Paolo Barucca & Oscar Acero & Gerardo Gage & Fabio Ortega, 2020, "Pattern recognition of financial institutions’ payment behavior," Borradores de Economia, Banco de la Republica de Colombia, number 1130, Sep, DOI: https://doi.org/10.32468/be.1130.
• Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2020, "An Adversarial Approach to Structural Estimation," Working Papers, Becker Friedman Institute for Research In Economics, number 2020-144.
• Michael Creel, 2020, "Inference Using Simulated Neural Moments," Working Papers, Barcelona School of Economics, number 1182, Jun.
• Salim Sercan SARI & Þule Yüksel YÝÐÝTER, 2020, "Borsa Istanbul Hisse Senedi Getirilerinin ANFIS Aracýlýðýyla Tahmin Edilmesi," Bingol University Journal of Economics and Administrative Sciences, Bingol University, Faculty of Economics and Administrative Sciences, volume 4, issue 1, pages 171-193, August, DOI: https://doi.org/10.33399/biibfad.74.
• Ferencek Aljaž & Kofjač Davorin & Škraba Andrej & Sašek Blaž & Borštnar Mirjana Kljajić, 2020, "Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period," Business Systems Research, Sciendo, volume 11, issue 2, pages 36-50, October, DOI: 10.2478/bsrj-2020-0014.
• Ramis Khbaibullin & Sergei Seleznev, 2020, "Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models," Bank of Russia Working Paper Series, Bank of Russia, number wps61, Oct.
• Jonnathan R. Cáceres Santos, 2020, "Modelos de Machine Learning para el análisis y pronóstico de la situación financiera de bancos – Caso boliviano," Revista de Análisis del BCB, Banco Central de Bolivia, volume 33, issue 1, pages 69-91, July - De.
• Soohyon Kim, 2020, "Macroeconomic and Financial Market Analyses and Predictions through Deep Learning," Working Papers, Economic Research Institute, Bank of Korea, number 2020-18, Sep.
• Mykola Babiak & Jozef Barunik, 2020, "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp677, Dec.
• Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020, "Financial Frictions and the Wealth Distribution," CESifo Working Paper Series, CESifo, number 8482.
• Oksana Bashchenko & Alexis Marchal, 2020, "Deep Learning for Asset Bubbles Detection," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-08, Mar.
• Oksana Bashchenko & Alexis Marchal, 2020, "Deep Learning, Jumps, and Volatility Bursts," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-10, Mar.
• Roberto Molinari & Gaetan Bakalli & Stéphane Guerrier & Cesare Miglioli & Samuel Orso & O. Scaillet, 2020, "Swag: A Wrapper Method for Sparse Learning," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-49, Jun.
• Luis Jorge Garay & Eduardo Salcedo-Albar�n & Daphne �lvarez, 2020, "Macro-Corrupción y Cooptación Institucional en el departamento de Córdoba, Colombia," Informes de Investigación, Fedesarrollo, number 18137, Apr.
• Giavazzi, Francesco & Lemoli, Giacomo & Rubera, Gaia & Iglhaut, Felix, 2020, "Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14455, Feb.
• Adams-Prassl, Abigail & Balgova, Maria & Qian, Matthias, 2020, "Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15263, Sep.
• Taylor, Mark & Filippou, Ilias & Rapach, David & Zhou, Guofu, 2020, "Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15305, Sep.
• Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020, "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15308, Sep.
• Niklas, Britta & Rinke, Wolfram, 2020, "Pricing Models for German Wine: Hedonic Regression vs. Machine Learning," Journal of Wine Economics, Cambridge University Press, volume 15, issue 3, pages 284-311, August.
• Ангелин Лалев & Александрина Александрова, 2020, "Използване На Дълбоки Невронни Мрежи За Откриване На Измами С Кредитни Карти," Scientific Research Almanac, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, volume 28, issue 1 Year 20, pages 39-62.
• Leonard Sabetti & Ronald Heijmans, 2020, "Shallow or deep? Detecting anomalous flows in the Canadian Automated Clearing and Settlement System using an autoencoder," Working Papers, DNB, number 681, Apr.
• Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020, "Nowcasting business cycle turning points with stock networks and machine learning," Working Paper Series, European Central Bank, number 2494, Nov.
• Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2020, "The relationship between air pollution and COVID-19-related deaths: An application to three French cities," Applied Energy, Elsevier, volume 279, issue C, DOI: 10.1016/j.apenergy.2020.115835.
• Thomas, Sheetal & Goel, Mridula & Agrawal, Dipak, 2020, "A framework for analyzing financial behavior using machine learning classification of personality through handwriting analysis," Journal of Behavioral and Experimental Finance, Elsevier, volume 26, issue C, DOI: 10.1016/j.jbef.2020.100315.
• Jahn, Malte, 2020, "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, volume 91, issue C, pages 148-154, DOI: 10.1016/j.econmod.2020.06.008.
• Philip, R., 2020, "Estimating permanent price impact via machine learning," Journal of Econometrics, Elsevier, volume 215, issue 2, pages 414-449, DOI: 10.1016/j.jeconom.2019.10.002.
• Jasiński, Tomasz, 2020, "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, volume 213, issue C, DOI: 10.1016/j.energy.2020.118784.
• Tölö, Eero, 2020, "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, volume 49, issue C, DOI: 10.1016/j.jfs.2020.100746.
• Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020, "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, volume 36, issue 2, pages 466-479, DOI: 10.1016/j.ijforecast.2019.07.002.
• Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2020, "Nonlinear forecast combinations: An example using euro-area real GDP growth," Journal of Economic Behavior & Organization, Elsevier, volume 180, issue C, pages 579-589, DOI: 10.1016/j.jebo.2018.09.021.
• Maehashi, Kohei & Shintani, Mototsugu, 2020, "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, volume 58, issue C, DOI: 10.1016/j.jjie.2020.101104.
• Kanazawa, Nobuyuki, 2020, "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, volume 64, issue C, DOI: 10.1016/j.jmacro.2020.103210.
• León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020, "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100011.
• Rodríguez-Vargas, Adolfo, 2020, "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100012.
• Rubio, Jeniffer & Barucca, Paolo & Gage, Gerardo & Arroyo, John & Morales-Resendiz, Raúl, 2020, "Classifying payment patterns with artificial neural networks: An autoencoder approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100013.
• Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2020, "Crisis transmission: Visualizing vulnerability," Pacific-Basin Finance Journal, Elsevier, volume 59, issue C, DOI: 10.1016/j.pacfin.2019.101255.
• Jiang, Minqi & Liu, Jiapeng & Zhang, Lu & Liu, Chunyu, 2020, "An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 541, issue C, DOI: 10.1016/j.physa.2019.122272.
• Brida, Juan Gabriel & Carrera, Edgar J. Sanchez & Segarra, Verónica, 2020, "Clustering and regime dynamics for economic growth and income inequality," Structural Change and Economic Dynamics, Elsevier, volume 52, issue C, pages 99-108, DOI: 10.1016/j.strueco.2019.09.010.
• Michael Puglia & Adam Tucker, 2020, "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-038, May, DOI: 10.17016/FEDS.2020.038.
• Wallimann, Hannes & Imhof, David & Huber, Martin, 2020, "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, number 513, Apr.
• Huber, Martin & Imhof, David, 2020, "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, number 519, Oct.
• Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020, "Neural Network Pricing of American Put Options," Risks, MDPI, volume 8, issue 3, pages 1-24, July.
• Gusarov, N. & Talebijmalabad, A. & Joly, I., 2020, "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers, Grenoble Applied Economics Laboratory (GAEL), number 2020-12.
• Nikita Gusarov & Amirreza Talebijamalabad & Iragaël Joly, 2020, "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers, HAL, number hal-03019739, Oct.
• Steffen Q. Mueller & Patrick Ring & Maria Fischer, 2020, "Excited and aroused: The predictive importance of simple choice process metrics," Working Papers, Chair for Economic Policy, University of Hamburg, number 067, Dec.
• Breuer, Wolfgang & Steininger, Bertram, 2020, "Recent Trends in Real Estate Research: A Comparison of Recent Working Papers and Publications using Machine Learning Algorithms," Working Paper Series, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, number 20/15, Dec.
• Pablo Rocha Portugal & Horacio Vera Cossio & Fernanda Wanderley, 2020, "Redes, características locales y flujos migratorios - Un estudio de la migración interna desde el análisis de redes sociales para impulsar el desarrollo local," SDSN Bolivia, Universidad Privada Boliviana, number 07-20, Aug.
• Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020, "Neural Network pricing of American put options," Working Papers REM, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa, number 2020/0122, Apr.
• Shahid Ali & Junrui Zhang & Aamir Azeem & Asif Mahmood, 2020, "Impact Of Electricity Consumption On Economic Growth: An Application Of Vector Error Correction Model and Artificial Neural Networks," Journal of Developing Areas, Tennessee State University, College of Business, volume 54, issue 4, pages 89-104, October-D.
• Özlem DENÄ°Z BAÅžAR & Elif GÃœNEREN GENÇ, 2020, "A Comparison Of Logistic Regression, Artificial Neural Networks And Moora Methods In Estimation Of The Safety Of Countries," JOURNAL OF LIFE ECONOMICS, Holistence Publications, volume 7, issue 2, pages .123-134, April, DOI: 10.15637/jlecon.7.008.
• Kubilay ERÄ°SLÄ°K & Özlem DENÄ°Z BAÅžAR, 2020, "Estimation Of The Sectors Of The Investments Made On Venture Capital Companies With Artificial Neural Networks And Multiple Logistic Regression Analysis," JOURNAL OF LIFE ECONOMICS, Holistence Publications, volume 7, issue 4, pages 297-308, October, DOI: 10.15637/jlecon.7.022.
• Jikhan Jeong, 2020, "Identifying Consumer Preferences from User- and Crowd-Generated Digital Footprints on Amazon.com by Leveraging Machine Learning and Natural Language Processing," 2020 Papers, Job Market Papers, number pje208, Nov.
• Zongwu Cai & Xiyuan Liu, 2020, "A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202017, Oct, revised Oct 2020.
• Zongwu Cai & Xiyuan Liu, 2020, "A Nonparametric Dynamic Network via Multivariate Quantile Autoregressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202209, Oct, revised Mar 2022.
• Zongwu Cai & Xiyuan Liu & Liangjun Su, 2020, "A Functional-Coefficient VAR Model for Dynamic Quantiles and Its Application to Constructing Nonparametric Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202406, Oct, revised Jan 2024.
• Jermain C. Kaminski & Christian Hopp, 2020, "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, volume 55, issue 3, pages 627-649, October, DOI: 10.1007/s11187-019-00218-w.
• Daniel Wochner, 2020, "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 20-472, May, DOI: 10.3929/ethz-b-000399304.
• Boriss Siliverstovs & Daniel Wochner, 2020, "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," Working Papers, Latvijas Banka, number 2020/02, Feb.
• Francesco Giavazzi & Felix Iglhaut & Giacomo Lemoli & Gaia Rubera, 2020, "Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter," NBER Working Papers, National Bureau of Economic Research, Inc, number 26825, Mar.
• Munisamy Gopinath & Feras A. Batarseh & Jayson Beckman, 2020, "Machine Learning in Gravity Models: An Application to Agricultural Trade," NBER Working Papers, National Bureau of Economic Research, Inc, number 27151, May.
• Marshall Burke & Anne Driscoll & David Lobell & Stefano Ermon, 2020, "Using Satellite Imagery to Understand and Promote Sustainable Development," NBER Working Papers, National Bureau of Economic Research, Inc, number 27879, Oct.
• 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.
• Tobias Götze & Marc Gürtler & Eileen Witowski, 2020, "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, volume 21, issue 5, pages 428-446, September, DOI: 10.1057/s41260-020-00167-0.
• Jaromir Vrbka, 2020, "The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic," Oeconomia Copernicana, Institute of Economic Research, volume 11, issue 2, pages 325-346, June, DOI: 10.24136/oc.2020.014.
• Grilli, Luca & Santoro, Domenico, 2020, "How Boltzmann Entropy Improves Prediction with LSTM," MPRA Paper, University Library of Munich, Germany, number 100578, May.
• Gomez-Ruano, Gerardo, 2020, "Data Science: A Primer for Economists," MPRA Paper, University Library of Munich, Germany, number 102928.
• Diunugala, Hemantha Premakumara & Mombeuil, Claudel, 2020, "Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods," MPRA Paper, University Library of Munich, Germany, number 103779, Oct.
• Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria, 2020, "Prediction of Socio-Economic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”," MPRA Paper, University Library of Munich, Germany, number 104234, Jul, revised 19 Nov 2020.
• Fajar, Muhammad & Hartini, Sri, 2020, "Comparison of ARIMA, SSA, and ARIMA – SSA hybrid model performance in Indonesian economic growth forecasting," MPRA Paper, University Library of Munich, Germany, number 105045, Jun, revised 16 Jun 2020.
• Grilli, Luca & Santoro, Domenico, 2020, "Generative Adversarial Network for Market Hourly Discrimination," MPRA Paper, University Library of Munich, Germany, number 99846, Apr.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020, "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working Papers, University of Pretoria, Department of Economics, number 202056, 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.
• Fela Ozbey & Semin Paksoy, 2020, "Estimation of the XU100 Index Return Volatility with the Integration of GARCH Family Models and ANN," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 11, issue 2, pages 385-396.
• 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.
• 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.
• Medina Reyes, José Eduardo & Castro Pérez, Judith Jazmin & Cabrera Llanos, Agustín Ignacio & Cruz Aké, Salvador, 2020, "Red neuronal autorregresiva difusa tipo Sugeno con funciones de membresía triangular y trapezoidal: una aplicación al pronóstico de índices del mercado bursátil / Sugeno Type Fuzzy Nonlinear Autoregressive Neural Networks with Triangular and Trapezoi," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, volume 10, issue 1, pages 77-101, enero-jun.
• Anton Gerunov, 2020, "Binary Classification Problems in Economics and 136 Different Ways to Solve Them," Bulgarian Economic Papers, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St Kliment Ohridski, number bep-2020-02, Mar, revised Mar 2020.
• Uğur ERCAN & Sezgin IRMAK & Kerim Kürşat ÇEVİK & Erokan CANBAZOĞLU, 2020, "Estimating Electricity Consumption Levels in Dwellings Using Artificial Neural NetworksAbstract: Most of the studies on electricity consumption were conducted using econometric models and statistical methods. Studies that aiming at predicting electri," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(46).
• Yakup SÖYLEMEZ, 2020, "Prediction of Gold Prices Using Multilayer Artificial Neural Networks Method," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(46).
• Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020, "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, volume 2, issue 1, pages 69-96, September, DOI: 10.1007/s42521-020-00019-x.
• 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.
• Wolfgang Breuer & Bertram I. Steininger, 2020, "Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms," Journal of Business Economics, Springer, volume 90, issue 7, pages 963-974, August, DOI: 10.1007/s11573-020-01005-w.
• Octavian Machidon & Dragoș Stoica & Aleš Tavčar, 2020, "Enhancing the Usability of European Digital Cultural Library Using Web Architectures and Deep Learning," Springer Proceedings in Business and Economics, Springer, in: Vicky Katsoni & Thanasis Spyriadis, "Cultural and Tourism Innovation in the Digital Era", DOI: 10.1007/978-3-030-36342-0_16.
• Nataliya Matveeva & Anuška Ferligoj, 2020, "Scientific collaboration in Russian universities before and after the excellence initiative Project 5-100," Scientometrics, Springer;Akadémiai Kiadó, volume 124, issue 3, pages 2383-2407, September, DOI: 10.1007/s11192-020-03602-6.
• Agustín García & Agustín García & Miguel A. Jaramillo-Morán, 2020, "Short-term European Union Allowance price forecasting with artificial neural networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 8, issue 1, pages 261-275, September, DOI: 10.9770/jesi.2020.8.1(18).
• Islam, Raisul & Volkov, Vladimir, 2020, "Contagion or interdependence? Comparing signed and unsigned spillovers," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2020-05.
• Islam, Raisul & Volkov, Vladimir, 2020, "Calm before the storm: an early warning approach before and during the COVID-19 crisis," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2020-09.
• Kohei Maehashi & Mototsugu Shintani, 2020, "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo, number CIRJE-F-1146, Mar.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020, "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working papers, University of Connecticut, Department of Economics, number 2020-10, Aug.
• Dmytro Krukovets, 2020, "Data Science Opportunities at Central Banks: Overview," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 249, pages 13-24, DOI: 10.26531/vnbu2020.249.02.
• Jerić Silvija Vlah, 2020, "Comparing classification algorithms for prediction on CROBEX data," Croatian Review of Economic, Business and Social Statistics, Sciendo, volume 6, issue 2, pages 4-11, December, DOI: 10.2478/crebss-2020-0007.
• Wójcik Filip & Górnik Michał, 2020, "Improvement of E-Commerce Recommendation Systems with Deep Hybrid Collaborative Filtering with Content: A Case Study," Econometrics. Advances in Applied Data Analysis, Sciendo, volume 24, issue 3, pages 37-50, September, DOI: 10.15611/eada.2020.3.03.
• Latoszek Michał & Ślepaczuk Robert, 2020, "Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor," Economics and Business Review, Sciendo, volume 6, issue 1, pages 46-81, March, DOI: 10.18559/ebr.2020.1.3.
• Tratkowski Grzegorz, 2020, "Identification of nonlinear determinants of stock indices derived by Random Forest algorithm," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, volume 56, issue 3, pages 209-217, September, DOI: 10.2478/ijme-2020-0017.
• Lee Changro & Park Keith Key-Ho, 2020, "Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach," Real Estate Management and Valuation, Sciendo, volume 28, issue 4, pages 15-23, December, DOI: 10.1515/remav-2020-0028.
• Oleh Bilyk & Paweł Sakowski & Robert Ślepaczuk, 2020, "Investing in VIX futures based on rolling GARCH models forecasts," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-10.
• Maciej Wysocki & Robert Ślepaczuk, 2020, "Artificial Neural Networks Performance in WIG20 Index Options Pricing," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-19.
• Mateusz Kijewski & Robert Ślepaczuk, 2020, "Predicting prices of S&P500 index using classical methods and recurrent neural networks," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-27.
• Karol Kielak & Robert Ślepaczuk, 2020, "Value-at-risk — the comparison of state-of-the-art models on various assets," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-28.
• Bartłomiej Bollin & Robert Ślepaczuk, 2020, "Variance Gamma Model in Hedging Vanilla and Exotic Options," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-31.
• Robert Ślepaczuk & Igor Wabik, 2020, "The impact of the results of football matches on the stock prices of soccer clubs," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-35.
• Andrea Bucci, 2020, "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., volume 39, issue 6, pages 865-876, September, DOI: 10.1002/for.2664.
• Hinterlang, Natascha, 2020, "Predicting monetary policy using artificial neural networks," Discussion Papers, Deutsche Bundesbank, number 44/2020.
• Ollech, Daniel & Webel, Karsten, 2020, "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers, Deutsche Bundesbank, number 55/2020.
• Diunugala, Hemantha Premakumara & Mombeuil, Claudel, 2020, "Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 6, issue 3, pages 3-13, DOI: 10.5281/zenodo.4055960.
• Dautel, Alexander Jakob & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2020, "Forex exchange rate forecasting using deep recurrent neural networks," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-006.
• Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020, "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-013.

2019

• Ali Babikir & Mohammed Elamin Hassan & Henry Mwambi, 2019, "Asymmetry, Fat-tail and Autoregressive Conditional Density in Daily Stocks Return Data," Annals of Economics and Statistics, GENES, issue 135, pages 57-68, DOI: 10.15609/annaeconstat2009.135.0057.
• Jens Ludwig & Sendhil Mullainathan & Jann Spiess, 2019, "Augmenting Pre-Analysis Plans with Machine Learning," AEA Papers and Proceedings, American Economic Association, volume 109, pages 71-76, May.
• Cristiana Chiriac & Ștefan Grapă & Mihai-Cristian Orzan, 2019, "An EEG Analysis on the Perception of the Consumers Regarding Video-Commercials from the Automotive Industry," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, volume 1, issue 1, pages 318-326, November.
• Cristiana Chiriac & Laura Daniela RoÈ™ca, 2019, "Automotive Industry Video-Commercials – A Pluralistic Research Based on an Eye-Tracking Experiment," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, volume 1, issue 1, pages 327-336, November.
• Alfonso Aja Kindelan & Leovardo Mata Mata & Jaime Humberto Beltrán Godoy, 2019, "Analysis and projection of Pfizer's stock returns, in the period 2018-2020, through differential neural networks," The Anahuac Journal, Business and Economics School. Anahuac University (Mexico)., volume 19, issue 1, pages 13-34, June, DOI: 10.36105/theanahuacjour.2019v19n1.0.
• Күзенбаев С.Т. // Kuzenbayev S.T. & Крупа Е. А. // Krupa E.A., 2019, "Использование технологии искусственного интеллекта при осуществлении денежно-кредитной политики // The use of artificial intelligence technology in the implementation of monetary policy," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue 1, pages 55-69.
• Andreas Joseph, 2019, "From interpretability to inference: an estimation framework for universal approximators," Papers, arXiv.org, number 1903.04209, Mar, revised Dec 2024.
• Ali Habibnia & Esfandiar Maasoumi, 2019, "Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Papers, arXiv.org, number 1904.11145, Apr.
• Stefania Albanesi & Domonkos F. Vamossy, 2019, "Predicting Consumer Default: A Deep Learning Approach," Papers, arXiv.org, number 1908.11498, Aug, revised Oct 2019.
• Freddy Cepeda-Lopez & Fredy Gamboa-Estrada & Carlos León & Hernán Rincón-Castro, 2019, "Colombian liberalization and integration to world trade markets: Much ado about nothing," Borradores de Economia, Banco de la Republica de Colombia, number 1065, Feb, DOI: 10.32468/be.1065.pdf?sequence=10&is.
• Denis Shibitov & Mariam Mamedli, 2019, "The finer points of model comparison in machine learning: forecasting based on russian banks’ data," Bank of Russia Working Paper Series, Bank of Russia, number wps43, Aug.
• Andreas Joseph, 2019, "Parametric inference with universal function approximators," Bank of England working papers, Bank of England, number 784, Mar.
• Lyudmyla Маlyarets & Oleksandr Dorokhov & Vitaliya Koybichuk & Liudmyla Dorokhova, 2019, "Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach," Journal of Central Banking Theory and Practice, Central bank of Montenegro, volume 8, issue 1, pages 163-182.
• Chuku Chuku & Anthony Simpasa & Jacob Oduor, 2019, "Intelligent forecasting of economic growth for developing economies," International Economics, CEPII research center, issue 159, pages 74-93.
• Paola Andrea Vaca González, 2019, "Cálculo y evaluación del riesgo operativo en entidades de salud a partir del enfoque de redes bayesianas," Ensayos de Economía, Universidad Nacional de Colombia Sede Medellín, number 18302, Jul, DOI: 10.15446/ede.v29n55.78411.
• Albanesi, Stefania & Vamossy, Domonkos, 2019, "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13914, Aug.
• Fernández-Villaverde, Jesús & Hurtado, Samuel & Nuño, Galo, 2019, "Financial Frictions and the Wealth Distribution," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14002, Sep.
• Mariapia Mendola & Mengesha Yayo Negasi, 2019, "Nutritional and Schooling Impact of a Cash Transfer Program in Ethiopia: A Retrospective Analysis of Childhood Experience," Development Working Papers, Centro Studi Luca d'Agliano, University of Milano, number 451, Jun.
• José Carlos Casas del Rosal & David E. Casas del Rosal & José María Caridad y Ocerin & Julia Núñez Tabales, 2019, "Mercado inmobiliario de españa: Una herramienta para el análisis de la oferta," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, volume 42, issue 120, pages 207-218, Diciembre.
• Jawwad Noor, 2019, "Intuitive Beliefs," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2216, Dec.
• Roncoroni, Alan & Battiston, Stefano & D'Errico, Marco & Hałaj, Grzegorz & Kok, Christoffer, 2019, "Interconnected banks and systemically important exposures," Working Paper Series, European Central Bank, number 2331, Nov.
• Richard Sarpong-Streetor & Rajalingam A/L Sokkalingam & Mahmod bin Othman & Dennis Ling Chuan Ching & Hamzah bin Sakidin, 2019, "A Hybrid Autoregressive Integrated Moving Average-phGMDH Model to Forecast Crude Oil Price," International Journal of Energy Economics and Policy, Econjournals, volume 9, issue 5, pages 135-141.
• Kolidakis, Stylianos & Botzoris, George & Profillidis, Vassilios & Lemonakis, Panagiotis, 2019, "Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis," Economic Analysis and Policy, Elsevier, volume 64, issue C, pages 159-171, DOI: 10.1016/j.eap.2019.08.002.
• Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2019, "Tail event driven networks of SIFIs," Journal of Econometrics, Elsevier, volume 208, issue 1, pages 282-298, DOI: 10.1016/j.jeconom.2018.09.016.
• Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019, "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, volume 78, issue C, pages 656-667, DOI: 10.1016/j.eneco.2017.12.035.
• Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019, "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, volume 80, issue C, pages 937-949, DOI: 10.1016/j.eneco.2019.03.006.
• Jasiński, Tomasz, 2019, "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, volume 179, issue C, pages 831-842, DOI: 10.1016/j.energy.2019.04.221.
• Huber, Martin & Imhof, David, 2019, "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, volume 65, issue C, pages 277-301, DOI: 10.1016/j.ijindorg.2019.04.002.
• Chuku, Chuku & Simpasa, Anthony & Oduor, Jacob, 2019, "Intelligent forecasting of economic growth for developing economies," International Economics, Elsevier, volume 159, issue C, pages 74-93, DOI: 10.1016/j.inteco.2019.06.001.
• Szafranek, Karol, 2019, "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, volume 35, issue 3, pages 1042-1059, DOI: 10.1016/j.ijforecast.2019.04.007.
• Arifovic, Jasmina & Yıldızoğlu, Murat, 2019, "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, volume 157, issue C, pages 191-208, DOI: 10.1016/j.jebo.2017.11.001.
• Adcock, Robert & Gradojevic, Nikola, 2019, "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 531, issue C, DOI: 10.1016/j.physa.2019.121727.
• Sommervoll, Åvald & Sommervoll, Dag Einar, 2019, "Learning from man or machine: Spatial fixed effects in urban econometrics," Regional Science and Urban Economics, Elsevier, volume 77, issue C, pages 239-252, DOI: 10.1016/j.regsciurbeco.2019.04.005.
• Tiwari, Aviral Kumar & Gupta, Rangan, 2019, "Chaos in G7 stock markets using over one century of data: A note," Research in International Business and Finance, Elsevier, volume 47, issue C, pages 304-310, DOI: 10.1016/j.ribaf.2018.08.005.
• Tiwari, Aviral Kumar & Gupta, Rangan, 2019, "Reprint of: Chaos in G7 stock markets using over one century of data: A note," Research in International Business and Finance, Elsevier, volume 49, issue C, pages 315-321, DOI: 10.1016/j.ribaf.2019.05.002.
• Imaduddin Sahabat & Tumpak Silalahi & Ratih Indrastuti & Marizsa Herlina, 2019, "The interbank payment network and financial system stability," Studies in Economics and Finance, Emerald Group Publishing Limited, volume 37, issue 1, pages 1-17, September, DOI: 10.1108/SEF-10-2018-0310.
• Dejan Zivkov & Slavica Manic & Jasmina Duraskovic & Jelena Kovacevic, 2019, "Bidirectional Nexus between Inflation and Inflation Uncertainty in the Asian Emerging Markets – The GARCH-in-Mean Approach," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, volume 69, issue 6, pages 580-599, December.
• Andrey A. Kozlov & Andrey V. Vlasov, 2019, "Cryptoeconomics: Pilot Study on Investments in ICO Startups Using Neural Networks," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 76-87, February, DOI: 10.31107/2075-1990-2019-1-76-87.
• Charlie Joyez, 2019, "Alignment of Multinational Firms along Global Value Chains: A Network-based Perspective," GREDEG Working Papers, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France, number 2019-05, Feb.
• Miriam Steurer & Robert Hill, 2019, "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers, University of Graz, Department of Economics, number 2019-02, Feb.
• Roman Matkovskyy & Taoufik Bouraoui, 2019, "Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model," Post-Print, HAL, number hal-02155402, Jun, DOI: 10.1007/s40953-018-0133-8.
• Jasmina Arifovic & Murat Yildizoglu, 2019, "Learning the Ramsey Outcome in a Kydland & Prescott Economy," Post-Print, HAL, number hal-03428629, Jan, DOI: 10.2139/ssrn.2487941.
• Steffen Q. Mueller & Patrick Ring & Maria Schmidt, 2019, "Forecasting economic decisions under risk: The predictive importance of choice-process data," Working Papers, Chair for Economic Policy, University of Hamburg, number 066, Jan.
• Grodecka, Anna & Hull, Isaiah, 2019, "The Impact of Local Taxes and Public Services on Property Values," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden), number 374, Apr.
• Shahid Anjum & Naveeda Qaseem, 2019, "Big Data Algorithms And Prediction: Bingos And Risky Zones In Sharia Stock Market Index," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, volume 5, issue 3, pages 475-490, November, DOI: https://doi.org/10.21098/jimf.v5i3..
• Şahap KAVCIOĞLU, 2019, "Kurumsal Kredi Skorlamasında Klasik Yöntemlerle Yapay Sinir Ağı Karşılaştırması," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, volume 69, issue 2, pages 207-246, December, DOI: 10.26650/ISTJECON2019-0021.
• Boriss Siliverstovs & Daniel Wochner, 2019, "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 19-463, Oct, DOI: 10.3929/ethz-b-000374306.
• Nazaraghaei, Mehdi & Ghiasi, Hosein & Asgharkhah Chafi, Mohammad, 2019, "Classification of Customer’s Credit Risk Using Ensemble learning (Case study: Sepah Bank)," Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی), Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, volume 12, issue 39, pages 166-129, May.
• Stefania Albanesi & Domonkos F. Vamossy, 2019, "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers, National Bureau of Economic Research, Inc, number 26165, Aug.
• Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2019, "Financial Frictions and the Wealth Distribution," NBER Working Papers, National Bureau of Economic Research, Inc, number 26302, Sep.
• Kireyev, A., 2019, "A Network Model of Multilateral Equilibrium Exchange Rates," Journal of the New Economic Association, New Economic Association, volume 41, issue 1, pages 12-33.
• Arnaud Pincet & Shu Okabe & Martin Pawelczyk, 2019, "Linking Aid to the Sustainable Development Goals – a machine learning approach," OECD Development Co-operation Working Papers, OECD Publishing, number 52, Feb, DOI: 10.1787/4bdaeb8c-en.
• Jesus Fernandez-Villaverde & Samuel Hurtado & Galo Nuno, 2019, "Financial Frictions and the Wealth Distribution," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 19-015, Sep.
• Jaromir Vrbka & Elvira Nica & Ivana Podhorska, 2019, "The application of Kohonen networks for identification of leaders in the trade sector in Czechia," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 14, issue 4, pages 739-761, December, DOI: 10.24136/eq.2019.034.
• Fajar, Muhammad, 2019, "An application of hybrid forecasting singular spectrum analysis – extreme learning machine method in foreign tourists forecasting," MPRA Paper, University Library of Munich, Germany, number 105044, Oct, revised 31 Oct 2019.
• Hossain, Md. Mobarak & Chowdhury, Md Niaz Murshed, 2019, "Econometric Ways to Estimate the Age and Price of Abalone," MPRA Paper, University Library of Munich, Germany, number 91210, Jan.
• Hollenbeck, Brett & Taylor, Wayne, 2019, "Leveraging Loyalty Programs Using Competitor Based Targeting," MPRA Paper, University Library of Munich, Germany, number 92900.
• Brummelhuis, Raymond & Luo, Zhongmin, 2019, "Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques," MPRA Paper, University Library of Munich, Germany, number 94779, Mar.
• Bucci, Andrea, 2019, "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper, University Library of Munich, Germany, number 95137, Jul.
• Bucci, Andrea, 2019, "Realized Volatility Forecasting with Neural Networks," MPRA Paper, University Library of Munich, Germany, number 95443, Aug.
• Zolnikov, Pavel & Zubov, Maxim & Nikitinsky, Nikita & Makarov, Ilya, 2019, "Efficient Algorithms for Constructing Multiplex Networks Embedding," MPRA Paper, University Library of Munich, Germany, number 97310, Sep, revised 23 Sep 2019.
• Benkovich, Nikita & Dedenok, Roman & Golubev, Dmitry, 2019, "Deep Quarantine for Suspicious Mail," MPRA Paper, University Library of Munich, Germany, number 97311, Sep, revised 23 Sep 2019.