Research classified by Journal of Economic Literature (JEL) codes

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

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This topic is covered by the following reading lists:

1. SOEP based publications
2. Technology Assessment

2024

• Gharad Bryan & Dean Karlan & Adam Osman, 2024. "Big Loans to Small Businesses: Predicting Winners and Losers in an Entrepreneurial Lending Experiment," American Economic Review, American Economic Association, vol. 114(9), pages 2825-2860, September.
• Anil Kumar & Che-Yuan Liang, 2024. "Labor Market Effects of Credit Constraints: Evidence from a Natural Experiment," American Economic Journal: Economic Policy, American Economic Association, vol. 16(3), pages 1-26, August.
  • Anil Kumar & Che-Yuan Liang, 2018. "Labor Market Effects of Credit Constraints: Evidence from a Natural Experiment," Working Papers 1810, Federal Reserve Bank of Dallas, revised 04 Feb 2023.
• Nikhil Agarwal & Ray Huang & Alex Moehring & Pranav Rajpurkar & Tobias Salz & Feiyang Yu, 2024. "Comparative Advantage of Humans versus AI in the Long Tail," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 618-622, May.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024. "The Unreasonable Effectiveness of Algorithms," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 623-627, May.
• Annie Liang & Jay Lu, 2024. "Algorithmic Fairness and Social Welfare," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 628-632, May.
  • Annie Liang & Jay Lu, 2024. "Algorithmic Fairness and Social Welfare," Papers 2404.04424, arXiv.org.
• Victoria Angelova & Will Dobbie & Crystal S. Yang, 2024. "Algorithmic Recommendations When the Stakes Are High: Evidence from Judicial Elections," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 633-637, May.
• Hauke Licht & Ronja Sczepanski & Moritz Laurer & Ayjeren Bekmuratovna, 2024. "No More Cost in Translation: Validating Open-Source Machine Translation for Quantitative Text Analysis," ECONtribute Discussion Papers Series 276, University of Bonn and University of Cologne, Germany.
• Hauke Licht & Ronja Sczepanksi, 2024. "Who are They Talking About? Detecting Mentions of Social Groups in Political Texts with Supervised Learning," ECONtribute Discussion Papers Series 277, University of Bonn and University of Cologne, Germany.
• Michal Mec & Mikulas Zeman & Klara Cermakova, 2024. "Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market," International Journal of Economic Sciences, European Research Center, vol. 13(2), pages 87-103, November.
• Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
  • Bartosz Bieganowski & Robert Slepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers 2404.01866, arXiv.org, revised Jun 2024.
• Annie Liang & Jay Lu, 2024. "Algorithmic Fairness and Social Welfare," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 628-632, May.
  • Annie Liang & Jay Lu, 2024. "Algorithmic Fairness and Social Welfare," Papers 2404.04424, arXiv.org.
• Adam Korniejczuk & Robert Ślepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers 2024-09, Faculty of Economic Sciences, University of Warsaw.
  • Adam Korniejczuk & Robert 'Slepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Papers 2406.10695, arXiv.org.
• Zuzanna Kostecka & Robert Ślepaczuk, 2024. "Improving Realized LGD approximation: A Novel Framework with XGBoost for handling missing cash-flow data," Working Papers 2024-12, Faculty of Economic Sciences, University of Warsaw.
  • Zuzanna Kostecka & Robert 'Slepaczuk, 2024. "Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data," Papers 2406.17308, arXiv.org.
• Kamil Kashif & Robert Ślepaczuk, 2024. "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Working Papers 2024-07, Faculty of Economic Sciences, University of Warsaw.
  • Kamil Kashif & Robert 'Slepaczuk, 2024. "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Papers 2406.18206, arXiv.org.
• Maciej Wysocki & Robert Ślepaczuk, 2024. "Construction and Hedging of Equity Index Options Portfolios," Working Papers 2024-14, Faculty of Economic Sciences, University of Warsaw.
  • Maciej Wysocki & Robert 'Slepaczuk, 2024. "Construction and Hedging of Equity Index Options Portfolios," Papers 2407.13908, arXiv.org.
• Natalia Roszyk & Robert Ślepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Working Papers 2024-13, Faculty of Economic Sciences, University of Warsaw.
  • Natalia Roszyk & Robert 'Slepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers 2407.16780, arXiv.org.
• Stefania Albanesi & Domonkos F. Vamossy, 2024. "Credit Scores: Performance and Equity," NBER Working Papers 32917, National Bureau of Economic Research, Inc.
  • Stefania Albanesi & Domonkos F. Vamossy, 2024. "Credit Scores: Performance and Equity," Papers 2409.00296, arXiv.org.
• Massimo Guidolin, 2024. "Machine Learning in Portfolio Decisions," World Scientific Book Chapters, in: Marco Corazza & René Garcia & Faisal Shah Khan & Davide La Torre & Hatem Masri (ed.), Artificial Intelligence and Beyond for Finance, chapter 1, pages 1-72, World Scientific Publishing Co. Pte. Ltd..
  • Manuela Pedio & Massimo Guidolin & Giulia Panzeri, 2024. "Machine Learning in Portfolio Decisions," BAFFI CAREFIN Working Papers 24233, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
• Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
  • Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," BIS Working Papers 1188, Bank for International Settlements.
  • Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers 24-15, Bank of Canada.
• Pierre Beck & Pablo Garcia Sanchez & Alban Moura & Julien Pascal & Olivier Pierrard, 2024. "Deep learning solutions of DSGE models: A technical report," BCL working papers 184, Central Bank of Luxembourg.
• Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024. "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers) 850, Bank of Italy, Economic Research and International Relations Area.
• Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
  • Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers 24-15, Bank of Canada.
  • Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," BIS Working Papers 1188, Bank for International Settlements.
• Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
  • Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers 24-15, Bank of Canada.
  • Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," BIS Working Papers 1188, Bank for International Settlements.
• Shang, Linmei & Wang, Jifeng & Schäfer, David & Heckelei, Thomas & Gall, Juergen & Appel, Franziska & Storm, Hugo, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 75(1), pages 235-260.
  • Linmei Shang & Jifeng Wang & David Schäfer & Thomas Heckelei & Juergen Gall & Franziska Appel & Hugo Storm, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 235-260, February.
• Burda Martin & Schroeder Adrian K., 2024. "Recurrent Neural Network GO-GARCH Model for Portfolio Selection," Journal of Time Series Econometrics, De Gruyter, vol. 16(2), pages 67-81.
• Guy Aridor & Rava Azeredo da Silveira & Michael Woodford, 2024. "Information-Constrained Coordination of Economic Behavior," CESifo Working Paper Series 10935, CESifo.
• Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2024. "Neural Network Learning for Nonlinear Economies," CEPR Discussion Papers 19295, C.E.P.R. Discussion Papers.
  • Julian Ashwin & Paul Beaudry & Martin Ellison, 2024. "Neural Network Learning for Nonlinear Economies," Discussion Papers 2432, Centre for Macroeconomics (CFM).
  • Julian Ashwin & Paul Beaudry & Martin Ellison, 2024. "Neural Network Learning for Nonlinear Economies," NBER Working Papers 32807, National Bureau of Economic Research, Inc.
• Aysun Can Turetken & Markus Leippold, 2024. "Battle of Transformers: Adversarial Attacks on Financial Sentiment Models," Swiss Finance Institute Research Paper Series 24-59, Swiss Finance Institute.
• Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024. "High frequency monitoring of credit creation: A new tool for central banks in emerging market economies," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
  • Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024. "High Frequency Monitoring of Credit Creation: A New Tool for Central Banks in Emerging Market Economies," Documentos de trabajo 21077, FLAR.
• Peter B. Dixon & Maureen T. Rimmer & Florian Schiffmann, 2024. "Neural-Network approximation of reduced forms for CGE models explained by elementary examples," Centre of Policy Studies/IMPACT Centre Working Papers g-348, Victoria University, Centre of Policy Studies/IMPACT Centre.
• Julian Ashwin & Paul Beaudry & Martin Ellison, 2024. "Neural Network Learning for Nonlinear Economies," NBER Working Papers 32807, National Bureau of Economic Research, Inc.
  • Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2024. "Neural Network Learning for Nonlinear Economies," CEPR Discussion Papers 19295, C.E.P.R. Discussion Papers.
  • Julian Ashwin & Paul Beaudry & Martin Ellison, 2024. "Neural Network Learning for Nonlinear Economies," Discussion Papers 2432, Centre for Macroeconomics (CFM).
• Hauzenberger , Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2024. "Bayesian Neural Networks for Macroeconomic Analysis," CEPR Discussion Papers 19381, C.E.P.R. Discussion Papers.
• Christian Manuel Moreno Rocha & Melidey Díaz Ospino & Israel Blanco Ramos & Andres Medina Guzman, 2024. "Enhancing Sustainable Mobility: Multi-Criteria Analysis for Electric Vehicle Integration and Policy Implementation," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 205-218, January.
• Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024. "Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 186-194, July.
• Christian Manuel Moreno Rocha & Daina Arenas Buelvas & Sandra De la Hoz Escorcia, 2024. "Evaluation and Ranking of Energy Alternatives for Implementation in Different Geographic Scenarios using Decision Methods: Case Study of Colombia," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 191-202, September.
• Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024. "Transforming Mexico’s Electric Load Infrastructure: A Quantile Transformer Network Deep Learning Approach, 2019-2020," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 527-533, September.
• Nguyen, Jeremy K., 2024. "Human bias in AI models? Anchoring effects and mitigation strategies in large language models," Journal of Behavioral and Experimental Finance, Elsevier, vol. 43(C).
• Pascal, Julien, 2024. "Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
  • Julien Pascal, 2023. "Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator," BCL working papers 172, Central Bank of Luxembourg.
• Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
• Yang, Qu & Yu, Yuanyuan & Dai, Dongsheng & He, Qian & Lin, Yu, 2024. "Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
• Zhang, Wenyu, 2024. "Dynamic monitoring of financial security risks: A novel China financial risk index and an early warning system," Economics Letters, Elsevier, vol. 234(C).
• Baumgärtner, Martin & Zahner, Johannes, 2024. "Talking fragmentation away – Decoding the ’whatever it takes’ effect," Economics Letters, Elsevier, vol. 234(C).
• Abdou, Hussein A. & Elamer, Ahmed A. & Abedin, Mohammad Zoynul & Ibrahim, Bassam A., 2024. "The impact of oil and global markets on Saudi stock market predictability: A machine learning approach," Energy Economics, Elsevier, vol. 132(C).
• Zadeh, Omid Razavi & Romagnoli, Silvia, 2024. "Financing sustainable energy transition with algorithmic energy tokens," Energy Economics, Elsevier, vol. 132(C).
• Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024. "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, vol. 133(C).
• Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
• Deng, Sinan & Inekwe, John & Smirnov, Vladimir & Wait, Andrew & Wang, Chao, 2024. "Seasonality in deep learning forecasts of electricity imbalance prices," Energy Economics, Elsevier, vol. 137(C).
• Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024. "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, vol. 138(C).
• Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
• Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
• Wang, Li & Huang, Yiting & Hong, Zhiwu, 2024. "Digitalization as a double-edged sword: A deep learning analysis of risk management in Chinese banks," International Review of Financial Analysis, Elsevier, vol. 94(C).
• Heger, Julia & Min, Aleksey & Zagst, Rudi, 2024. "Analyzing credit spread changes using explainable artificial intelligence," International Review of Financial Analysis, Elsevier, vol. 94(C).
• Peng, Yaohao & de Moraes Souza, João Gabriel, 2024. "Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?," International Review of Financial Analysis, Elsevier, vol. 95(PB).
• Oehler, Andreas & Horn, Matthias, 2024. "Does ChatGPT provide better advice than robo-advisors?," Finance Research Letters, Elsevier, vol. 60(C).
• Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
• Beckmann, Lars & Hark, Paul F., 2024. "ChatGPT and the banking business: Insights from the US stock market on potential implications for banks," Finance Research Letters, Elsevier, vol. 63(C).
• Hwang, Yoontae & Park, Junpyo & Kim, Jang Ho & Lee, Yongjae & Fabozzi, Frank J., 2024. "Heterogeneous trading behaviors of individual investors: A deep clustering approach," Finance Research Letters, Elsevier, vol. 65(C).
• Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024. "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, vol. 67(PB).
• Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2024. "Deep reinforcement learning for portfolio selection," Global Finance Journal, Elsevier, vol. 62(C).
• Corsaro, Stefania & Marino, Zelda & Scognamiglio, Salvatore, 2024. "Quantile mortality modelling of multiple populations via neural networks," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 114-133.
• Nguyen, Hang & Sherris, Michael & Villegas, Andrés M. & Ziveyi, Jonathan, 2024. "Scenario selection with LASSO regression for the valuation of variable annuity portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 27-43.
• Ballester, Laura & González-Urteaga, Ana & Shen, Long, 2024. "Green bond issuance and credit risk: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 94(C).
• Kurz, Konstantin & Bock, Carolin & Hanschur, Leonard, 2024. "Flip the tweet – the two-sided coin of entrepreneurial empathy and its ambiguous influence on new product development," Journal of Business Venturing, Elsevier, vol. 39(2).
• Comola, Margherita & Inguaggiato, Carla & Mendola, Mariapia, 2024. "Social networks and economic transformation: Evidence from a resettled village in Brazil," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 17-34.
• Moreno-Pérez, Carlos & Minozzo, Marco, 2024. "‘Making text talk’: The minutes of the Central Bank of Brazil and the real economy," Journal of International Money and Finance, Elsevier, vol. 147(C).
• Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
  • Peter Christensen & Paul Francisco & Erica Myers & Hansen Shao & Mateus Souza, 2022. "Energy Efficiency Can Deliver for Climate Policy: Evidence from Machine Learning-Based Targeting," NBER Working Papers 30467, National Bureau of Economic Research, Inc.
• Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024. "High frequency monitoring of credit creation: A new tool for central banks in emerging market economies," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
  • Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024. "High Frequency Monitoring of Credit Creation: A New Tool for Central Banks in Emerging Market Economies," Documentos de trabajo 21077, FLAR.
• Carbó, José Manuel & Gorjón, Sergio, 2024. "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 123-140.
• de França Carvalho, João Vinícius & Guimarães, Acássio Silva, 2024. "Systemic risk assessment using complex networks approach: Evidence from the Brazilian (re)insurance market," Research in International Business and Finance, Elsevier, vol. 67(PA).
• Ghosh, Indranil & Jana, Rabin K., 2024. "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
• Saâdaoui, Foued & Rabbouch, Hana, 2024. "Financial forecasting improvement with LSTM-ARFIMA hybrid models and non-Gaussian distributions," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
• Stiven Agusta & Fuad Rakhman & Jogiyanto Hartono Mustakini & Singgih Wijayana, 2024. "Enhancing the accuracy of stock return movement prediction in Indonesia through recent fundamental value incorporation in multilayer perceptron," Asian Journal of Accounting Research, Emerald Group Publishing Limited, vol. 9(4), pages 358-377, July.
• Luan Thanh Le & Trang Xuan-Thi-Thu, 2024. "Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood," Maritime Business Review, Emerald Group Publishing Limited, vol. 9(3), pages 243-262, July.
• Leonardo E. Torre & Eva E. González & Luis R. Casillas & Jorge A. Alvarado, 2024. "à ndices de sentimiento regionales y su asociación con indicadores oportunos de actividad económica en México, 2016-2021/Regional sentiment indexes and their association with timely indicators of e," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 39(2), pages 349–419-3.
• Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
  • Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16b, Federal Reserve Bank of Atlanta.
• Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
  • Celso Brunetti & Marc Joëts & Valérie Mignon, 2024. "Reasons Behind Words: OPEC Narratives and the Oil Market," Finance and Economics Discussion Series 2024-003, Board of Governors of the Federal Reserve System (U.S.).
  • Valérie Mignon & Celso Brunetti & Marc Joëts, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," EconomiX Working Papers 2023-24, University of Paris Nanterre, EconomiX.
  • Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers 2023-19, CEPII research center.
• Thomas R. Cook & Zach Modig & Nathan M. Palmer, 2024. "Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values," Finance and Economics Discussion Series 2024-075, Board of Governors of the Federal Reserve System (U.S.).
• Norbert Pfeifer & Miriam Steurer, 2024. "Stabilizing Geo-Spatial Surfaces in Data-Sparse Regions -€“ An Application to Residential Property Prices," Graz Economics Papers 2024-11, University of Graz, Department of Economics.
• Tea Šestanović, 2024. "A Comprehensive Approach To Bitcoin Forecasting Using Neural Networks," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), vol. 75(1), pages 62-85.
• Susan Athey & Lisa K. Simon & Oskar N. Skans & Johan Vikstrom & Yaroslav Yakymovych, 2023. "The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets," Papers 2307.06684, arXiv.org, revised Feb 2024.
  • Athey, Susan & Simon, Lisa & Skans, Oskar & Johan Vikström, Johan & Yakymovych, Yaroslav, 2024. "The heterogeneous earnings impact of job lossacross workers, establishments, and markets," Working Paper Series 2024:10, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  • Athey, Susan & Simon, Lisa K. & Skans, Oskar N. & Vikstrom, Johan & Yakymovych, Yaroslav, 2023. "The Heterogeneous Earnings Impact of Job Loss across Workers, Establishments, and Markets," Research Papers 4148, Stanford University, Graduate School of Business.
• Mühlbauer, Sabrina & Weber, Enzo, 2024. "Predicting Job Match Quality: A Machine Learning Approach," IAB-Discussion Paper 202409, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
• Chandan Kumar Roy & Tapati Basak, 2024. "Women in Leadership, Skilled Workforce, and Firm Performance in Bangladesh: A Machine Learning Analysis on Enterprise Survey Data," Croatian Economic Survey, The Institute of Economics, Zagreb, vol. 26(1), pages 59-93, June.
• Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
• Johannes Carow & Niklas M. Witzig, 2024. "Time Pressure and Strategic Risk-Taking in Professional Chess," Working Papers 2404, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
• Zongwu Cai & Pixiong Chen, 2024. "Online Investor Sentiment via Machine Learning," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202411, University of Kansas, Department of Economics, revised Sep 2024.
• Xiaolong Tang & Yuping Song & Xingrui Jiao & Yankun Sun, 2024. "On Forecasting Realized Volatility for Bitcoin Based on Deep Learning PSO–GRU Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2011-2033, May.
• Aparna Gupta & Vipula Rawte & Mohammed J. Zaki, 2024. "Predicting Firm Financial Performance from SEC Filing Changes Using Automatically Generated Dictionary," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 307-334, July.
• Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
• Aike Steentoft & Bu-Sung Lee & Markus Schläpfer, 2024. "Quantifying the uncertainty of mobility flow predictions using Gaussian processes," Transportation, Springer, vol. 51(6), pages 2301-2322, December.
• Beomseok Seo & Younghwan Lee & Hyungbae Cho, 2024. "Measuring News Sentiment of Korea Using Transformer," Korean Economic Review, Korean Economic Association, vol. 40, pages 149-176.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024. "The Unreasonable Effectiveness of Algorithms," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 623-627, May.
  • Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024. "The Unreasonable Effectiveness of Algorithms," NBER Working Papers 32125, National Bureau of Economic Research, Inc.
• Jeff Dominitz & Charles F. Manski, 2024. "Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory," NBER Working Papers 32269, National Bureau of Economic Research, Inc.
• Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2024. "Neural Network Learning for Nonlinear Economies," CEPR Discussion Papers 19295, C.E.P.R. Discussion Papers.
  • Julian Ashwin & Paul Beaudry & Martin Ellison, 2024. "Neural Network Learning for Nonlinear Economies," NBER Working Papers 32807, National Bureau of Economic Research, Inc.
  • Julian Ashwin & Paul Beaudry & Martin Ellison, 2024. "Neural Network Learning for Nonlinear Economies," Discussion Papers 2432, Centre for Macroeconomics (CFM).
• Stefania Albanesi & Domonkos F. Vamossy, 2024. "Credit Scores: Performance and Equity," Papers 2409.00296, arXiv.org.
  • Stefania Albanesi & Domonkos F. Vamossy, 2024. "Credit Scores: Performance and Equity," NBER Working Papers 32917, National Bureau of Economic Research, Inc.
• Antoine Didisheim & Shikun (Barry) Ke & Bryan T. Kelly & Semyon Malamud, 2024. "APT or “AIPT”? The Surprising Dominance of Large Factor Models," NBER Working Papers 33012, National Bureau of Economic Research, Inc.
• Ruslan Goyenko & Bryan T. Kelly & Tobias J. Moskowitz & Yinan Su & Chao Zhang, 2024. "Trading Volume Alpha," NBER Working Papers 33037, National Bureau of Economic Research, Inc.
• Iva Glišic, 2024. "A comparison of using MIDAS and LSTM models for GDP nowcasting," Working Papers Bulletin 22, National Bank of Serbia.
• Georgi Hristov, 2024. "Improving the Quality of Financial Information Through Machine Learning," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 3, pages 529-540, September.
• Fabrice Murtin & Max Salomon-Ermel, 2024. "Nowcasting subjective well-being with Google Trends: A meta-learning approach," OECD Papers on Well-being and Inequalities 27, OECD Publishing.
• Mariapia Mendola & Mengesha Yayo Negasi, 2024. "Nutritional and Schooling Impact of a Social Protection Program in Ethiopia: A Retrospective Analysis of Childhood Exposure," Journal of African Economies, Centre for the Study of African Economies, vol. 33(4), pages 390-410.
• Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2024. "Volatility Forecasting with Machine Learning and Intraday Commonality," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 492-530.
• Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
  • Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
• Maria S. Mavillonio, 2024. "Textual Representation of Business Plans and Firm Success," Discussion Papers 2024/308, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
• Caterina Giannetti & Maria Saveria Mavillonio, 2024. "Crowdfunding Success: Human Insights vs Algorithmic Textual Extraction," Discussion Papers 2024/315, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
• Maria Saveria Mavillonio, 2024. "Natural Language Processing Techniques for Long Financial Document," Discussion Papers 2024/317, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
• Mestiri, Sami, 2024. "Financial applications of machine learning using R software," MPRA Paper 119998, University Library of Munich, Germany.
• Tüzüntürk, Selim, 2024. "Forecasting Drinking Water Sales Values with Artificial Neural Networks: A Comparative Analysis with ARIMA and Winters’ Methods," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 15(4), pages 371-388, October.
• Taoxiong Liu & Huolan Cheng, 2024. "Can The Classical Economic Model Improve The Performance Of Deep Learning? A GDP Forecasting Example," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 86-110, July.
• Alina Cornelia LUCHIAN & Vasile STRAT, 2024. "The Trustworthiness of AI Algorithms and the Simulator Bias in Trading," PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ECONOMICS AND SOCIAL SCIENCES, Bucharest University of Economic Studies, Romania, vol. 6(1), pages 211-220, August.
• Andres Christian Admin De la Huerta Avila, 2024. "The Predictive Power of Central Bank Communication: Evidence from Mexico," Sobre México. Revista de Economía, Sobre México. Temas en economía, vol. 1(9), pages 83-127.
• Francesco Audrino & Jessica Gentner & Simon Stalder, 2024. "Quantifying uncertainty: a new era of measurement through large language models," Working Papers 2024-12, Swiss National Bank.
• Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2024. "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Annals of Operations Research, Springer, vol. 334(1), pages 679-699, March.
• Nikola Gradojevic & Dragan Kukolj, 2024. "Unlocking the black box: Non-parametric option pricing before and during COVID-19," Annals of Operations Research, Springer, vol. 334(1), pages 59-82, March.
• Maria Kubara, 2024. "Spatiotemporal localisation patterns of technological startups: the case for recurrent neural networks in predicting urban startup clusters," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 72(3), pages 797-829, March.
• Axel Groß-Klußmann, 2024. "Learning deep news sentiment representations for macro-finance," Digital Finance, Springer, vol. 6(3), pages 341-377, September.
• Nacira Agram & Bernt Øksendal & Jan Rems, 2024. "Deep learning for quadratic hedging in incomplete jump market," Digital Finance, Springer, vol. 6(3), pages 463-499, September.
• Cosimo Magazzino & Marco Mele & Claudiu Tiberiu Albulescu & Nicholas Apergis & Mihai Ioan Mutascu, 2024. "The presence of a latent factor in gasoline and diesel prices co-movements," Empirical Economics, Springer, vol. 66(5), pages 1921-1939, May.
• Fumitaka Furuoka & Luis A. Gil-Alana & OlaOluwa S. Yaya & Elayaraja Aruchunan & Ahamuefula E. Ogbonna, 2024. "A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis," Empirical Economics, Springer, vol. 66(6), pages 2471-2499, June.
• Bassam A. Ibrahim & Ahmed A. Elamer & Thamir H. Alasker & Marwa A. Mohamed & Hussein A. Abdou, 2024. "Volatility contagion between cryptocurrencies, gold and stock markets pre-and-during COVID-19: evidence using DCC-GARCH and cascade-correlation network," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-28, December.
• Mustafa Tevfik Kartal & Serpil Kılıç Depren & Ugur Korkut Pata & Dilvin Taşkın & Tuba Şavlı, 2024. "Modeling the link between environmental, social, and governance disclosures and scores: the case of publicly traded companies in the Borsa Istanbul Sustainability Index," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-20, December.
• Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024. "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
• Lukas Gonon, 2024. "Deep neural network expressivity for optimal stopping problems," Finance and Stochastics, Springer, vol. 28(3), pages 865-910, July.
• Fuat Sekmen & Isa Demirkol & Haşmet Gökırmak, 2024. "Evaluation of urban transportation preferences with analytical hierarchy process method," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(3), pages 2087-2101, June.
• Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
• Tharindu P. De Alwis & S. Yaser Samadi, 2024. "Stacking-based neural network for nonlinear time series analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 901-924, July.
• Gavin Ooft & Monique Thijn-Baank, 2024. "Measuring Financial Stability in Curaçao and Sint Maarten," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(4), pages 1-2.
• Joana Katina & Joana Katina & Igor Katin & Igor Katin & Vera Komarova, 2024. "Cryptocurrency price forecasting: a comparative analysis of autoregressive and recurrent neural network models," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 11(4), pages 425-436, June.
• Ubarhande Prashant & Chandani Arti & Pathak Mohit & Agrawal Reena & Bagade Sonali, 2024. "Modelling Financial Variables Using Neural Networking to Access Creditworthiness," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 20(2), pages 62-76.
• Dahani Zouhair & Dehhaoui Mohammed & Bousselhami Ahmed & Maatala Nassreddine, 2024. "Analysis of the Determinants of Technological Innovations Within Agri-Food Companies in Morocco," Folia Oeconomica Stetinensia, Sciendo, vol. 24(1), pages 1-21.
• Renigier-Biłozor Małgorzata & Janowski Artur, 2024. "Human-Machine Synergy in Real Estate Similarity Concept," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 13-30.
• Wagner Marco, 2024. "Künstliche Intelligenz: ChatGPT bei EZB-Prognosen," Wirtschaftsdienst, Sciendo, vol. 104(9), pages 592-592.
• Bartosz Bieganowski & Robert Slepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers 2404.01866, arXiv.org, revised Jun 2024.
  • Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
• Kamil Kashif & Robert 'Slepaczuk, 2024. "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Papers 2406.18206, arXiv.org.
  • Kamil Kashif & Robert Ślepaczuk, 2024. "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Working Papers 2024-07, Faculty of Economic Sciences, University of Warsaw.
• Adam Korniejczuk & Robert 'Slepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Papers 2406.10695, arXiv.org.
  • Adam Korniejczuk & Robert Ślepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers 2024-09, Faculty of Economic Sciences, University of Warsaw.
• Sugarbayar Enkhbayar & Robert Ślepaczuk, 2024. "Predictive modeling of foreign exchange trading signals using machine learning techniques," Working Papers 2024-10, Faculty of Economic Sciences, University of Warsaw.
• Zuzanna Kostecka & Robert 'Slepaczuk, 2024. "Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data," Papers 2406.17308, arXiv.org.
  • Zuzanna Kostecka & Robert Ślepaczuk, 2024. "Improving Realized LGD approximation: A Novel Framework with XGBoost for handling missing cash-flow data," Working Papers 2024-12, Faculty of Economic Sciences, University of Warsaw.
• Natalia Roszyk & Robert 'Slepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers 2407.16780, arXiv.org.
  • Natalia Roszyk & Robert Ślepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Working Papers 2024-13, Faculty of Economic Sciences, University of Warsaw.
• Maciej Wysocki & Robert 'Slepaczuk, 2024. "Construction and Hedging of Equity Index Options Portfolios," Papers 2407.13908, arXiv.org.
  • Maciej Wysocki & Robert Ślepaczuk, 2024. "Construction and Hedging of Equity Index Options Portfolios," Working Papers 2024-14, Faculty of Economic Sciences, University of Warsaw.
• Stanisław Łaniewski & Robert Ślepaczuk, 2024. "Enhancing literature review with NLP methods Algorithmic investment strategies case," Working Papers 2024-16, Faculty of Economic Sciences, University of Warsaw.
• Zeeshan Nezami Ansari & Md Mustafa & Rajendra Narayan Paramanik, 2024. "Linkages of International Business Cycle: An Euclidean Distance-Based Network Approach," Economic Research Guardian, Weissberg Publishing, vol. 14(2), pages 163-175, December.
• Jiawen Luo & Tony Klein & Thomas Walther & Qiang Ji, 2024. "Forecasting realized volatility of crude oil futures prices based on machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1422-1446, August.
  • 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 2021/04, Queen's University Belfast, Queen's Business School.
• Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2024. "Estimating Nonlinear Heterogeneous Agent Models with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1499, University of Warwick, Department of Economics.
• Linmei Shang & Jifeng Wang & David Schäfer & Thomas Heckelei & Juergen Gall & Franziska Appel & Hugo Storm, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 235-260, February.
  • Shang, Linmei & Wang, Jifeng & Schäfer, David & Heckelei, Thomas & Gall, Juergen & Appel, Franziska & Storm, Hugo, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 75(1), pages 235-260.
• Tänzer, Alina, 2024. "The effectiveness of central bank purchases of long-term treasury securities: A neural network approach," IMFS Working Paper Series 204, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Holtemöller, Oliver & Kozyrev, Boris, 2024. "Forecasting economic activity using a neural network in uncertain times: Monte Carlo evidence and application to the German GDP," IWH Discussion Papers 6/2024, Halle Institute for Economic Research (IWH).
• Kozyrev, Boris, 2024. "Forecast combination and interpretability using random subspace," IWH Discussion Papers 21/2024, Halle Institute for Economic Research (IWH).
• Büchel, Jan & Engler, Jan, 2024. "Generative KI in Deutschland: Künstliche Intelligenz in Gesellschaft und Unternehmen," IW-Reports 23/2024, Institut der deutschen Wirtschaft (IW) / German Economic Institute.
• Büchel, Jan & Monsef, Roschan, 2024. "Künstliche Intelligenz: Bessere Entlohnung durch Produktivitätsbooster? [Artificial Intelligence: Will boosted productivity lead to better pay?]," IW-Trends – Vierteljahresschrift zur empirischen Wirtschaftsforschung, Institut der deutschen Wirtschaft (IW) / German Economic Institute, vol. 51(2), pages 45-63.
• Gschnaidtner, Christoph & Dehghan, Robert & Hottenrott, Hanna & Schwierzy, Julian, 2024. "Adoption and diffusion of blockchain technology," ZEW Discussion Papers 24-018, ZEW - Leibniz Centre for European Economic Research.
• Asatryan, Zareh & Birkholz, Carlo & Heinemann, Friedrich, 2024. "Evidence-based policy or beauty contest? An LLM-based meta-analysis of EU cohesion policy evaluations," ZEW Discussion Papers 24-037, ZEW - Leibniz Centre for European Economic Research.
• Petar Cisar & Sanja Maravic Cisar & Attila Pásztor, 2024. "Improving Synchronous Motor Modelling with Artificial Intelligence," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 22(3), pages 329-340.

2023

• John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2023. "Supervised Machine Learning for Eliciting Individual Demand," American Economic Journal: Microeconomics, American Economic Association, vol. 15(4), pages 146-182, November.
• Alistair Macaulay & Wenting Song, 2023. "News Media, Inflation, and Sentiment," AEA Papers and Proceedings, American Economic Association, vol. 113, pages 172-176, May.
• Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
• Berta Marcos Ceron & Manuel Monge, 2023. "Financial Technologies (FINTECH) Revolution and Covid-19: Time Trends and Persistence," Review of Development Finance Journal, Chartered Institute of Development Finance, vol. 13(1), pages 58-64.
• Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2023. "Trading on short-term path forecasts of intraday electricity prices. Part II -- Distributional Deep Neural Networks," WORking papers in Management Science (WORMS) WORMS/23/01, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
• Elliott Ash & Stephen Hansen, 2023. "Text Algorithms in Economics," Annual Review of Economics, Annual Reviews, vol. 15(1), pages 659-688, September.
  • Ash, Elliott & Hansen, Stephen, 2023. "Text Algorithms in Economics," CEPR Discussion Papers 18125, C.E.P.R. Discussion Papers.
• Дәулетханұлы Е. // Dauletkhanuly Ye. & Ойшынова Г.А. // Oishynova G.А., 2023. "Применение машинного обучения и искусственного интеллекта монетарным регулятором // Using Machine Learning and Artificial Intelligence by a Monetary Regulator," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue 4, pages 4-19.
• Ajit Desai, 2023. "Machine learning for economics research: when, what and how," Staff Analytical Notes 2023-16, Bank of Canada.
  • Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
• Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Working Papers 23-15, Federal Reserve Bank of Cleveland.
  • Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Papers 2305.19921, arXiv.org.
• Athey, Susan & Simon, Lisa K. & Skans, Oskar N. & Vikstrom, Johan & Yakymovych, Yaroslav, 2023. "The Heterogeneous Earnings Impact of Job Loss across Workers, Establishments, and Markets," Research Papers 4148, Stanford University, Graduate School of Business.
  • Susan Athey & Lisa K. Simon & Oskar N. Skans & Johan Vikstrom & Yaroslav Yakymovych, 2023. "The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets," Papers 2307.06684, arXiv.org, revised Feb 2024.
  • Athey, Susan & Simon, Lisa & Skans, Oskar & Johan Vikström, Johan & Yakymovych, Yaroslav, 2024. "The heterogeneous earnings impact of job lossacross workers, establishments, and markets," Working Paper Series 2024:10, IFAU - Institute for Evaluation of Labour Market and Education Policy.
• Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized Neural Networks for Agent-Based Model Forecasting," Bank of Russia Working Paper Series wps115, Bank of Russia.
  • Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized neural networks for agent-based model forecasting," Papers 2308.05753, arXiv.org.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023. "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers 2023-23, Faculty of Economic Sciences, University of Warsaw.
  • Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023. "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Papers 2309.10546, arXiv.org.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023. "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Working Papers 2023-25, Faculty of Economic Sciences, University of Warsaw.
  • Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023. "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Papers 2309.15640, arXiv.org.
• Teo Ljubicic & Marko Hell, 2023. "Student Success Prediction Using Artificial Neural Networks," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 32(2), pages 361-374, december.
• Johan Brannlund & Helen Lao & Maureen MacIsaac & Jing Yang, 2023. "Predicting Changes in Canadian Housing Markets with Machine Learning," Discussion Papers 2023-21, Bank of Canada.
• Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
  • Ajit Desai, 2023. "Machine learning for economics research: when, what and how," Staff Analytical Notes 2023-16, Bank of Canada.
• Pascal, Julien, 2024. "Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
  • Julien Pascal, 2023. "Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator," BCL working papers 172, Central Bank of Luxembourg.
• Julien Pascal, 2023. "Rental housing market and directed search," BCL working papers 179, Central Bank of Luxembourg.
• Mercedes de Luis & Emilio Rodríguez & Diego Torres, 2023. "Machine learning applied to active fixed-income portfolio management: a Lasso logit approach," Working Papers 2324, Banco de España.
• Katia Boria & Andrea Luciani & Sabina Marchetti & Marco Viticoli, 2023. "Siamese neural networks for detecting banknote printing defects," Temi di discussione (Economic working papers) 34, Bank of Italy, Economic Research and International Relations Area.
• Julián Alonso Cárdenas-Cárdenas & Deicy J. Cristiano-Botia & Nicolás Martínez-Cortés, 2023. "Colombian inflation forecast using Long Short-Term Memory approach," Borradores de Economia 1241, Banco de la Republica de Colombia.
• Karina Acosta & Yuri Reina-Aranza, 2023. "Categorías municipales en Colombia: Avanzando hacia un modelo de descentralización asimétrica," Documentos de trabajo sobre Economía Regional y Urbana 321, Banco de la Republica de Colombia.
• Jonathan Chassot & Michael Creel, 2023. "Constructing Efficient Simulated Moments Using Temporal Convolutional Networks," Working Papers 1412, Barcelona School of Economics.
• Koresh Galil & Ami Hauptman & Rosit Levy Rosenboim, 2023. "Prediction of Corporate Credit Ratings with Machine Learning: Simple Interpretative Models," Working Papers 2308, Ben-Gurion University of the Negev, Department of Economics.
• Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized neural networks for agent-based model forecasting," Papers 2308.05753, arXiv.org.
  • Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized Neural Networks for Agent-Based Model Forecasting," Bank of Russia Working Paper Series wps115, Bank of Russia.
• Kumar, Rishabh & Koshiyama, Adriano & da Costa, Kleyton & Kingsman, Nigel & Tewarrie, Marvin & Kazim, Emre & Roy, Arunita & Treleaven, Philip & Lovell, Zac, 2023. "Deep learning model fragility and implications for financial stability and regulation," Bank of England working papers 1038, Bank of England.
• Anastasios Petropoulos & Evangelos Stavroulakis & Panagiotis Lazaris & Vasilis Siakoulis & Nikolaos Vlachogiannakis, 2023. "Is COVID-19 reflected in AnaCredit dataset? A big data - machine learning approach for analysing behavioural patterns using loan level granular information," Working Papers 315, Bank of Greece.
• Ollech Daniel & Webel Karsten, 2023. "A Random Forest-based Approach to Combining and Ranking Seasonality Tests," Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 117-130, January.
• Michael Mueller-Smith & Benjamin Pyle & Caroline Walker, 2023. "Estimating the Impact of the Age of Criminal Majority: Decomposing Multiple Treatments in a Regression Discontinuity Framework," Working Papers 23-01, Center for Economic Studies, U.S. Census Bureau.
• Lorenzo Bretscher & Jesús Fernández-Villaverde & Simon Scheidegger, 2023. "Ricardian Business Cycles," Swiss Finance Institute Research Paper Series 23-43, Swiss Finance Institute.
• Valérie Mignon & Celso Brunetti & Marc Joëts, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," EconomiX Working Papers 2023-24, University of Paris Nanterre, EconomiX.
  • Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers 2023-19, CEPII research center.
  • Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
  • Celso Brunetti & Marc Joëts & Valérie Mignon, 2024. "Reasons Behind Words: OPEC Narratives and the Oil Market," Finance and Economics Discussion Series 2024-003, Board of Governors of the Federal Reserve System (U.S.).
• Ash, Elliott & Hansen, Stephen, 2023. "Text Algorithms in Economics," CEPR Discussion Papers 18125, C.E.P.R. Discussion Papers.
• Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
  • Ron Kaniel & Zihan Lin & Markus Pelger & Stijn Van Nieuwerburgh, 2022. "Machine-Learning the Skill of Mutual Fund Managers," NBER Working Papers 29723, National Bureau of Economic Research, Inc.
  • Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-Learning the Skill of Mutual Fund Managers," CEPR Discussion Papers 18129, C.E.P.R. Discussion Papers.
• Maximilian Ahrens & Deniz Erdemlioglu & Michael McMahon & Christopher J. Neely & Xiye Yang, 2023. "Mind Your Language: Market Responses to Central Bank Speeches," Working Papers 2023-013, Federal Reserve Bank of St. Louis, revised 28 Sep 2024.
  • Ahrens, Maximilian & Erdemlioglu, Deniz & Mcmahon, Michael & Neely, Christopher J & Yang, Xiye, 2023. "Mind Your Language: Market Responses to Central Bank Speeches," CEPR Discussion Papers 18191, C.E.P.R. Discussion Papers.
• Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers 2023-19, CEPII research center.
  • Valérie Mignon & Celso Brunetti & Marc Joëts, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," EconomiX Working Papers 2023-24, University of Paris Nanterre, EconomiX.
  • Celso Brunetti & Marc Joëts & Valérie Mignon, 2024. "Reasons Behind Words: OPEC Narratives and the Oil Market," Finance and Economics Discussion Series 2024-003, Board of Governors of the Federal Reserve System (U.S.).
  • Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
• Susan Athey & Lisa K. Simon & Oskar N. Skans & Johan Vikstrom & Yaroslav Yakymovych, 2023. "The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets," Papers 2307.06684, arXiv.org, revised Feb 2024.
  • Athey, Susan & Simon, Lisa K. & Skans, Oskar N. & Vikstrom, Johan & Yakymovych, Yaroslav, 2023. "The Heterogeneous Earnings Impact of Job Loss across Workers, Establishments, and Markets," Research Papers 4148, Stanford University, Graduate School of Business.
  • Athey, Susan & Simon, Lisa & Skans, Oskar & Johan Vikström, Johan & Yakymovych, Yaroslav, 2024. "The heterogeneous earnings impact of job lossacross workers, establishments, and markets," Working Paper Series 2024:10, IFAU - Institute for Evaluation of Labour Market and Education Policy.
• Christian Manuel Moreno Rocha & Jorge D. Pertuz Ortiz & Neyder A. Rodriguez Ibanez, 2023. "A Diffuse Analysis Based on Analytical Processes to Prioritize Barriers in the Development of Renewable Energy Technologies in Alignment with the United Nations Sustainable Development Goals: Evidence," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 481-195, July.
• Hamdy Ahmad Aly Alhendawy & Mohammed Galal Abdallah Mostafa & Mohamed Ibrahim Elgohari & Ibrahim Abdalla Abdelraouf Mohamed & Nabil Medhat Arafat Mahmoud & Mohamed Ahmed Mohamed Mater, 2023. "Determinants of Renewable Energy Production in Egypt New Approach: Machine Learning Algorithms," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 679-689, November.
• Kwon, Yujin & Park, Sung Y., 2023. "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, vol. 84(C).
• Chuliá, Helena & Estévez, Marc & Uribe, Jorge M., 2023. "Systemic political risk," Economic Modelling, Elsevier, vol. 125(C).
• Li, Houjian & Zhou, Deheng & Hu, Jiayu & Li, Junwen & Su, Mengying & Guo, Lili, 2023. "Forecasting the realized volatility of Energy Stock Market: A multimodel comparison," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
• Amendolagine, Vito & von Jacobi, Nadia, 2023. "Symbiotic relationships among formal and informal institutions: Comparing five Brazilian cultural ecosystems," Economic Systems, Elsevier, vol. 47(3).
• Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023. "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, vol. 151(C).
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• Benedek Nagy & Manuela Rozalia Gabor & Ioan Bogdan Baco? & Moaaz Kabil & Kai Zhu & Lóránt Dénes Dávid, 2023. "Google and apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(2), pages 419-459, June.
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• Gutierrez-Lythgoe, Antonio, 2023. "Autoempleo y Machine Learning: Una aplicación para España [Self-employment and Machine Learning: An application for Spain]," MPRA Paper 117275, University Library of Munich, Germany.
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• Temel, Tugrul & Phumpiu, Paul, 2023. "Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience," MPRA Paper 118466, University Library of Munich, Germany.
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• Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.
• Francisco J. Delgado & Elena Fernández-Rodríguez & Roberto García-Fernández & Manuel Landajo & Antonio Martínez-Arias, 2023. "Tax avoidance and earnings management: a neural network approach for the largest European economies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
• Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023. "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
• Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 549-568, March.
• Tobias Götze & Marc Gürtler & Eileen Witowski, 2023. "Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market," Journal of Business Economics, Springer, vol. 93(9), pages 1629-1660, November.
• K. Kumaraswamy & N. Ch. Bhatracharyulu, 2023. "Statistical brand switching model: an Hidden Markov approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 942-950, June.
• Indra Budi & Yaniasih Yaniasih, 2023. "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 735-759, January.
• Mei-Mei Lin & Fu-Hsiang Kuo, 2023. "A Principal Component Analysis of Digital Banking Development in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(4), pages 1-2.
• Yelyzaveta Apanovych & Yelyzaveta Apanovych & Stanislav Prágr, 2023. "Determination of iron procurement strategy for manufacturing companies," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 11(2), pages 331-348, December.
• Santiago Picasso, 2023. "Crecimiento y convergencia: un análisis desde la teoría de grafos," Documentos de Trabajo (working papers) 23-15, Instituto de Economía - IECON.
• Diana Barro & Luca Barzanti & Marco Corazza & Martina Nardon, 2023. "Machine Learning and Fundraising: Applications of Artificial Neural Networks," Working Papers 2023: 33, Department of Economics, University of Venice "Ca' Foscari".
• Adelaide Baronchelli & Roberto Ricciuti & Mattia Viale, 2023. "Elite persistence in medieval Venice after the Black Death," Working Papers 01/2023, University of Verona, Department of Economics.
• Sabek Amine, 2023. "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 9(1), pages 16-32, July.
• Souto Hugo Gobato & Moradi Amir, 2023. "Forecasting realized volatility through financial turbulence and neural networks," Economics and Business Review, Sciendo, vol. 9(2), pages 133-159, April.
• Kaczmarek Tomasz & Grobelny Przemysław, 2023. "How to fly to safety without overpaying for the ticket," Economics and Business Review, Sciendo, vol. 9(2), pages 160-183, April.
• Węcel Krzysztof & Sawiński Marcin & Stróżyna Milena & Lewoniewski Włodzimierz & Księżniak Ewelina & Stolarski Piotr & Abramowicz Witold, 2023. "Artificial intelligence—friend or foe in fake news campaigns," Economics and Business Review, Sciendo, vol. 9(2), pages 41-70, April.
• Manta Eduard Mihai & Bogoevici Flavia, 2023. "Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes," Journal of Social and Economic Statistics, Sciendo, vol. 12(2), pages 88-108, December.
• Maudud Hassan Uzzal & Robert Ślepaczuk, 2023. "The performance of time series forecasting based on classical and machine learning methods for S&P 500 index," Working Papers 2023-05, Faculty of Economic Sciences, University of Warsaw.
• Karol Chojnacki & Robert Ślepaczuk, 2023. "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers 2023-15, Faculty of Economic Sciences, University of Warsaw.
• Damian Ślusarczyk & Robert Ślepaczuk, 2023. "Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models," Working Papers 2023-17, Faculty of Economic Sciences, University of Warsaw.
• Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023. "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers 2023-20, Faculty of Economic Sciences, University of Warsaw.
• Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023. "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Papers 2309.10546, arXiv.org.
  • Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023. "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers 2023-23, Faculty of Economic Sciences, University of Warsaw.
• Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023. "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Papers 2309.15640, arXiv.org.
  • Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023. "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Working Papers 2023-25, Faculty of Economic Sciences, University of Warsaw.
• Sahil Teymurzade & Robert Ślepaczuk, 2023. "Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models," Working Papers 2023-27, Faculty of Economic Sciences, University of Warsaw.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
  • Jesus Fernandez-Villaverde & Samuel Hurtado & Galo Nuno, 2019. "Financial Frictions and the Wealth Distribution," PIER Working Paper Archive 19-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial frictions and the wealth distribution," Working Papers 2013, Banco de España.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2019. "Financial Frictions and the Wealth Distribution," NBER Working Papers 26302, National Bureau of Economic Research, Inc.
  • Fernández-Villaverde, Jesús & Hurtado, Samuel & Nuño, Galo, 2019. "Financial Frictions and the Wealth Distribution," CEPR Discussion Papers 14002, C.E.P.R. Discussion Papers.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial Frictions and the Wealth Distribution," CESifo Working Paper Series 8482, CESifo.
• Mohamad Hassan Shahrour & Mostafa Dekmak, 2023. "Intelligent stock prediction: A neural network approach," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 1-14, March.
• Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
• Stempel, Daniel & Zahner, Johannes, 2023. "Whose inflation rates matter most? A DSGE model and machine learning approach to monetary policy in the Euro area," IMFS Working Paper Series 188, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Baumgärtner, Martin & Zahner, Johannes, 2023. "Whatever it takes to understand a central banker: Embedding their words using neural networks," IMFS Working Paper Series 194, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Büchel, Jan & Engler, Jan & Mertens, Armin, 2023. "Gesuchte Datenkompetenzen in Deutschland [The demand for data skills in Germany]," IW-Trends – Vierteljahresschrift zur empirischen Wirtschaftsforschung, Institut der deutschen Wirtschaft (IW) / German Economic Institute, vol. 50(2), pages 3-17.
• Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
• Singla, Shikhar, 2023. "Regulatory costs and market power," LawFin Working Paper Series 47, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
• Foltas, Alexander, 2023. "Quantifying priorities in business cycle reports: Analysis of recurring textual patterns around peaks and troughs," Working Papers 44, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
• Stempel, Daniel & Zahner, Johannes, 2023. "Whose Inflation Rates Matter Most? A DSGE Model and Machine Learning Approach to Monetary Policy in the Euro Area," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277627, Verein für Socialpolitik / German Economic Association.
• Holtemöller, Oliver & Kozyrev, Boris, 2023. "Forecasting Economic Activity with a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to German GDP," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277688, Verein für Socialpolitik / German Economic Association.

2022

• Mikkel Bennedsen & Eric Hillebrand & Sebastian Jensen, 2022. "A Neural Network Approach to the Environmental Kuznets Curve," CREATES Research Papers 2022-09, Department of Economics and Business Economics, Aarhus University.
• Francesco Bianchi & Sydney C. Ludvigson & Sai Ma, 2022. "Belief Distortions and Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 112(7), pages 2269-2315, July.
  • Bianchi, Francesco & Ludvigson, Sydney & Ma, Sai, 2020. "Belief Distortions and Macroeconomic Fluctuations," CEPR Discussion Papers 15003, C.E.P.R. Discussion Papers.
  • Francesco Bianchi & Sydney C. Ludvigson & Sai Ma, 2020. "Belief Distortions and Macroeconomic Fluctuations," NBER Working Papers 27406, National Bureau of Economic Research, Inc.
• Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2022. "Peer Effects in Product Adoption," American Economic Journal: Applied Economics, American Economic Association, vol. 14(3), pages 488-526, July.
  • Theresa Kuchler & Arlene Wong & Johannes Stroebel, 2018. "Peer effects in product adoption," 2018 Meeting Papers 1001, Society for Economic Dynamics.
  • Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2019. "Peer effects in product adoption," CESifo Working Paper Series 7685, CESifo.
  • Michael Bailey & Drew M. Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2019. "Peer Effects in Product Adoption," NBER Working Papers 25843, National Bureau of Economic Research, Inc.
  • Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2021. "Peer Effects in Product Adoption," Working Papers 2021-66, Princeton University. Economics Department..
  • Ströbel, Johannes & Bailey, Michael & Johnston, Drew & Kuchler, Theresa & Wong, Arlene, 2019. "Peer Effects in Product Adoption," CEPR Discussion Papers 13731, C.E.P.R. Discussion Papers.
• Ignacia Mercadal, 2022. "Dynamic Competition and Arbitrage in Electricity Markets: The Role of Financial Players," American Economic Journal: Microeconomics, American Economic Association, vol. 14(3), pages 665-699, August.
• Arman Khachiyan & Anthony Thomas & Huye Zhou & Gordon Hanson & Alex Cloninger & Tajana Rosing & Amit K. Khandelwal, 2022. "Using Neural Networks to Predict Microspatial Economic Growth," American Economic Review: Insights, American Economic Association, vol. 4(4), pages 491-506, December.
  • Arman Khachiyan & Anthony Thomas & Huye Zhou & Gordon H. Hanson & Alex Cloninger & Tajana Rosing & Amit Khandelwal, 2021. "Using Neural Networks to Predict Micro-Spatial Economic Growth," NBER Working Papers 29569, National Bureau of Economic Research, Inc.
• Rossella Calvi & Jacob Penglase & Denni Tommasi, 2022. "Measuring Women's Empowerment in Collective Households," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 556-560, May.
• Laurence Kotlikoff, 2022. "Does Prediction Machines Predict Our AI Future? A Review," Journal of Economic Literature, American Economic Association, vol. 60(3), pages 1052-1057, September.
• Mariam Dundua & Otar Gorgodze, 2022. "Application of Artificial Intelligence for Monetary Policy-Making," NBG Working Papers 02/2022, National Bank of Georgia.
• Mariam Dundua & Otar Gorgodze, 2022. "Application of Artificial Intelligence for Monetary Policy-Making," NBG Working Papers 02/2022, National Bank of Georgia.
• ȘTefan Bolotä‚ & Mircea Asandului, 2022. "Using Machine Learning In Detecting Fake News," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 30, pages 53-66, December.
• Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
  • Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
  • Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers 202207, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2022.
• Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
  • Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
• Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Bank of Russia Working Paper Series wps104, Bank of Russia.
  • Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
• Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022. "Bayesian Neural Networks for Macroeconomic Analysis," Papers 2211.04752, arXiv.org, revised Apr 2024.
  • Hauzenberger , Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2024. "Bayesian Neural Networks for Macroeconomic Analysis," CEPR Discussion Papers 19381, C.E.P.R. Discussion Papers.
• Anton A. Gerunov, 2022. "Performance of 109 Machine Learning Algorithms across Five Forecasting Tasks: Employee Behavior Modeling, Online Communication, House Pricing, IT Support and Demand Planning," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 15-43.
• Andrés Alonso-Robisco & José Manuel Carbó, 2022. "Inteligencia artificial y finanzas: una alianza estratégica," Occasional Papers 2222, Banco de España.
• José Manuel Carbó & Sergio Gorjón, 2022. "Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin," Working Papers 2215, Banco de España.
• Carlos Moreno Pérez & Marco Minozzo, 2022. "Natural Language Processing and Financial Markets: Semi-supervised Modelling of Coronavirus and Economic News," Working Papers 2228, Banco de España.
• Carlos Moreno Pérez & Marco Minozzo, 2022. "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers 2229, Banco de España.
• Carlos Moreno Pérez & Marco Minozzo, 2022. "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers 2240, Banco de España.
• Valerio Astuti & Alessio Ciarlone & Alberto Coco, 2022. "The role of central bank communication in inflation-targeting Eastern European emerging economies," Temi di discussione (Economic working papers) 1381, Bank of Italy, Economic Research and International Relations Area.
• Torre Leonardo & González Eva & Casillas Ramón & Alvarado Jorge, 2022. "Sentiment Indexes and Economic Activity Indicators in Mexico 2016-2021," Working Papers 2022-18, Banco de México.
• Luis Gerardo Gage & Raúl Morales-Resendiz & John Arroyo & Jeniffer Rubio & Paolo Barucca, 2022. "Classifying payment patterns with artificial neural networks: an autoencoder approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
  • 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, vol. 1(1).
• Jošić Hrvoje & Žmuk Berislav, 2022. "A Machine Learning Approach to Forecast International Trade: The Case of Croatia," Business Systems Research, Sciendo, vol. 13(3), pages 144-160, October.
• Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
  • Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Bank of Russia Working Paper Series wps104, Bank of Russia.
• Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
  • Huber, Martin & Imhof, David, 2020. "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers 519, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
• Buckmann, Marcus & Joseph, Andreas, 2022. "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers 984, Bank of England.
• Ash, Elliott & Durante, Ruben & Grebenshchikova, Mariia & Schwarz, Carlo, 2022. "Visual Representation and Stereotypes in News Media," CEPR Discussion Papers 16624, C.E.P.R. Discussion Papers.
  • Elliott Ash & Ruben Durante & Maria Grebenshchikova & Carlo Schwarz, 2022. "Visual Representation and Stereotypes in News Media," CESifo Working Paper Series 9686, CESifo.
• David Anderson & Urban Ulrych, 2022. "Accelerated American Option Pricing with Deep Neural Networks," Swiss Finance Institute Research Paper Series 22-03, Swiss Finance Institute.
• Dongshuai Zhao & Zhongli Wang & Florian Schweizer-Gamborino & Didier Sornette, 2022. "Polytope Fraud Theory," Swiss Finance Institute Research Paper Series 22-41, Swiss Finance Institute.
• Oksana Bashchenko, 2022. "Bitcoin Price Factors: Natural Language Processing Approach," Swiss Finance Institute Research Paper Series 22-48, Swiss Finance Institute.
• Fredy Cepeda-Lopez & Fredy Gamboa-Estrada & Carlos Leon-Rincón & Hernán Rincon-Castro, 2022. "Colombian Liberalization and Integration into World Trade Markets: Much Ado about Nothing," Revista de Economía del Rosario, Universidad del Rosario, vol. 25(2), pages 1-44, December.
  • 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 1065, Banco de la Republica de Colombia.
• Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2022. "Estimating Nonlinear Heterogeneous Agents Models with Neural Networks," CEPR Discussion Papers 17391, C.E.P.R. Discussion Papers.
• Katumullage, Duwani & Yang, Chenyu & Barth, Jackson & Cao, Jing, 2022. "Using Neural Network Models for Wine Review Classification," Journal of Wine Economics, Cambridge University Press, vol. 17(1), pages 27-41, February.
• Yang, Chenyu & Barth, Jackson & Katumullage, Duwani & Cao, Jing, 2022. "Wine Review Descriptors as Quality Predictors: Evidence from Language Processing Techniques," Journal of Wine Economics, Cambridge University Press, vol. 17(1), pages 64-80, February.
• Festus Victor Bekun, 2022. "Mitigating Emissions in India: Accounting for the Role of Real Income, Renewable Energy Consumption and Investment in Energy," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 188-192.
• Christian Manuel Moreno Rocha & Jose Ricardo Nunez Alvarez & Daniel A. Diaz Castillo & Esnaider D. Florian Domingue & Juan Camilo Barrera Hernandez, 2022. "Implementation of the Hierarchical Analytical Process in the Selection of the Best Source of Renewable Energy in the Colombian Caribbean Region," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 111-119, March.
• Adrian-Nicolae Buturache & Stelian Stancu, 2022. "Building Energy Consumption Prediction Using Neural-Based Models," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 30-38, March.
• Christian Manuel Moreno Rocha & Esnaider D. Florian Domingue & Daniel A. Diaz Castillo & Kevin Logreira Vargas & Andres Alfredo Medina Guzman, 2022. "Evaluation of Energy Alternatives through FAHP for the Energization of Colombian Insular Areas," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 87-98, July.
• Christian Manuel Moreno Rocha & David Fernandez Perez & Jesus Rodriguez Retamoza & Jorge Silva Ortega & Denis Brieva Bohorquez & Luis Taborda Catalan, 2022. "Evaluation, Hierarchy and Selection of the best Source of Energy by using AHP, as a Proposed Solution to an Energy and Socio-economic Problem, in the case of Colombia s Pacific Zone," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 409-419, September.
• Davidescu, Adriana AnaMaria & Petcu, Monica Aureliana & Curea, Stefania Cristina & Manta, Eduard Mihai, 2022. "Two faces of the same coin: Exploring the multilateral perspective of informality in relation to Sustainable Development Goals based on bibliometric analysis," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 683-705.
• Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
• Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
• Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
  • Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021. "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series 8835, CESifo.
• Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
• Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
• Ghabri, Yosra & Ben Rhouma, Oussama & Gana, Marjène & Guesmi, Khaled & Benkraiem, Ramzi, 2022. "Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
• Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
• Kutuk, Yasin & Barokas, Lina, 2022. "Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries," Finance Research Letters, Elsevier, vol. 45(C).
• García-Céspedes, Rubén & Moreno, Manuel, 2022. "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
• Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
• Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).
• Al-Mudafer, Muhammed Taher & Avanzi, Benjamin & Taylor, Greg & Wong, Bernard, 2022. "Stochastic loss reserving with mixture density neural networks," Insurance: Mathematics and Economics, Elsevier, vol. 105(C), pages 144-174.
• Xu, Shuzhe & Zhang, Chuanlong & Hong, Don, 2022. "BERT-based NLP techniques for classification and severity modeling in basic warranty data study," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 57-67.
• Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022. "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
• Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
• Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
• Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
• Ouyang, Zisheng & Chen, Shili & Lai, Yongzeng & Yang, Xite, 2022. "The correlations among COVID-19, the effect of public opinion, and the systemic risks of China’s financial industries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  • Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
• Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022. "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, vol. 60(C).
• Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
• Bernt P. Stigum, 2022. "Consumer Choice under Certainty and Uncertainty in Applied Econometrics," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 65(1), pages 1-27.
• Adriana Anamaria Davidescu & Răzvan Gabriel Hapau & Eduard Mihai Manta, 2022. "Impact of Crises on Capital Market Volatility: A Bibliometric Analysis," Contemporary Studies in Economic and Financial Analysis, in: The New Digital Era: Other Emerging Risks and Opportunities, volume 109, pages 21-53, Emerald Group Publishing Limited.
• Lukas Janasek, 2022. "Acquisition of Costly Information in Data-Driven Decision Making," Working Papers IES 2022/10, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2022.
• Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
  • Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16b, Federal Reserve Bank of Atlanta.
• Nelson P. Rayl & Nitish R. Sinha, 2022. "Integrating Prediction and Attribution to Classify News," Finance and Economics Discussion Series 2022-042, Board of Governors of the Federal Reserve System (U.S.).
• Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022. "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, vol. 15(2), pages 1-30, February.
  • Calvo Pardo, Héctor & Olmo, Jose & Mancini, Tullio, 2021. "Machine Learning the Carbon Footprint of Bitcoin Mining," CEPR Discussion Papers 16267, C.E.P.R. Discussion Papers.
• Susan Ariel Aaronson, 2022. "Wicked Problems Might Inspire Greater Data Sharing," Working Papers 2022-09, The George Washington University, Institute for International Economic Policy.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  • Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  • Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  • Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
  • Pierre-Philippe Combes & Laurent Gobillon & yanos Zylberberg, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," SciencePo Working papers halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & yanos Zylberberg, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & yanos Zylberberg, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Post-Print halshs-03673240, HAL.
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," SciencePo Working papers halshs-03231786, HAL.
• Südbeck, Insa & Mindlina, Julia & Schnabel, André & Helber, Stefan, 2022. "Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines," Hannover Economic Papers (HEP) dp-703, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
• Tea Baldigara, 2022. "Primjena Umjetnih Neuronskih Mreža U Modeliranju Domaće Turističke Potražnje," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), vol. 73(3), pages 349-370.
• Mühlbauer, Sabrina & Weber, Enzo, 2022. "Machine Learning for Labour Market Matching," IAB-Discussion Paper 202203, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
• Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022. "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(1), pages 1-23, Enero - M.
• Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
  • Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers 202207, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2022.
  • Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
• Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022. ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers 202215, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
• Cochrane, William & Poot, Jacques & Roskruge, Matthew, 2022. "Urban Resilience and Social Security Uptake: New Zealand Evidence from the Global Financial Crisis and the COVID-19 Pandemic," IZA Discussion Papers 15510, Institute of Labor Economics (IZA).
• William A. Barnett & Kun He & Jingtong He, 2022. "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," JRFM, MDPI, vol. 15(10), pages 1-17, October.
  • William Barnett & Kun He & Jingtong He, 2022. "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202217, University of Kansas, Department of Economics.
• Giovanni Villani, 2022. "A Neural Network Approach to Value R&D Compound American Exchange Option," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 305-324, June.
• Reza Bradrania & Davood Pirayesh Neghab & Mojtaba Shafizadeh, 2022. "State-dependent stock selection in index tracking: a machine learning approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(1), pages 1-28, March.
• Alexander V. Giczy & Nicholas A. Pairolero & Andrew A. Toole, 2022. "Identifying artificial intelligence (AI) invention: a novel AI patent dataset," The Journal of Technology Transfer, Springer, vol. 47(2), pages 476-505, April.
• Paolo Savona, 2022. "Prospects for Reforming the Money and Financial System," Open Economies Review, Springer, vol. 33(1), pages 187-195, February.
• Chris Charalambous & Spiros H. Martzoukos & Zenon Taoushianis, 2022. "Estimating corporate bankruptcy forecasting models by maximizing discriminatory power," Review of Quantitative Finance and Accounting, Springer, vol. 58(1), pages 297-328, January.
• Miriam Sosa & Christian Bucio & Edgar Ortiz Calisto, 2022. "Dynamic Stock Dependence and Monetary Variables in the United States (2000- 2016) - A Copula and Neural Network Approach," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 96, pages 201-234, January-J.
• Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
• Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022. "The Virtue of Complexity in Return Prediction," NBER Working Papers 30217, National Bureau of Economic Research, Inc.
• Yacine Aït-Sahalia & Jianqing Fan & Lirong Xue & Yifeng Zhou, 2022. "How and When are High-Frequency Stock Returns Predictable?," NBER Working Papers 30366, National Bureau of Economic Research, Inc.
• Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Papers 2112.11751, arXiv.org.
  • Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
  • Korobilis, Dimitris & Shimizu, Kenichi, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper 111631, University Library of Munich, Germany.
• Rodríguez Guevara, David Esteban & Rendón García, Juan Fernando & Trespalacios Carrasquilla, Alfredo & Jiménez Echeverri, Edwin Andrés, 2022. "Modelación de riesgo de crédito de personas naturales. Un caso aplicado a una caja de compensación familiar colombiana [Natural People Credit Risk Modeling. An applied case in a Colombian Family Be," 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, vol. 33(1), pages 29-48, June.
• Díez, José Ignacio & Giannasi, Nadia & Scudelati, Mariela, 2022. "Redes, conocimientos y capacidades en el contexto de la industria del software. Análisis del caso Bahía Blanca [Network, knowledge and capabilities in the context of the software industry. Analysis," 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, vol. 34(1), pages 137-154, December.
• Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
• Prisznyák, Alexandra, 2022. "Bankrobotics: Artificial Intelligence and Machine Learning Powered Banking Risk Management — Prevention of Money Laundering and Terrorist Financing," Public Finance Quarterly, Corvinus University of Budapest, vol. 67(2), pages 288-303.
• Liu, Mengxiao & Wang, Luhang & Yi, Yimin, 2022. "Quality Innovation, Cost Innovation, Export, and Firm Productivity Evolution: Evidence from the Chinese Electronics Industry," MPRA Paper 113270, University Library of Munich, Germany.
• Yang, Bill Huajian, 2022. "Modeling Path-Dependent State Transition by a Recurrent Neural Network," MPRA Paper 114188, University Library of Munich, Germany, revised 18 Jul 2022.
• Caio Almeida & Jianqing Fan & Gustavo Freire & Francesca Tang, 2022. "Can a Machine Correct Option Pricing Models?," Working Papers 2022-9, Princeton University. Economics Department..
• Damian Przekop, 2022. "Artificial Neural Networks vs Spatial Regression Approach in Property Valuation," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 14(2), pages 199-223, June.
• Aike Steentoft, Bu-Sung Lee, Markus Schl pfer, 2022. "Interpretable Prediction of Urban Mobility Flows with Deep Neural Networks as Gaussian Processes," Diskussionsschriften credresearchpaper36, Universitaet Bern, Departement Volkswirtschaft - CRED.
• Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  • Korobilis, Dimitris & Shimizu, Kenichi, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper 111631, University Library of Munich, Germany.
  • Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Papers 2112.11751, arXiv.org.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
• Radu LUPU & Iulia LUPU & Tanase STAMULE & Mihai ROMAN, 2022. "Entropy as Leading Indicator for Extreme Systemic Risk Events," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 58-73, December.
• Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
• Soham Banerjee & Diganta Mukherjee, 2022. "Short Term Stock Price Prediction in Indian Market: A Neural Network Perspective," Studies in Microeconomics, , vol. 10(1), pages 23-49, June.
• Elda Xhumari & Suela Maxhelaku & Endrit Xhina, 2022. "A review of Knowledge Graph and Graph Neural Network application," Proceedings of Economics and Finance Conferences 13615626, International Institute of Social and Economic Sciences.
• Suela Maxhelaku & Elda Xhumari & Endrit Xhina, 2022. "Exploiting Knowledge Graphs in Healthcare," Proceedings of Economics and Finance Conferences 13615633, International Institute of Social and Economic Sciences.
• Vito Polito & Yunyi Zhang, 2022. "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," Working Papers 2022004, The University of Sheffield, Department of Economics.
• Luca Grilli & Domenico Santoro, 2022. "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, vol. 19(4), pages 665-681, October.
• Charl Maree & Christian W. Omlin, 2022. "Reinforcement learning with intrinsic affinity for personalized prosperity management," Digital Finance, Springer, vol. 4(2), pages 241-262, September.
• Sylwia Sysko-Romańczuk & Piotr Zaborek & Anna Wróblewska & Jacek Dąbrowski & Sergiy Tkachuk, 2022. "Data modalities, consumer attributes and recommendation performance in the fashion industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1279-1292, September.
• Raisul Islam & Vladimir Volkov, 2022. "Contagion or interdependence? Comparing spillover indices," Empirical Economics, Springer, vol. 63(3), pages 1403-1455, September.
• Simon Blöthner & Mario Larch, 2022. "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, vol. 63(4), pages 1771-1807, October.
  • Simon Blöthner & Mario Larch, 2021. "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series 9233, CESifo.
• Adem Baltaci & Raif Cergibozan & Ali Ari, 2022. "Cultural values and the global financial crisis: a missing link?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 507-529, September.
• Andreas Falke & Harald Hruschka, 2022. "Analyzing browsing across websites by machine learning methods," Journal of Business Economics, Springer, vol. 92(5), pages 829-852, July.
• Cuiyuan Wang & Tao Wang & Changhe Yuan & Jane Yihua Rong, 2022. "Learning to trade on sentiment," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 308-323, April.
• Luis Gerardo Hernández García, 2022. "Transport equipment network analysis: the value-added contribution," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 11(1), pages 1-25, December.
• Yu-Min Lian & Jia-Ling Chen & Hsueh-Chien Cheng, 2022. "Predicting Bitcoin Prices via Machine Learning and Time Series Models," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(5), pages 1-2.
• Simona Hašková & Petr Šuleř & Tomáš Krulický, 2022. "Advantages of fuzzy approach compared to probabilistic approach in project evaluation," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 10(1), pages 483-493, September.
• Jaromír Vrbka & Jakub Horák & Tomáš Krulický, 2022. "The influence of world oil prices on the Chinese Yuan exchange rate," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(4), pages 439-462, June.
• Antonio Rodríguez Andrés & Abraham Otero & Voxi Heinrich Amavilah, 2022. "Knowledge economy classification in African countries: A model-based clustering approach," Information Technology for Development, Taylor & Francis Journals, vol. 28(2), pages 372-396, April.
  • Amavilah, Voxi Heinrich & Otero, Abraham & Andres, Antonio Rodriguez, 2021. "Knowledge Economy Classification in African Countries: A Model-Based Clustering Approach," MPRA Paper 109188, University Library of Munich, Germany.
• Matthias Lalisse, 2022. "Measuring the Impact of Campaign Finance on Congressional Voting: A Machine Learning Approach," Working Papers Series inetwp178, Institute for New Economic Thinking.
• Lee Changro, 2022. "Training and Interpreting Machine Learning Models: Application in Property Tax Assessment," Real Estate Management and Valuation, Sciendo, vol. 30(1), pages 13-22, March.
• Sulejmani Artan & Tevdovski Dragan, 2022. "How the Contagion is Transmitted to the Macedonian Stock Market? an Analysis of Co-Exceedances," South East European Journal of Economics and Business, Sciendo, vol. 17(1), pages 1-13, June.
• Baiquan Ma & Robert Ślepaczuk, 2022. "The profitability of pairs trading strategies on Hong-Kong stock market: distance, cointegration, and correlation methods," Working Papers 2022-02, Faculty of Economic Sciences, University of Warsaw.
• Maciej Wysocki & Paweł Sakowski, 2022. "Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models," Working Papers 2022-12, Faculty of Economic Sciences, University of Warsaw.
• Illia Baranochnikov & Robert Ślepaczuk, 2022. "A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy," Working Papers 2022-21, Faculty of Economic Sciences, University of Warsaw.
• Katarzyna Kryńska & Robert Ślepaczuk, 2022. "Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index," Working Papers 2022-25, Faculty of Economic Sciences, University of Warsaw.
• Thi Thu Giang Nguyen & Robert Ślepaczuk, 2022. "The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index," Working Papers 2022-29, Faculty of Economic Sciences, University of Warsaw.
• Mohammad Zoynul Abedin & M. Kabir Hassan & Imran Khan & Ivan F. Julio, 2022. "Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 39(04), pages 1-26, August.
• Tayyab Raza Fraz & Samreen Fatima, 2022. "Modeling And Forecasting Volatility Of Stock Market Using Family Of Garch Models: Evidence From Cpec Linked Countries," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-15, March.
• Tingbin Bian & Jin Chen & Qu Feng & Jingyi Li, 2022. "Comparing Econometric Analyses With Machine Learning Approaches: A Study On Singapore Private Property Market," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 67(06), pages 1787-1810, December.
• Molina, José Alberto & Iñíguez, David & Ruiz, Gonzalo & Tarancón, Alfonso, 2022. "Networks in Population Economics: production and collaborations," GLO Discussion Paper Series 1051, Global Labor Organization (GLO).
• Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
• Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Zahner, Johannes & Baumgärtner, Martin, 2022. "Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks," VfS Annual Conference 2022 (Basel): Big Data in Economics 264019, Verein für Socialpolitik / German Economic Association.

2021

• Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
• Metin Aktas & Osman Taylan, 2021. "Determinants of Islamic Banks’ Profitability Using Panel Data Analysis and ANFIS Approaches in Saudi Arabia محددات ربحية المصارف الإسلامية باستخدام تحليل البيانات المَقْطعية وأساليب الاستدلال التَّكَي," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 34(2), pages 19-40, July.
• Francesco Decarolis & Gabriele Rovigatti, 2021. "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising," American Economic Review, American Economic Association, vol. 111(10), pages 3299-3327, October.
  • Decarolis, Francesco & Rovigatti, Gabriele, 2019. "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising," CEPR Discussion Papers 13897, C.E.P.R. Discussion Papers.
• Christopher R. Knittel & Samuel Stolper, 2021. "Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 440-444, May.
• Tetsuya Kaji & Elena Manresa & Guillaume A. Pouliot, 2021. "Adversarial Inference Is Efficient," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 621-625, May.
• Ramos & Pablo Negri & Martín Breitkopf & María Laura Ojeda, 2021. "From International to Regional Commodity Price Pass-through Using Self-Driven Recurrent Networks," Asociación Argentina de Economía Política: Working Papers 4513, Asociación Argentina de Economía Política.
• Adrian-Nicolae Buturache & Stelian Stancu, 2021. "Usage of Neural-Based Predictive Modeling and IIoT in Wind Energy Applications," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(57), pages 412-412.
• Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, vol. 39(2), pages 884-900.
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• Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafal Weron, 2021. "Erratum to 'Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark' [Appl. Energy 293 (2021) 116983]," WORking papers in Management Science (WORMS) WORMS/21/12, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
• Efe Arda & Güray Küçükkocaoğlu, 2021. "Yapay Zeka Yöntemleri İle Hisse Senedi Fiyat Öngörüleri," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 6(2), pages 565-586.
• Denuit, Michel & Charpentier , Arthur & Trufin, Julien, 2021. "Autocalibration and Tweedie-dominance for insurance pricing with machine learning," LIDAM Reprints ISBA 2021049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
• Lachlan O'Neill & Simon D Angus & Satya Borgohain & Nader Chmait & David Dowe, 2021. "Creating Powerful and Interpretable Models with Regression Networks," SoDa Laboratories Working Paper Series 2021-09, Monash University, SoDa Laboratories.
• Lachlan O'Neill & Nandini Anantharama & Wray Buntine & Simon D Angus, 2021. "Quantitative Discourse Analysis at Scale - AI, NLP and the Transformer Revolution," SoDa Laboratories Working Paper Series 2021-12, Monash University, SoDa Laboratories.
• Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
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• Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series 2104, University of St. Gallen, School of Economics and Political Science.
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• Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
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• Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  • Korobilis, Dimitris & Shimizu, Kenichi, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper 111631, University Library of Munich, Germany.
  • Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Papers 2112.11751, arXiv.org.
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• Gavrilenko N. G., 2021. "The use of artificial neural networks when planning the target indicators for the truck haulage development in the Russian Federation," Russian Journal of Social Sciences and Humanities, Omsk Humanitarian Academy, vol. 15(2), pages 213-218, June.
• Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
• Gerardin Mathilde, & Ranvier Martial., 2021. "Enrichment of the Banque de France’s monthly business survey: lessons from textual analysis of business leaders’ comments," Working papers 821, Banque de France.
• Hannes Mueller & André Groeger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2021. "Monitoring War Destruction from Space Using Machine Learning," Working Papers 1257, Barcelona School of Economics.
• Gambacorta, Leonardo & Amstad, Marlene & He, Chao & XIA, Fan Dora, 2021. "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," CEPR Discussion Papers 15682, C.E.P.R. Discussion Papers.
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• Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
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• Fernández-Villaverde, Jesús & Ebrahimi Kahou, Mahdi & Perla, Jesse & Sood, Arnav, 2021. "Exploiting Symmetry in High-Dimensional Dynamic Programming," CEPR Discussion Papers 16285, C.E.P.R. Discussion Papers.
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• Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2021. "Optimal Price Targeting," CESifo Working Paper Series 9439, CESifo.
• Alexis Marchal, 2021. "Risk & Returns around Fomc Press Conferences: A Novel Perspective from Computer Vision," Swiss Finance Institute Research Paper Series 21-18, Swiss Finance Institute.
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• Blanka Horvath & Josef Teichmann & Zan Zuric, 2021. "Deep Hedging under Rough Volatility," Swiss Finance Institute Research Paper Series 21-88, Swiss Finance Institute.
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• Penafiel Chang, Luis Eduardo, 2021. "Panorama económico, político y sanitario de América Latina y el Caribe al comienzo de la pandemia del COVID-19," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 95, pages 11-44, July.
• Sosa Castro, Magnolia Miriam & Bucio Pacheco, Christian & Ortiz Calisto, Edgar, 2021. "Dynamic Stock Dependence and Monetary Variables in the United States (2000-2016): A Copula and Neural Network Approach," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 96, pages 201-234, November.
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• Smith, Adam & Seiler, Stephan & Aggarwal, Ishant, 2022. "Optimal Price Targeting," CEPR Discussion Papers 16096, C.E.P.R. Discussion Papers.
• Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022. "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, vol. 15(2), pages 1-30, February.
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• Mahdi Ebrahimi Kahou & Jesús Fernández-Villaverde & Jesse Perla & Arnav Sood, 2021. "Exploiting Symmetry in High-Dimensional Dynamic Programming," CESifo Working Paper Series 9161, CESifo.
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• Elliott Ash & Ruben Durante & Maria Grebenshchikova & Carlo Schwarz, 2022. "Visual Representation and Stereotypes in News Media," CESifo Working Paper Series 9686, CESifo.
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• Comola, Margherita & Inguaggiato, Carla & Mendola, Mariapia, 2021. "Learning about Farming: Innovation and Social Networks in a Resettled Community in Brazil," IZA Discussion Papers 14092, Institute of Labor Economics (IZA).
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• Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
• Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
• Pavel Baboshkin & Mafura Uandykova, 2021. "Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 384-391.
• Felix Ghislain Yem Souhe & Camille Franklin Mbey & Alexandre Teplaira Boum & Pierre Ele, 2021. "Forecasting of Electrical Energy Consumption of Households in a Smart Grid," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 221-233.
• Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
• Fang, Ming & Taylor, Stephen, 2021. "A machine learning based asset pricing factor model comparison on anomaly portfolios," Economics Letters, Elsevier, vol. 204(C).
• Su, Jiun-Hua, 2021. "Model selection in utility-maximizing binary prediction," Journal of Econometrics, Elsevier, vol. 223(1), pages 96-124.
• Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
• Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021. "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, vol. 48(C).
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• Delong, Łukasz & Lindholm, Mathias & Wüthrich, Mario V., 2021. "Gamma Mixture Density Networks and their application to modelling insurance claim amounts," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 240-261.
• Denuit, Michel & Charpentier, Arthur & Trufin, Julien, 2021. "Autocalibration and Tweedie-dominance for insurance pricing with machine learning," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 485-497.
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• Kireyev, Alexei & Leonidov, Andrei, 2021. "Twin trade shocks: Spillovers from US-China trade tensions," International Economics, Elsevier, vol. 167(C), pages 174-188.
• Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
• Bhar, Ramaprasad & Malliaris, A.G., 2021. "Modeling U.S. monetary policy during the global financial crisis and lessons for Covid-19," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 15-33.
• Shen, Lily & Ross, Stephen, 2021. "Information value of property description: A Machine learning approach," Journal of Urban Economics, Elsevier, vol. 121(C).
• Sabetti, Leonard & Heijmans, Ronald, 2021. "Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).
• Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.
• Gaggero, Alberto A. & Piazza, Giovanni, 2021. "Multilayer networks and route entry into the airline industry: Evidence from the U.S. domestic market," Research in Transportation Economics, Elsevier, vol. 90(C).
• Chen, Shun & Ge, Lei, 2021. "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 936-942.
• Barrales-Ruiz, Jose & Arnim, Rudiger von, 2021. "Endogenous fluctuations in demand and distribution: An empirical investigation," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 204-220.
• Bernt P. Stigum, 2021. "Consumer Choice under Certainty and Uncertainty in Applied Econometrics," EERI Research Paper Series EERI RP 2021/08, Economics and Econometrics Research Institute (EERI), Brussels.
• Martin Vallejos, 2021. "La Dinámica de los Precios del Petróleo y Tipos de Cambio en Latinoamérica," Cuadernos de Investigación Económica Boliviana, Ministerio de Economía y Finanzas Públicas de Bolivia, vol. 4(1), pages 67-110, Junio.
• Taniya Ghosh & Sakshi Agarwal & William A. Barnett & Bruno S. Sergi, 2021. "Do Machine Learning Models Hold the Key to Better Money Demand Forecasting?," International Symposia in Economic Theory and Econometrics, in: Environmental, Social, and Governance Perspectives on Economic Development in Asia, volume 29, pages 21-36, Emerald Group Publishing Limited.
• Taniya Ghosh & Sakshi Agarwal, 2021. "Do Machine Learning Models Hold the Key to Better Money Demand Forecasting?," International Symposia in Economic Theory and Econometrics, in: Environmental, Social, and Governance Perspectives on Economic Development in Asia, volume 29, pages 21-36, Emerald Group Publishing Limited.
• Marcin Hernes & Adrianna Kozierkiewicz & Marcin Maleszka & Artur Rot & Agata Kozina & Karolina Matenczuk & Jakub Janus & Ewelina Wrobel, 2021. "Deep Learning for Repayment Prediction in Leasing Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 1134-1148.
• Bartosz Przysucha & Tomasz Rymarczyk & Dariusz Wojcik & Marcin Kowalski & Ryszard Bialek, 2021. "Management of Early Failure Detection of Production Process: The Case of the Clutch Shaft Alignment using LSTM Deep Learning Algorithm," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 189-197.
• Grzegorz Kłosowski & Monika Kulisz & Jerzy Lipski & Michal Maj & Ryszard Bialek, 2021. "The Use of Transfer Learning with Very Deep Convolutional Neural Network in Quality Management," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 253-263.
• Pawel Rymarczyk & Piotr Golabek & Sylwia Skrzypek - Ahmed & Magdalena Rzemieniak, 2021. "Profiling and Segmenting Clients with the Use of Machine Learning Algorithms," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 513-522.
• Damian Pliszczuk & Piotr Lesiak & Krzysztof Zuk & Tomasz Cieplak, 2021. "Forecasting Sales in the Supply Chain Based on the LSTM Network: The Case of Furniture Industry," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 627-636.
• Natalia Nehrebecka, 2021. "Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 719-736.
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• Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  • Korobilis, Dimitris & Shimizu, Kenichi, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper 111631, University Library of Munich, Germany.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
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  • Pierre-Philippe Combes & Laurent Gobillon & yanos Zylberberg, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & yanos Zylberberg, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Post-Print halshs-03673240, HAL.
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & yanos Zylberberg, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," SciencePo Working papers halshs-03673240, HAL.
• Rajka Hrbić & Tomislav Grebenar, 2021. "Assessment of Readiness of Croatian Companies to Introduce I4.0 Technologies," Working Papers 63, The Croatian National Bank, Croatia.
• Ai Nur Bayinah & Muhammad Said & Munzier Suparta, 2021. "Social-Commercial Interconnection: Lessons From Bank Muamalat Indonesia & Baitulmaal Muamalat Affiliation," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, vol. 7(2), pages 341-368, May.
• Ai Nur Bayinah & Muhammad Said & Munzier Suparta, 2021. "Social-Commercial Interconnection: Lessons From Bank Muamalat Indonesia & Baitulmaal Muamalat Affiliation," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 7(2), pages 341-368.
• Rogelio Ladrón de Guevara Cortés & Salvador Torra Porras & Enric Monte Moreno, 2021. "Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(TNEA), pages 1-25, Septiembr.
• Judith Jazmin Castro Pérez & José Eduardo Medina Reyes, 2021. "Fuzzy Portfolio Selection with Sugeno Type Fuzzy Neural Network: Investing in the Mexican Stock Market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(TNEA), pages 1-25, Septiembr.
• Ntigkaris Alexandros, 2021. "Cryptocurrency analysis: Benefits, dangers and price prediction using neural networks," Romanian Journal of Economics, Institute of National Economy, vol. 52(1(61)), pages 05-17, June.
• Margherita Comola & Carla Inguaggiato & Mariapia Mendola, 2021. "Learning about Farming: Innovation and Social Networks in a Resettled Community in Brazil," Development Working Papers 468, Centro Studi Luca d'Agliano, University of Milano.
  • Comola, Margherita & Inguaggiato, Carla & Mendola, Mariapia, 2021. "Learning about Farming: Innovation and Social Networks in a Resettled Community in Brazil," IZA Discussion Papers 14092, Institute of Labor Economics (IZA).
• Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
  • Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," IZA Discussion Papers 14259, Institute of Labor Economics (IZA).
  • Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series 2104, University of St. Gallen, School of Economics and Political Science.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  • Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
  • Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
• Gimenez-Nadal, José Ignacio & Gracia-Lazaro, Carlos & Molina, José Alberto, 2021. "Bike-Sharing: Network Efficiency and Demand Profiles," IZA Discussion Papers 14818, Institute of Labor Economics (IZA).
• Jing Yuan & Yajing Dong & Weijie Zhai & Zongwu Cai, 2021. "Economic Policy Uncertainty: Cross-Country Linkages and Spillover Effects on Economic Development in Some Belt and Road Countries," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202110, University of Kansas, Department of Economics, revised Nov 2021.
• Raushan Kumar, 2021. "Predicting Wheat Futures Prices in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 121-140, March.
• Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.
• Athanasios Tsadiras & Maria Pempetzoglou & Iosif Viktoratos, 2021. "Making Predictions of Global Warming Impacts Using a Semantic Web Tool that Simulates Fuzzy Cognitive Maps," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 715-745, October.
• Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
• Timothy Holt & Mitsuru Igami & Simon Scheidegger, 2021. "Detecting Edgeworth Cycles," Cahiers de Recherches Economiques du Département d'économie 21.16, Université de Lausanne, Faculté des HEC, Département d’économie.
• Luis Eduardo Peñafiel Chang, 2021. "Economic, Political and Health Panorama of Latin America and the Caribbean at the Beginning of The COVID-19 Pandemic," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 95, pages 11-44, July-Dece.
• Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Zahner, Johannes & Baumgärtner, Martin, 2022. "Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks," VfS Annual Conference 2022 (Basel): Big Data in Economics 264019, Verein für Socialpolitik / German Economic Association.
• Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
• Sylwia Radomska, 2021. "Prognozowanie indeksu WIG20 za pomocą sieci neuronowych NARX i metody SVM," Bank i Kredyt, Narodowy Bank Polski, vol. 52(5), pages 457-472.
• Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning [The role of boards of directors in corporate governance: A conceptual framework and survey]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3226-3264.
  • Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3226-3264, National Bureau of Economic Research, Inc.
  • Erel, Isil & Stern, Lea Henny & Tan, Chenhao & Weisbach, Michael S., 2018. "Selecting Directors Using Machine Learning," Working Paper Series 2018-05, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
  • Isil Erel & Léa H. Stern & Chenhao Tan & Michael S. Weisbach, 2018. "Selecting Directors Using Machine Learning," NBER Working Papers 24435, National Bureau of Economic Research, Inc.
• Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021. "Measuring Corporate Culture Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3265-3315, National Bureau of Economic Research, Inc.
• Younes Boujelbene & Nedra Baklouti, 2021. "The Causal Probabilistic Relationship Between Economic Development and Democracy: Evidence from Bayesian Networks Analysis," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 27-42, March.
• Julie Lassébie & Luca Marcolin & Marieke Vandeweyer & Benjamin Vignal, 2021. "Speaking the same language: A machine learning approach to classify skills in Burning Glass Technologies data," OECD Social, Employment and Migration Working Papers 263, OECD Publishing.
• Sergiu Mihai Haţegan, 2021. "A Mapping Of The Literature On Econophysics," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 92-100, July.
• Apaar Sadhwani & Kay Giesecke & Justin Sirignano, 2021. "Deep Learning for Mortgage Risk [The Subprime Virus]," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 313-368.
• Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
  • Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
• Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
• Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3226-3264, National Bureau of Economic Research, Inc.
  • Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning [The role of boards of directors in corporate governance: A conceptual framework and survey]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3226-3264.
  • Erel, Isil & Stern, Lea Henny & Tan, Chenhao & Weisbach, Michael S., 2018. "Selecting Directors Using Machine Learning," Working Paper Series 2018-05, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
  • Isil Erel & Léa H. Stern & Chenhao Tan & Michael S. Weisbach, 2018. "Selecting Directors Using Machine Learning," NBER Working Papers 24435, National Bureau of Economic Research, Inc.
• Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021. "Measuring Corporate Culture Using Machine Learning [Machine learning methods that economists should know about]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3265-3315.
• Constantin Ilie & Andreea-Daniela Moraru, 2021. "Management Based on Data Analysis. Part Two. Artificial Intelligence Data Modeling," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 743-748, December.
• Andrea Kolková & Aleksandr Kljuènikov, 2021. "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1063-1094, December.
• Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
  • Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2021. "Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach," MPRA Paper 108272, University Library of Munich, Germany.
• Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.
• Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
  • Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Evaluation of technology clubs by clustering: A cautionary note," MPRA Paper 109138, University Library of Munich, Germany.
• Antonio Rodríguez Andrés & Abraham Otero & Voxi Heinrich Amavilah, 2022. "Knowledge economy classification in African countries: A model-based clustering approach," Information Technology for Development, Taylor & Francis Journals, vol. 28(2), pages 372-396, April.
  • Amavilah, Voxi Heinrich & Otero, Abraham & Andres, Antonio Rodriguez, 2021. "Knowledge Economy Classification in African Countries: A Model-Based Clustering Approach," MPRA Paper 109188, University Library of Munich, Germany.
• Kitova, Olga & Savinova, Victoria, 2021. "Development of an Ensemble of Models for Predicting Socio-Economic Indicators of the Russian Federation using IRT-Theory and Bagging Methods," MPRA Paper 110824, University Library of Munich, Germany.
• Medel-Ramírez, Carlos & Medel-López, Hilario & Lara-Mérida, Jennifer, 2021. "(SARS-CoV-2) COVID 19: Vigilancia genómica y evaluación del impacto en la población hablante de lengua indígena en México [(SARS-CoV-2) COVID 19: Genomic surveillance and impact assessment on the i," MPRA Paper 110858, University Library of Munich, Germany.
• Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
  • Korobilis, Dimitris & Shimizu, Kenichi, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper 111631, University Library of Munich, Germany.
  • Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
  • Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Papers 2112.11751, arXiv.org.
• Aksoy, Baris, 2021. "Predicting Direction of Stock Price Using Machine Learning Techniques: The Sample of Borsa Istanbul (Pay Senedi Fiyat Yönünün Makine Öğrenmesi Yöntemleri ile Tahmini: Borsa İstanbul Örneği)," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 12(1), pages 89-110, January.
• Dimitriadou, Athanasia & Agrapetidou, Anna & Gogas, Periklis & Papadimitriou, Theophilos, 2021. "Credit Rating Agencies: Evolution or Extinction?," DUTH Research Papers in Economics 9-2021, Democritus University of Thrace, Department of Economics.
• de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
• Holland, Christopher & Kavuri, Anil, 2021. "Artificial Intelligence and Digital Transformation of Insurance Markets," Journal of Financial Transformation, Capco Institute, vol. 54, pages 104-115.
• Daehyeon PARK & Doojin RYU, 2021. "Forecasting Stock Market Dynamics using Bidirectional Long Short-Term Memory," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 22-34, June.
• López Malpica, Gustavo & Hoyos Reyes, Luis Fernando & Rodríguez Benavides, Domingo & Mora Gutiérrez, Roman Anselmo, 2021. "Técnicas metaheurísticas para pronosticar el tipo de cambio del dólar de Estados Unidos con respecto al peso mexicano / Adaptation of Metaheuristic Techniques to Forecast the USD Dollar-MXN Peso Excha," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 11(2), pages 147-172, julio-dic.
• Paolo Angelis & Roberto Marchis & Mario Marino & Antonio Luciano Martire & Immacolata Oliva, 2021. "Betting on bitcoin: a profitable trading between directional and shielding strategies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 883-903, December.
• Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2021. "Accuracy of deep learning in calibrating HJM forward curves," Digital Finance, Springer, vol. 3(3), pages 209-248, December.
• Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
• José Américo Pereira Antunes, 2021. "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
• Ali Habibnia & Esfandiar Maasoumi, 2021. "Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 363-381, December.
  • Ali Habibnia & Esfandiar Maasoumi, 2019. "Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Papers 1904.11145, arXiv.org.
• Harald Hruschka, 2021. "Comparing unsupervised probabilistic machine learning methods for market basket analysis," Review of Managerial Science, Springer, vol. 15(2), pages 497-527, February.
• Rosina O. Weber & Kedma B. Duarte, 2021. "Data-driven artificial intelligence to automate researcher assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3265-3281, April.
• Judith J. Castro Pérez & José E. Medina Reyes & Agustín I. Cabrera Llanos, 2021. "Forecasting the Effects of the COVID-19 Crisis on Economic Growth and the Microfinance Sector in Latin America: An Approach with Fuzzy Neural Networks," Springer Books, in: Griselda Dávila-Aragón & Salvador Rivas-Aceves (ed.), The Future of Companies in the Face of a New Reality, pages 79-107, Springer.
• Yu-Min Lian & Chia-Hsuan Li & Yi-Hsuan Wei, 2021. "Machine Learning and Time Series Models for VNQ Market Predictions," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 11(5), pages 1-2.
• Ieva Meidutė-Kavaliauskienė & Gitana Dudzevičiūtė & Agnė Šimelytė & Nijolė Maknickienė, 2021. "Sustainability and regional security in the context of Lithuania," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(3), pages 248-266, March.
• Simona Hašková & Petr Šuleř & Tomáš Krulický, 2021. "Advantages of fuzzy approach compared to probabilistic approach in project evaluation," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(2), pages 446-456, December.
• Hanjo Odendaal, 2021. "A machine learning approach to domain specific dictionary generation. An economic time series framework," Working Papers 06/2021, Stellenbosch University, Department of Economics.
• Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
  • Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Evaluation of technology clubs by clustering: A cautionary note," MPRA Paper 109138, University Library of Munich, Germany.
• Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
  • Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
• Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
  • Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series 2104, University of St. Gallen, School of Economics and Political Science.
  • Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," IZA Discussion Papers 14259, Institute of Labor Economics (IZA).
• Wei-Bin Zhang, 2021. "Economic Growth And Human Networking," Business & Management Compass, University of Economics Varna, issue 1, pages 5-25.
• Perez Katarzyna & Szczyt Małgorzata, 2021. "Classification of Open-End Investment Funds Using Artificial Neural Networks. The Case of Polish Equity Funds," Central European Economic Journal, Sciendo, vol. 8(55), pages 269-284, January.
• Dzik-Walczak Aneta & Odziemczyk Maciej, 2021. "Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland," Central European Economic Journal, Sciendo, vol. 8(55), pages 352-377, January.
• Chi Yeong Nain & Chi Orson, 2021. "Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal Arima and Multilayer Perceptron Neural Network," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 25(3), pages 21-41, September.
• Młodzianowski Piotr & Valencia Hernandez Jose Aldo, 2021. "Evaluation of Cluster Management Quality Based on Consumer Opinion Sentiment Analysis," Foundations of Management, Sciendo, vol. 13(1), pages 219-228, January.
• Gosztonyi Márton, 2021. "Comparative Research of Central and Eastern European Startup Researches Based on Artificial Intelligence-Based Natural Language Processing," Journal of Intercultural Management, Sciendo, vol. 13(4), pages 4-33, December.
• Dawid Siwicki, 2021. "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers 2021-05, Faculty of Economic Sciences, University of Warsaw.
• Piotr Borowski & Marcin Chlebus, 2021. "Machine learning in the prediction of flat horse racing results in Poland," Working Papers 2021-13, Faculty of Economic Sciences, University of Warsaw.
• Kamil Korzeń & Robert Ślepaczuk, 2021. "Enhanced Index Replication Based on Smart Beta and Tail-Risk Asset Allocation," Working Papers 2021-18, Faculty of Economic Sciences, University of Warsaw.
• Jan Grudniewicz & Robert Ślepaczuk, 2021. "Application of machine learning in quantitative investment strategies on global stock markets," Working Papers 2021-23, Faculty of Economic Sciences, University of Warsaw.
• Nguyen Vo & Robert Ślepaczuk, 2021. "Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index," Working Papers 2021-25, Faculty of Economic Sciences, University of Warsaw.
• Sergio Castellano Gómez & Robert Ślepaczuk, 2021. "Robust optimisation in algorithmic investment strategies," Working Papers 2021-27, Faculty of Economic Sciences, University of Warsaw.
• Garg, Karan, 2021. "Machines and Markets : Assessing the Impact of Algorithmic Trading on Financial Market Efficiency," Warwick-Monash Economics Student Papers 11, Warwick Monash Economics Student Papers.
• Hinterlang, Natascha & Tänzer, Alina, 2021. "Optimal monetary policy using reinforcement learning," Discussion Papers 51/2021, Deutsche Bundesbank.
• Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Jiawen Luo & Tony Klein & Thomas Walther & Qiang Ji, 2024. "Forecasting realized volatility of crude oil futures prices based on machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1422-1446, August.
  • 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 2021/04, Queen's University Belfast, Queen's Business School.
• Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.
  • 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 322, Leibniz Institute for Financial Research SAFE.
• 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 21-062, ZEW - Leibniz Centre for European Economic Research.

2020

• Bo Cowgill & Fabrizio Dell'Acqua & Sandra Matz, 2020. "The Managerial Effects of Algorithmic Fairness Activism," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 85-90, May.
  • Bo Cowgill & Fabrizio Dell'Acqua & Sandra Matz, 2020. "The Managerial Effects of Algorithmic Fairness Activism," Papers 2012.02393, arXiv.org.
• Ashesh Rambachan & Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan, 2020. "An Economic Perspective on Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 91-95, May.
• Bo Cowgill & Megan T. Stevenson, 2020. "Algorithmic Social Engineering," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 96-100, May.
• 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, vol. 58(4), pages 997-1044, December.
  • Martha Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2017. "How Well Do Automated Linking Methods Perform? Lessons from U.S. Historical Data," NBER Working Papers 24019, National Bureau of Economic Research, Inc.
• 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.
• Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
  • Wallimann, Hannes & Imhof, David & Huber, Martin, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers 513, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
  • Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
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• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
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• Niklas, Britta & Rinke, Wolfram, 2020. "Pricing Models for German Wine: Hedonic Regression vs. Machine Learning," Journal of Wine Economics, Cambridge University Press, vol. 15(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, vol. 28(1 Year 20), pages 39-62.
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• Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020. "Nowcasting business cycle turning points with stock networks and machine learning," Working Paper Series 2494, European Central Bank.
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• Luis Gerardo Gage & Raúl Morales-Resendiz & John Arroyo & Jeniffer Rubio & Paolo Barucca, 2022. "Classifying payment patterns with artificial neural networks: an autoencoder approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
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• Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
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• 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 pje208, Job Market Papers.
• 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 202017, University of Kansas, Department of Economics, revised Oct 2020.
• Zongwu Cai & Xiyuan Liu, 2020. "A Nonparametric Dynamic Network via Multivariate Quantile Autoregressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202209, University of Kansas, Department of Economics, 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 202406, University of Kansas, Department of Economics, revised Jan 2024.
• Jermain C. Kaminski & Christian Hopp, 2020. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 55(3), pages 627-649, October.
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• 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 26825, National Bureau of Economic Research, Inc.
• Munisamy Gopinath & Feras A. Batarseh & Jayson Beckman, 2020. "Machine Learning in Gravity Models: An Application to Agricultural Trade," NBER Working Papers 27151, National Bureau of Economic Research, Inc.
• Marshall Burke & Anne Driscoll & David Lobell & Stefano Ermon, 2020. "Using Satellite Imagery to Understand and Promote Sustainable Development," NBER Working Papers 27879, National Bureau of Economic Research, Inc.
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• Nicolas Woloszko, 2020. "Adaptive Trees: a new approach to economic forecasting," OECD Economics Department Working Papers 1593, OECD Publishing.
• Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
• Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
• 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, vol. 11(2), pages 325-346, June.
• Grilli, Luca & Santoro, Domenico, 2020. "How Boltzmann Entropy Improves Prediction with LSTM," MPRA Paper 100578, University Library of Munich, Germany.
• Gomez-Ruano, Gerardo, 2020. "Data Science: A Primer for Economists," MPRA Paper 102928, University Library of Munich, Germany.
• Diunugala, Hemantha Premakumara & Mombeuil, Claudel, 2020. "Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods," MPRA Paper 103779, University Library of Munich, Germany.
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• Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, vol. 2(1), pages 69-96, September.
  • Dautel, Alexander J. & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2019. "Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks," IRTG 1792 Discussion Papers 2019-008, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  • 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 2020-006, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• 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, vol. 59(3), pages 1285-1305, September.
• 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, vol. 90(7), pages 963-974, August.
  • 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 20/15, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
• 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, in: Vicky Katsoni & Thanasis Spyriadis (ed.), Cultural and Tourism Innovation in the Digital Era, pages 201-207, Springer.
• Nataliya Matveeva & Anuška Ferligoj, 2020. "Scientific collaboration in Russian universities before and after the excellence initiative Project 5-100," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2383-2407, September.
• 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, vol. 8(1), pages 261-275, September.
• Islam, Raisul & Volkov, Vladimir, 2020. "Contagion or interdependence? Comparing signed and unsigned spillovers," Working Papers 2020-05, University of Tasmania, Tasmanian School of Business and Economics.
• Islam, Raisul & Volkov, Vladimir, 2020. "Calm before the storm: an early warning approach before and during the COVID-19 crisis," Working Papers 2020-09, University of Tasmania, Tasmanian School of Business and Economics.
• Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
  • Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020. "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working Papers 202056, University of Pretoria, Department of Economics.
  • Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020. "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working papers 2020-10, University of Connecticut, Department of Economics.
• 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.
• Jerić Silvija Vlah, 2020. "Comparing classification algorithms for prediction on CROBEX data," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(2), pages 4-11, December.
• Jerić Silvija Vlah, 2020. "Comparing classification algorithms for prediction on CROBEX data," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(2), pages 4-11, December.
• 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, vol. 24(3), pages 37-50, September.
• 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, vol. 6(1), pages 46-81, March.
  • Michał Latoszek & Robert Ślepaczuk, 2019. "Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor," Working Papers 2019-14, Faculty of Economic Sciences, University of Warsaw.
• 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, vol. 56(3), pages 209-217, September.
• Lee Changro & Park Keith Key-Ho, 2020. "Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach," Real Estate Management and Valuation, Sciendo, vol. 28(4), pages 15-23, December.
• Oleh Bilyk & Paweł Sakowski & Robert Ślepaczuk, 2020. "Investing in VIX futures based on rolling GARCH models forecasts," Working Papers 2020-10, Faculty of Economic Sciences, University of Warsaw.
• Maciej Wysocki & Robert Ślepaczuk, 2020. "Artificial Neural Networks Performance in WIG20 Index Options Pricing," Working Papers 2020-19, Faculty of Economic Sciences, University of Warsaw.
• Mateusz Kijewski & Robert Ślepaczuk, 2020. "Predicting prices of S&P500 index using classical methods and recurrent neural networks," Working Papers 2020-27, Faculty of Economic Sciences, University of Warsaw.
• Karol Kielak & Robert Ślepaczuk, 2020. "Value-at-risk — the comparison of state-of-the-art models on various assets," Working Papers 2020-28, Faculty of Economic Sciences, University of Warsaw.
• Bartłomiej Bollin & Robert Ślepaczuk, 2020. "Variance Gamma Model in Hedging Vanilla and Exotic Options," Working Papers 2020-31, Faculty of Economic Sciences, University of Warsaw.
• Robert Ślepaczuk & Igor Wabik, 2020. "The impact of the results of football matches on the stock prices of soccer clubs," Working Papers 2020-35, Faculty of Economic Sciences, University of Warsaw.
• Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
  • Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
• Hinterlang, Natascha, 2020. "Predicting monetary policy using artificial neural networks," Discussion Papers 44/2020, Deutsche Bundesbank.
• Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
• 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, vol. 6(3), pages 3-13.
• Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, vol. 2(1), pages 69-96, September.
  • Dautel, Alexander J. & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2019. "Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks," IRTG 1792 Discussion Papers 2019-008, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  • 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 2020-006, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers 2020-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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.
• Jens Ludwig & Sendhil Mullainathan & Jann Spiess, 2019. "Augmenting Pre-Analysis Plans with Machine Learning," AEA Papers and Proceedings, American Economic Association, vol. 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, vol. 1(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, vol. 1(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)., vol. 19(1), pages 13-34, June.
• 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.
• Joseph, Andreas, 2019. "Parametric inference with universal function approximators," Bank of England working papers 784, Bank of England, revised 22 Jul 2020.
  • Andreas Joseph, 2019. "Parametric inference with universal function approximators," Papers 1903.04209, arXiv.org, revised Oct 2020.
• Ali Habibnia & Esfandiar Maasoumi, 2021. "Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 363-381, December.
  • Ali Habibnia & Esfandiar Maasoumi, 2019. "Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Papers 1904.11145, arXiv.org.
• Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
  • Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Papers 1908.11498, arXiv.org, revised Oct 2019.
  • Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
  • Albanesi, Stefania & Vamossy, Domonkos, 2019. "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers 13914, C.E.P.R. Discussion Papers.
• Fredy Cepeda-Lopez & Fredy Gamboa-Estrada & Carlos Leon-Rincón & Hernán Rincon-Castro, 2022. "Colombian Liberalization and Integration into World Trade Markets: Much Ado about Nothing," Revista de Economía del Rosario, Universidad del Rosario, vol. 25(2), pages 1-44, December.
  • 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 1065, Banco de la Republica de Colombia.
• 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 wps43, Bank of Russia.
• Andreas Joseph, 2019. "Parametric inference with universal function approximators," Papers 1903.04209, arXiv.org, revised Oct 2020.
  • Joseph, Andreas, 2019. "Parametric inference with universal function approximators," Bank of England working papers 784, Bank of England, revised 22 Jul 2020.
• Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
• 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, vol. 8(1), pages 163-182.
• Chuku, Chuku & Simpasa, Anthony & Oduor, Jacob, 2019. "Intelligent forecasting of economic growth for developing economies," International Economics, Elsevier, vol. 159(C), pages 74-93.
  • 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 18302, Universidad Nacional de Colombia Sede Medellín.
• Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
  • Albanesi, Stefania & Vamossy, Domonkos, 2019. "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers 13914, C.E.P.R. Discussion Papers.
  • Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Papers 1908.11498, arXiv.org, revised Oct 2019.
  • Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
  • Jesus Fernandez-Villaverde & Samuel Hurtado & Galo Nuno, 2019. "Financial Frictions and the Wealth Distribution," PIER Working Paper Archive 19-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Fernández-Villaverde, Jesús & Hurtado, Samuel & Nuño, Galo, 2019. "Financial Frictions and the Wealth Distribution," CEPR Discussion Papers 14002, C.E.P.R. Discussion Papers.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial frictions and the wealth distribution," Working Papers 2013, Banco de España.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2019. "Financial Frictions and the Wealth Distribution," NBER Working Papers 26302, National Bureau of Economic Research, Inc.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial Frictions and the Wealth Distribution," CESifo Working Paper Series 8482, CESifo.
• 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 451, Centro Studi Luca d'Agliano, University of Milano.
• 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, vol. 42(120), pages 207-218, Diciembre.
• Jawwad Noor, 2019. "Intuitive Beliefs," Cowles Foundation Discussion Papers 2216, Cowles Foundation for Research in Economics, Yale University.
• Richard Heuver & Ron TriepelsTriepels, 2019. "Liquidity stress detection in the European banking sector," DNB Working Papers 642, Netherlands Central Bank, Research Department.
• Roncoroni, Alan & Battiston, Stefano & D’Errico, Marco & Hałaj, Grzegorz & Kok, Christoffer, 2021. "Interconnected banks and systemically important exposures," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
  • Alan Roncoroni & Stefano Battiston & Marco D’Errico & Grzegorz Halaj & Christoffer Kok, 2019. "Interconnected Banks and Systemically Important Exposures," Staff Working Papers 19-44, Bank of Canada.
  • Roncoroni, Alan & Battiston, Stefano & D'Errico, Marco & Hałaj, Grzegorz & Kok, Christoffer, 2019. "Interconnected banks and systemically important exposures," Working Paper Series 2331, European Central Bank.
• 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, vol. 9(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, vol. 64(C), pages 159-171.
• Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2019. "Tail event driven networks of SIFIs," Journal of Econometrics, Elsevier, vol. 208(1), pages 282-298.
• Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
• 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, vol. 80(C), pages 937-949.
• Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
• Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
  • Huber, Martin & Imhof, David, 2018. "Machine Learning with Screens for Detecting Bid-Rigging Cartels," FSES Working Papers 494, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
• 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.
  • Chuku, Chuku & Simpasa, Anthony & Oduor, Jacob, 2019. "Intelligent forecasting of economic growth for developing economies," International Economics, Elsevier, vol. 159(C), pages 74-93.
• Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
  • Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
• Arifovic, Jasmina & Yıldızoğlu, Murat, 2019. "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 191-208.
  • Jasmina ARIFOVIC & Murat YILDIZOGLU, 2014. "Learning the Ramsey outcome in a Kydland & Prescott economy," Cahiers du GREThA (2007-2019) 2014-06, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
  • Jasmina Arifovic & Murat Yildizoglu, 2019. "Learning the Ramsey Outcome in a Kydland & Prescott Economy," Post-Print hal-03428629, HAL.
• Liu, Yancai & Cai, Rui & Duan, Jinqiao, 2019. "Lévy noise induced escape in the Morris–Lecar model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C), pages 1-1.
• Adcock, Robert & Gradojevic, Nikola, 2019. "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
• Sommervoll, Åvald & Sommervoll, Dag Einar, 2019. "Learning from man or machine: Spatial fixed effects in urban econometrics," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 239-252.
• 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, vol. 47(C), pages 304-310.
  • Aviral Kumar Tiwari & Rangan Gupta & Stelios Bekiros, 2016. "Chaos in G7 Stock Markets using Over One Century of Data: A Note," Working Papers 201678, University of Pretoria, Department of Economics.
• 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, vol. 49(C), pages 315-321.
• 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, vol. 37(1), pages 1-17, September.
• 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, vol. 69(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.
• Charlie Joyez, 2019. "Alignment of Multinational Firms along Global Value Chains: A Network-based Perspective," GREDEG Working Papers 2019-05, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
• Miriam Steurer & Robert Hill, 2019. "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers 2019-02, University of Graz, Department of Economics.
• 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," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 433-446, June.
  • 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-02155402, HAL.
• Arifovic, Jasmina & Yıldızoğlu, Murat, 2019. "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 191-208.
  • Jasmina ARIFOVIC & Murat YILDIZOGLU, 2014. "Learning the Ramsey outcome in a Kydland & Prescott economy," Cahiers du GREThA (2007-2019) 2014-06, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
  • Jasmina Arifovic & Murat Yildizoglu, 2019. "Learning the Ramsey Outcome in a Kydland & Prescott Economy," Post-Print hal-03428629, HAL.
• Steffen Q. Mueller & Patrick Ring & Maria Schmidt, 2019. "Forecasting economic decisions under risk: The predictive importance of choice-process data," Working Papers 066, Chair for Economic Policy, University of Hamburg.
  • Steffen Q. Mueller & Patrick Ring & Maria Fischer, 2020. "Excited and aroused: The predictive importance of simple choice process metrics," Working Papers 067, Chair for Economic Policy, University of Hamburg.
• Grodecka, Anna & Hull, Isaiah, 2019. "The Impact of Local Taxes and Public Services on Property Values," Working Paper Series 374, Sveriges Riksbank (Central Bank of Sweden).
• 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, vol. 5(3), pages 1-16.
• 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, vol. 5(3), pages 475-490, November.
• Shahid Anjum & Naveeda Qaseem, 2019. "Big Data Algorithms And Prediction: Bingos And Risky Zones In Sharia Stock Market Index," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 5(3), pages 1-16.
• Şahap KAVCIOĞLU, 2019. "Kurumsal Kredi Skorlamasında Klasik Yöntemlerle Yapay Sinir Ağı Karşılaştırması," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 69(2), pages 207-246, December.
• 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 19-463, KOF Swiss Economic Institute, ETH Zurich.
  • Boriss Siliverstovs & Daniel Wochner, 2020. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," Working Papers 2020/02, Latvijas Banka.
• Nazaraghaei, Mehdi & Ghiasi, Hosein & Asgharkhah Chafi, Mohammad, 2019. "Classification of Customer’s Credit Risk Using Ensemble learning (Case study: Sepah Bank) (in Persian)," Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی), Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 12(39), pages 166-129, May.
• Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
  • Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
  • Albanesi, Stefania & Vamossy, Domonkos, 2019. "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers 13914, C.E.P.R. Discussion Papers.
  • Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Papers 1908.11498, arXiv.org, revised Oct 2019.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
  • Jesus Fernandez-Villaverde & Samuel Hurtado & Galo Nuno, 2019. "Financial Frictions and the Wealth Distribution," PIER Working Paper Archive 19-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial frictions and the wealth distribution," Working Papers 2013, Banco de España.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2019. "Financial Frictions and the Wealth Distribution," NBER Working Papers 26302, National Bureau of Economic Research, Inc.
  • Fernández-Villaverde, Jesús & Hurtado, Samuel & Nuño, Galo, 2019. "Financial Frictions and the Wealth Distribution," CEPR Discussion Papers 14002, C.E.P.R. Discussion Papers.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial Frictions and the Wealth Distribution," CESifo Working Paper Series 8482, CESifo.
• Kireyev, A., 2019. "A Network Model of Multilateral Equilibrium Exchange Rates," Journal of the New Economic Association, New Economic Association, vol. 41(1), pages 12-33.
  • Mr. Alexei P Kireyev & Andrei Leonidov, 2016. "A Network Model of Multilaterally Equilibrium Exchange Rates," IMF Working Papers 2016/130, International Monetary Fund.
• Arnaud Pincet & Shu Okabe & Martin Pawelczyk, 2019. "Linking Aid to the Sustainable Development Goals – a machine learning approach," OECD Development Co-operation Working Papers 52, OECD Publishing.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
  • Fernández-Villaverde, Jesús & Hurtado, Samuel & Nuño, Galo, 2019. "Financial Frictions and the Wealth Distribution," CEPR Discussion Papers 14002, C.E.P.R. Discussion Papers.
  • Jesus Fernandez-Villaverde & Samuel Hurtado & Galo Nuno, 2019. "Financial Frictions and the Wealth Distribution," PIER Working Paper Archive 19-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial frictions and the wealth distribution," Working Papers 2013, Banco de España.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2019. "Financial Frictions and the Wealth Distribution," NBER Working Papers 26302, National Bureau of Economic Research, Inc.
  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial Frictions and the Wealth Distribution," CESifo Working Paper Series 8482, CESifo.
• 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, vol. 14(4), pages 739-761, December.
• Fajar, Muhammad, 2019. "An application of hybrid forecasting singular spectrum analysis – extreme learning machine method in foreign tourists forecasting," MPRA Paper 105044, University Library of Munich, Germany, revised 31 Oct 2019.
• Hossain, Md. Mobarak & Chowdhury, Md Niaz Murshed, 2019. "Econometric Ways to Estimate the Age and Price of Abalone," MPRA Paper 91210, University Library of Munich, Germany.
• Wayne Taylor & Brett Hollenbeck, 2021. "Leveraging loyalty programs using competitor based targeting," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 417-455, December.
  • Hollenbeck, Brett & Taylor, Wayne, 2019. "Leveraging Loyalty Programs Using Competitor Based Targeting," MPRA Paper 92900, University Library of Munich, Germany.
• Brummelhuis, Raymond & Luo, Zhongmin, 2019. "Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques," MPRA Paper 94779, University Library of Munich, Germany.
• Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
  • Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
• Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
  • Andrea Bucci, 0. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
  • Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
• Zolnikov, Pavel & Zubov, Maxim & Nikitinsky, Nikita & Makarov, Ilya, 2019. "Efficient Algorithms for Constructing Multiplex Networks Embedding," MPRA Paper 97310, University Library of Munich, Germany, revised 23 Sep 2019.
• Benkovich, Nikita & Dedenok, Roman & Golubev, Dmitry, 2019. "Deep Quarantine for Suspicious Mail," MPRA Paper 97311, University Library of Munich, Germany, revised 23 Sep 2019.
• Kadyrov, Timur & Ignatov, Dmitry I., 2019. "Attribution of Customers’ Actions Based on Machine Learning Approach," MPRA Paper 97312, University Library of Munich, Germany, revised 23 Sep 2019.
• Osipov, Vasiliy & Zhukova, Nataly & Miloserdov, Dmitriy, 2019. "Neural Network Associative Forecasting of Demand for Goods," MPRA Paper 97314, University Library of Munich, Germany, revised 23 Sep 2019.
• Sumi, P. Sobana & Delhibabu, Radhakrishnan, 2019. "Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network," MPRA Paper 97315, University Library of Munich, Germany, revised 23 Sep 2019.
• Milan Fičura, 2019. "Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks," FFA Working Papers 1.001, Prague University of Economics and Business, revised 24 Nov 2019.
• Thomas R. Cook & Aaron Smalter Hall, 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper RWP 17-11, Federal Reserve Bank of Kansas City.
  • Thomas Cook, 2019. "Macroeconomic Indicator Forecasting with Deep Neural Networks," 2019 Meeting Papers 402, Society for Economic Dynamics.
• Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
  • Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras & Rangan Gupta, 2015. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," Working Papers 201548, University of Pretoria, Department of Economics.
  • Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
• Gonzales, Rolando & Wareham, Jonathan, 2019. "Analysing the impact of a business intelligence system and new conceptualizations of system use," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 24(48), pages 345-368.
• Malhotra, Yogesh, 2019. "Ai Augmentation For Large-Scale Global Systemic And Cyber Risk Management Projects: Model Risk Management For Minimizing The Downside Risks Of Ai And Machine Learning," Journal of Financial Transformation, Capco Institute, vol. 49, pages 94-99.
• Gheorghe RUXANDA & Sorin OPINCARIU & Stefan IONESCU, 2019. "Modelling Non-Stationary Financial Time Series with Input Warped Student T-Processes," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 51-61, September.
• Elda Xhumari & Julian Fejzaj, 2019. "Usage of artificial neural networks in data classification," Proceedings of International Academic Conferences 9211565, International Institute of Social and Economic Sciences.
• Fernando Fernandes Neto & Claudio Garcia, Rodrigo de Losso da Silveira Bueno, Pedro Delano Cavalcanti, Alemayehu Solomon Admas, 2019. "Deep Haar Scattering Networks in Unidimensional Pattern Recognition Problems," Working Papers, Department of Economics 2019_16, University of São Paulo (FEA-USP).
• 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," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 433-446, June.
  • 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-02155402, HAL.
• Daria Maltseva & Vladimir Batagelj, 2019. "Social network analysis as a field of invasions: bibliographic approach to study SNA development," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1085-1128, November.
• Ugis Sarma & Ugis Sarma & Girts Karnitis & Janis Zuters & Edvins Karnitis, 2019. "District heating networks: enhancement of the efficiency," Insights into Regional Development, VsI Entrepreneurship and Sustainability Center, vol. 1(3), pages 200-213, September.
• Lisa-Cheree Martin, 2019. "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers 12/2019, Stellenbosch University, Department of Economics.
• Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2020. "Crisis transmission: Visualizing vulnerability," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
  • Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2019. "Crisis transmission: visualizing vulnerability," Working Papers 2019-07, University of Tasmania, Tasmanian School of Business and Economics.
• Lily Shen & Stephen L. Ross, 2019. "Information Value of Property Description: A Machine Learning Approach," Working papers 2019-20, University of Connecticut, Department of Economics, revised Sep 2020.
• Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
• Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
• Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
• Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(52), pages 186-205, January.
  • Maryna Zenkova & Robert Ślepaczuk, 2019. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Working Papers 2019-02, Faculty of Economic Sciences, University of Warsaw.
• 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, vol. 6(1), pages 46-81, March.
  • Michał Latoszek & Robert Ślepaczuk, 2019. "Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor," Working Papers 2019-14, Faculty of Economic Sciences, University of Warsaw.
• Kamil Korzeń & Robert Ślepaczuk, 2019. "Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach," Working Papers 2019-17, Faculty of Economic Sciences, University of Warsaw.
• Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Katarzyna Maciejowska & Rafal Weron, 2019. "Electricity price forecasting," HSC Research Reports HSC/19/01, Hugo Steinhaus Center, Wroclaw University of Technology.
• Guodong Guo & Brad R. Humphreys & Qiangchang Wang & Yang Zhou, 2023. "Attractive or Aggressive? A Face Recognition and Machine Learning Approach for Estimating Returns to Visual Appearance," Journal of Sports Economics, , vol. 24(6), pages 737-758, August.
  • Guodong Guo & Brad R. Humphreys & Mohammad Iqbal Nouyed & Yang Zhou, 2019. "Attractive or Aggressive? A Face Recognition and Machine Learning Approach for Estimating Returns to Visual Appearance," Working Papers 19-01, Department of Economics, West Virginia University.
• Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Bank of Finland Research Discussion Papers 14/2019, Bank of Finland.
• Bilal Zorić, Alisa, 2019. "Predicting Students’ Success Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 58-66, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
• Hinterlang, Natascha, 2019. "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203503, Verein für Socialpolitik / German Economic Association.

2018

• Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
• Andrea Magda NAGY, 2018. "International Scientific Cooperation Networks of Top Universities in the CEE Region," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 45-54, November.
• Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
  • Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," International Association of Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
• Emrah Gulay, 2018. "Comparing Simple Forecasting Methods and Complex Methods: A Frame of Forecasting Competition," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 65(2), pages 159-169, June.
• Aytuğ Onan, 2018. "A Clustering Based Classifier Ensemble Approach to Corporate Bankruptcy Prediction," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 6(2), pages 365-376, December.
• Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," IREA Working Papers 201805, University of Barcelona, Research Institute of Applied Economics, revised Mar 2018.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," AQR Working Papers 201802, University of Barcelona, Regional Quantitative Analysis Group, revised Apr 2018.
• Renáta Myšková & Petr Hájek & Vladimír Olej, 2018. "Predicting Abnormal Stock Return Volatility Using Textual Analysis of News ? A Meta-Learning Approach," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 20(47), pages 185-185, February.
• Vyacheslav Dzhedzhula & Iryna Yepifanova, 2018. "Use Of Apparatus Of Hybrid Neural Networks For Evaluation Of An Intellectual Component Of The Energy-Saving Policy Of The Enterprise," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 4(1).
• Oksana Omelchenko & Oleksandr Dorokhov & Oleg Kolodiziev & Liudmyla Dorokhova, 2018. "Fuzzy Modeling of the Creditworthiness Assessments of Bank’s Potential Borrowers in Ukraine," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 100-125.
• Carlos León & Fabio Ortega, 2018. "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
  • Carlos León & Fabio Ortega, 2018. "Nowcasting economic activity with electronic payments data: A predictive modeling approach," Borradores de Economia 1037, Banco de la Republica de Colombia.
• Natalia Lamberova & Konstantin Sonin, 2018. "Economic transition and the rise of alternative institutions : Political connections in Putin's Russia," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 26(4), pages 615-648, October.
  • Sonin, Konstantin & Lamberova, Natalia, 2018. "Economic Transition and the Rise of Alternative Institutions: Political Connections in Putin's Russia," CEPR Discussion Papers 13177, C.E.P.R. Discussion Papers.
• Kim Ristolainen, 2018. "Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity," Scandinavian Journal of Economics, Wiley Blackwell, vol. 120(1), pages 31-62, January.
• José Alberto Molina & David Iñiguez & Gonzalo Ruiz & Alfonso Tarancón, 2018. "The Nobel Prize in Economics: individual or collective merits?," Boston College Working Papers in Economics 966, Boston College Department of Economics.
• Nobuhiro Abe & Kimiaki Shinozaki, 2018. "Compilation of Experimental Price Indices Using Big Data and Machine Learning:A Comparative Analysis and Validity Verification of Quality Adjustments," Bank of Japan Working Paper Series 18-E-13, Bank of Japan.
• Kunčič Aljaž, 2018. "SDG-Specific Country Groups: Subregional Analysis of the Arab Region," Review of Middle East Economics and Finance, De Gruyter, vol. 14(2), pages 1-22, August.
• Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
  • Johannes Berens & Simon Oster & Kerstin Schneider & Julian Burghoff, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," Schumpeter Discussion Papers sdp18006, Universitätsbibliothek Wuppertal, University Library.
  • Schneider, Kerstin & Berens, Johannes & Oster, Simon & Burghoff, Julian, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181544, Verein für Socialpolitik / German Economic Association.
• Schneider, Kerstin & Berens, Johannes & Oster, Simon & Burghoff, Julian, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181544, Verein für Socialpolitik / German Economic Association.
  • Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
  • Johannes Berens & Simon Oster & Kerstin Schneider & Julian Burghoff, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," Schumpeter Discussion Papers sdp18006, Universitätsbibliothek Wuppertal, University Library.
• Feng Zhou & Zhang Qun & Didier Sornette & Liu Jiang, 2018. "Cascading Logistic Regression Onto Gradient Boosted Decision Trees to Predict Stock Market Changes Using Technical Analysis," Swiss Finance Institute Research Paper Series 18-50, Swiss Finance Institute, revised Aug 2018.
• Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Flavio Scognamiglio, 2018. "Estimation and Updating Methods for Hedonic Valuation," Swiss Finance Institute Research Paper Series 18-76, Swiss Finance Institute.
• Carlos R. Barrera Chaupis, 2018. "Inventory Adjustments to Demand Shocks under Flexible Specifications," Monetaria, Centro de Estudios Monetarios Latinoamericanos, CEMLA, vol. 0(1), pages 149-201, january-j.
• Carlos R. Barrera Chaupis, 2018. "Ajuste de inventarios ante choques de demanda según especificaciones flexibles," Monetaria, Centro de Estudios Monetarios Latinoamericanos, CEMLA, vol. 0(1), pages 151-210, enero-jun.
• Carlos León & Fabio Ortega, 2018. "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
  • Carlos León & Fabio Ortega, 2018. "Nowcasting economic activity with electronic payments data: A predictive modeling approach," Borradores de Economia 1037, Banco de la Republica de Colombia.
• Natalia Lamberova & Konstantin Sonin, 2018. "Economic transition and the rise of alternative institutions : Political connections in Putin's Russia," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 26(4), pages 615-648, October.
  • Sonin, Konstantin & Lamberova, Natalia, 2018. "Economic Transition and the Rise of Alternative Institutions: Political Connections in Putin's Russia," CEPR Discussion Papers 13177, C.E.P.R. Discussion Papers.
• S.G. Hall & S.G.B. Henry, 2018. "Macro Modelling at the NIESR: Its Recent History," National Institute Economic Review, National Institute of Economic and Social Research, vol. 246(1), pages 15-23, November.
  • Hall, S.G. & Henry, S.G.B., 2018. "Macro Modelling at the NIESR: Its Recent History," National Institute Economic Review, National Institute of Economic and Social Research, vol. 246, pages 15-23, November.
• Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018. "How Do Households Allocate Risk?," Working Papers 20186, University of Liverpool, Department of Economics.
  • Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018. "How Do Households Allocate Risk?," SOEPpapers on Multidisciplinary Panel Data Research 1000, DIW Berlin, The German Socio-Economic Panel (SOEP).
• Jorge Barrientos Marin & Elkin Tabares Orozco & Esteban Velilla, 2018. "Forecasting electricity price in Colombia: A comparison between Neural Network, ARMA process and Hybrid Models," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 97-106.
• Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
• Di Gangi, Domenico & Lillo, Fabrizio & Pirino, Davide, 2018. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 117-141.
• Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018. "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, vol. 70(C), pages 143-157.
• Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
• He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
• Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
  • Huber, Martin & Imhof, David, 2018. "Machine Learning with Screens for Detecting Bid-Rigging Cartels," FSES Working Papers 494, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
• Sommervoll, Dag Einar & Sommervoll, Åvald, 2018. "Learning from man or machine: Spatial aggregation and house price prediction," CLTS Working Papers 4/18, Norwegian University of Life Sciences, Centre for Land Tenure Studies, revised 16 Oct 2019.
• Kanazawa, Nobuyuki, 2020. "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, vol. 64(C).
  • KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018. "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series HIAS-E-64, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
• Zárate, Héctor & Zapata-Sanabria, Daniel R., 2018. "Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting," IDB Publications (Working Papers) 9053, Inter-American Development Bank.
• Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 1-18, January.
• Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 325-342, January.
• Luis Manuel León Anaya & Víctor Manuel Landassuri Moreno & Héctor Rafael Orozco Aguirre & Maricela Quintana López, 2018. "Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 13(4), pages 603-629, Octubre-D.
• Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018. "How Do Households Allocate Risk?," SOEPpapers on Multidisciplinary Panel Data Research 1000, DIW Berlin, The German Socio-Economic Panel (SOEP).
  • Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018. "How Do Households Allocate Risk?," Working Papers 20186, University of Liverpool, Department of Economics.
• Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018. "How Do Households Allocate Risk?," SOEPpapers on Multidisciplinary Panel Data Research 1000, DIW Berlin, The German Socio-Economic Panel (SOEP).
  • Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018. "How Do Households Allocate Risk?," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2018_14, Max Planck Institute for Research on Collective Goods.
  • Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018. "How Do Households Allocate Risk?," Working Papers 20186, University of Liverpool, Department of Economics.
• Azzimonti, Marina & Fernandes, Marcos, 2023. "Social media networks, fake news, and polarization," European Journal of Political Economy, Elsevier, vol. 76(C).
  • Marina Azzimonti & Marcos Fernandes, 2018. "Social Media Networks, Fake News, and Polarization," NBER Working Papers 24462, National Bureau of Economic Research, Inc.
• Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  • Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
  • Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
• Millán Solarte, Julio César & Caicedo Cerezo, Edinson, 2018. "Modelos para otorgamiento y seguimiento en la gestión del riesgo de crédito || Models for Granting and Tracking in Credit Risk Management," 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, vol. 25(1), pages 23-41, Junio.
• Brummelhuis, Raymond & Luo, Zhongmin, 2018. "Arbitrage Opportunities in CDS Term Structure: Theory and Implications for OTC Derivatives," MPRA Paper 94778, University Library of Munich, Germany.
• Matúš Mihalovič, 2018. "Využitie skóringových modelov pri predikcii defaultu ekonomických subjektov v Slovenskej republike [Applicability of Scoring Models in Firms' Default Prediction. The Case of Slovakia]," Politická ekonomie, Prague University of Economics and Business, vol. 2018(6), pages 689-708.
• Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.
• Hall, S.G. & Henry, S.G.B., 2018. "Macro Modelling at the NIESR: Its Recent History," National Institute Economic Review, National Institute of Economic and Social Research, vol. 246, pages 15-23, November.
  • S.G. Hall & S.G.B. Henry, 2018. "Macro Modelling at the NIESR: Its Recent History," National Institute Economic Review, National Institute of Economic and Social Research, vol. 246(1), pages 15-23, November.
• Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
• Somsri Banditvilai & Siriluck Anansatitzin, 2018. "Comparative Study of Three Time Series Methods in Forecasting Dengue Hemorrhagic Fever Incidence in Thailand," Proceedings of International Academic Conferences 6409199, International Institute of Social and Economic Sciences.
• Lei Zhang, 2018. "Artificial Neural Network Based Chaotic Generator Design for The Prediction of Financial Time Series," Proceedings of International Academic Conferences 6409417, International Institute of Social and Economic Sciences.
• Díaz, Héctor & Sosa, Miriam & Ortiz, Edgar, 2018. "Inclusión financiera y ahorro en México: un análisis logístico binario y de redes neuronales artificiales/Financial inclusion and savings in Mexico: a binary logistic and artificial neural networks an," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 8(1), pages 53-84, enero-jun.
• Gordon H. Dash & Nina Kajiji & Domenic Vonella, 2018. "The role of supervised learning in the decision process to fair trade US municipal debt," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 139-168, June.
• Patrick Röhm, 2018. "Exploring the landscape of corporate venture capital: a systematic review of the entrepreneurial and finance literature," Management Review Quarterly, Springer, vol. 68(3), pages 279-319, August.
• Félix J. López-Iturriaga & Iván Pastor Sanz, 2018. "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 975-998, December.
• Anton Kolotilin & Valentyn Panchenko, 2018. "Estimation of a Scale-Free Network Formation Model," Discussion Papers 2018-10, School of Economics, The University of New South Wales.
• Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
• Gulay Emrah, 2018. "Comparing Simple Forecasting Methods and Complex Methods: A Frame of Forecasting Competition," Scientific Annals of Economics and Business, Sciendo, vol. 65(2), pages 159-169, June.
• Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(1), pages 186-205, January.
  • Maryna Zenkova & Robert Ślepaczuk, 2019. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Working Papers 2019-02, Faculty of Economic Sciences, University of Warsaw.
• Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(52), pages 186-205, January.
  • Maryna Zenkova & Robert Ślepaczuk, 2019. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Working Papers 2019-02, Faculty of Economic Sciences, University of Warsaw.
• Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.
• Nehrebecka Natalia, 2018. "An Evaluation of the Discriminatory Power of Selected Polish Bankruptcy Prediction Models As Part of the Validation Process," Financial Sciences. Nauki o Finansach, Sciendo, vol. 23(4), pages 63-88, December.
• Kaczmarczyk Paweł, 2018. "Neural Network Application to Support Regression Model in Forecasting Single-Sectional Demand for Telecommunications Services," Folia Oeconomica Stetinensia, Sciendo, vol. 18(2), pages 159-177, December.
• Przemysław Ryś & Robert Ślepaczuk, 2018. "Machine learning in algorithmic trading strategy optimization - implementation and efficiency," Working Papers 2018-25, Faculty of Economic Sciences, University of Warsaw.
• Katarzyna Hubicka & Grzegorz Marcjasz & Rafal Weron, 2018. "A note on averaging day-ahead electricity price forecasts across calibration windows," HSC Research Reports HSC/18/03, Hugo Steinhaus Center, Wroclaw University of Technology.
• Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
  • Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2018. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," HSC Research Reports HSC/18/05, Hugo Steinhaus Center, Wroclaw University of Technology.
• Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Katarzyna Maciejowska & Rafal Weron, 2019. "Electricity price forecasting," HSC Research Reports HSC/19/01, Hugo Steinhaus Center, Wroclaw University of Technology.
• Martinho, Vítor João Pereira Domingues, 2018. "Ranking the socioeconomic and environmental framework of European Union farms: A network analysis," EconStor Preprints 173285, ZBW - Leibniz Information Centre for Economics.
• Steinkraus, Arne, 2018. "Rethinking Policy Evaluation – Do Simple Neural Nets Bear Comparison with Synthetic Control Method?," EconStor Preprints 177390, ZBW - Leibniz Information Centre for Economics.
• Jahn, Malte, 2018. "Artificial neural network regression models: Predicting GDP growth," HWWI Research Papers 185, Hamburg Institute of International Economics (HWWI).
• Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
  • Schneider, Kerstin & Berens, Johannes & Oster, Simon & Burghoff, Julian, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181544, Verein für Socialpolitik / German Economic Association.
  • Johannes Berens & Simon Oster & Kerstin Schneider & Julian Burghoff, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," Schumpeter Discussion Papers sdp18006, Universitätsbibliothek Wuppertal, University Library.

2017

• Miroslav Karahuta & Peter Gallo & Daniela Matušíková & Anna Šenková & Kristína Šambronská, 2017. "Forecast Of Using Neural Networks In The Tourism Sector," CBU International Conference Proceedings, ISE Research Institute, vol. 5(0), pages 218-223, September.
• Nimet Melis Esenyel & Melda Akın, 2017. "Comparing Accuracy Performance of ELM, ARMA and ARMA-GARCH Model In Predicting Exchange Rate Return," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 5(1), pages 1-14, June.
• Ufuk Çelik & Çağatay Başarır, 2017. "The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 5(1), pages 45-54, June.
• Yusuf Kuvvetli, 2017. "Returned Product Acquisition Pricing by Adaptive Neuro Fuzzy Inference System," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 5(2), pages 207-214, October.
• Engin Taş, 2017. "Classification of Gene Samples Using Pair-Wise Support Vector Machines," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 5(2), pages 283-292, November.
• Brummelhuis, Raymond & Luo, Zhongmin, 2017. "CDS Rate Construction Methods by Machine Learning Techniques," MPRA Paper 79194, University Library of Munich, Germany.
  • Raymond Brummelhuis & Zhongmin Luo, 2017. "CDS Rate Construction Methods by Machine Learning Techniques," Papers 1705.06899, arXiv.org.
• Inna Strelchenko, 2017. "Modelling Of Scenarios Of The Crisis Phenomena Transfer Among Financial Markets," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 3(2).
• Aleksey Mints, 2017. "Classification Of Tasks Of Data Mining And Data Processing In The Economy," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 3(3).
• Mehmet OZCALICI, 2017. "Market Segmentation with Self-Organizing Maps in Banking Industry," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 11(2), pages 9-30.
• Danilo Leiva-Leon, 2017. "Measuring Business Cycles Intra-Synchronization in US: A Regime-switching Interdependence Framework," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 513-545, August.
  • Danilo Leiva-Leon, 2017. "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers 1726, Banco de España.
• Hector M. Zarate-Solano & Daniel R. Zapata-Sanabria, 2017. "Clustering and forecasting inflation expectations using the World Economic Survey: the case of the 2014 oil price shock on inflation targeting countries," Borradores de Economia 993, Banco de la Republica de Colombia.
• Danilo Leiva-Leon, 2017. "Measuring Business Cycles Intra-Synchronization in US: A Regime-switching Interdependence Framework," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 513-545, August.
  • Danilo Leiva-Leon, 2017. "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers 1726, Banco de España.
• Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
• Patrick Kouontchou & Bertrand Maillet & Alejandro Modesto & Sessi Tokpavi, 2017. "Quand l’union fait la force : un indice de risque systémique," Revue économique, Presses de Sciences-Po, vol. 68(HS1), pages 87-106.
• Alvaro J. Riascos & Mauricio Romero & Natalia Serna, 2017. "Risk Adjustment Revisited using Machine Learning Techniques," Documentos CEDE 15601, Universidad de los Andes, Facultad de Economía, CEDE.
• Andrés González, 2017. "Evaluación de pronósticos de modelos lineales y no lineales de la tasa de cambio de Colombia," Vniversitas Económica 15494, Universidad Javeriana - Bogotá.
• Luis Sandoval Garrido & Margarita Marin Jaramillo, 2017. "The effect of a police sectoral communication network on crime rates in Bogotá, Colombia," Revista Ecos de Economía, Universidad EAFIT, vol. 21(45), pages 5-25, December.
• Andrej Srakar, 2017. "Prevalence of Diseases and Health Care Utilization ofthe Self-Employed Artists and TheirEmpirical Determinants: Evidence From a Slovenian Survey," ACEI Working Paper Series AWP-08-2017, Association for Cultural Economics International, revised Sep 2017.
• Narges TALEBIMOTLAGH & Farzad HASHEMZADEH & Amir RIKHTEHGAR GHIASI & Sehraneh GHAEMI, 2017. "A Novel Method of Modeling Dynamic Evolutionary Game with Rational Agents for Market Forecasting," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 281-302.
• Huseyin INCE & Theodore B. TRAFALİS, 2017. "A Hybrid Forecasting Model for Stock Market Prediction," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(3), pages 263-280.
• Charlie Joyez, 2017. "Network Structure of French Multinational Firms," Working Papers DT/2017/08, DIAL (Développement, Institutions et Mondialisation).
• Sophie Harnay & Elisabeth Tovar, 2017. "Obeying vs. resisting unfair laws. A structural analysis of the internalization of collective preferences on redistribution using classification trees and random forests," EconomiX Working Papers 2017-34, University of Paris Nanterre, EconomiX.
• Maryam Hosseinzadeh & Saeed Daei-Karimzadeh, 2017. "Investigate the Effect of Exchange Rate Volatility on the Demand for Life Insurance in Iran," International Journal of Economics and Financial Issues, Econjournals, vol. 7(2), pages 166-174.
• Zhang, Heng-Guo & Su, Chi-Wei & Song, Yan & Qiu, Shuqi & Xiao, Ran & Su, Fei, 2017. "Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model," Economic Modelling, Elsevier, vol. 67(C), pages 355-367.
• Creel, Michael, 2017. "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, vol. 2(C), pages 36-49.
  • Michael Creel, 2016. "Neural Nets for Indirect Inference," Working Papers 942, Barcelona School of Economics.
• Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
• Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
• Brida, Juan Gabriel & Gómez, David Matesanz & Seijas, Maria Nela, 2017. "Debt and growth: A non-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 883-894.
• O.V. Andreeva & E.V. Shevchik, 2017. "Organizational and Financial Modeling of Transnational Industrial Clusters Sustainable Development: Experience, Risks, Management Innovation," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 137-147.
• Thomas R. Cook & Aaron Smalter Hall, 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper RWP 17-11, Federal Reserve Bank of Kansas City.
  • Thomas Cook, 2019. "Macroeconomic Indicator Forecasting with Deep Neural Networks," 2019 Meeting Papers 402, Society for Economic Dynamics.
• Jason Brown & Maeve Maloney & Jordan Rappaport & Aaron Smalter Hall, 2017. "How Centralized is U.S. Metropolitan Employment?," Research Working Paper RWP 17-16, Federal Reserve Bank of Kansas City.
• Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle & Yarema Okhrin, 2017. "Tail event driven networks of SIFIs," SFB 649 Discussion Papers SFB649DP2017-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Héctor Horacio Garza Sánchez & Klender Aimer Cortez Alejandro & Alma Berenice Méndez Sáenz & Martha del Pilar Rodríguez García, 2017. "Efecto en la calidad de la información ante cambios en la normatividad contable: caso aplicado al sector real mexicano," Contaduría y Administración, Accounting and Management, vol. 62(3), pages 746-760, Julio-Sep.
• Héctor Horacio Garza Sánchez & Klender Aimer Cortez Alejandro & Alma Berenice Méndez Sáenz & Martha del Pilar Rodríguez García, 2017. "Effect of information quality due accounting regulatory changes: Applied case to Mexican real sector," Contaduría y Administración, Accounting and Management, vol. 62(3), pages 761-774, Julio-Sep.
• Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
  • Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
• Evgenia Vasileva, 2017. "Creating of Something from Nothing. Methodic. Applying the Principles of Chaos and Complex Systems in a Learning Environment," Nauchni trudove, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 169-186, October.
• Fajar, Muhammad & Hartini, Sri, 2017. "Inflation forecasting by hybrid singular spectrum analysis – multilayer perceptrons neural network method, case of Indonesia," MPRA Paper 105100, University Library of Munich, Germany, revised 11 May 2018.
• de Rigo, Daniele & Caudullo, Giovanni & San-Miguel-Ayanz, Jesús & Barredo, José I., 2017. "Robust modelling of the impacts of climate change on the habitat suitability of forest tree species," MPRA Paper 78623, University Library of Munich, Germany.
• Raymond Brummelhuis & Zhongmin Luo, 2017. "CDS Rate Construction Methods by Machine Learning Techniques," Papers 1705.06899, arXiv.org.
  • Brummelhuis, Raymond & Luo, Zhongmin, 2017. "CDS Rate Construction Methods by Machine Learning Techniques," MPRA Paper 79194, University Library of Munich, Germany.
• Milan Fičura, 2017. "Forecasting Stock Market Realized Variance with Echo State Neural Networks," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2017(3), pages 145-155.
• Kaznacheev, Peter F. (Казначеев, Петр) & Kjurchiski, Nikola V. (Кюрчиски, Никола) & Samoilova, Regina V. (Самойлова, Регина), 2017. "Adaptation to Lower Oil Prices: International Corporations and Junior Shale Companies [Адаптация К Снижению Цен На Нефть: Международные Корпорации И Сланцевые Компании]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 6, pages 148-159, December.
• Dimitrios Lyridis & Nikolaos Manos & Panayotis Zacharioudakis & Athanassios Pappas & Aristidis Mavris, 2017. "Measuring Tanker Market Future Risk with the use of FORESIM," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 38-53, January-M.
• Florian Kreuchauff & Vladimir Korzinov, 2017. "A patent search strategy based on machine learning for the emerging field of service robotics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 743-772, May.
• Anthony Mouraud, 2017. "Innovative time series forecasting: auto regressive moving average vs deep networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 4(3), pages 282-293, March.
• Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
  • León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Discussion Paper 2017-009, Tilburg University, Center for Economic Research.
  • León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Other publications TiSEM 75d8648e-9855-4c5c-9aa9-0, Tilburg University, School of Economics and Management.
• Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
  • León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Other publications TiSEM 75d8648e-9855-4c5c-9aa9-0, Tilburg University, School of Economics and Management.
  • León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Discussion Paper 2017-009, Tilburg University, Center for Economic Research.
• Jaime Tinto Arandes & Kléber Antonio Luna Altamirano & William Henry Sarmiento Espinoza & Diego Patricio Cisneros Quintanilla, 2017. "STIM12 creativity model for the design lady shoes under the approach of fuzzy subsets," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 42(44), pages 129-152, july-dece.
• Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
  • Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras & Rangan Gupta, 2015. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," Working Papers 201548, University of Pretoria, Department of Economics.
  • Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
• Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Technology.
• Altgelt, Friederike & Koetter, Michael, 2017. "Too connected to fail? Wie die Vernetzung der Banken staatliche Rettungsmaßnahmen vorhersagen kann," Wirtschaft im Wandel, Halle Institute for Economic Research (IWH), vol. 23(4), pages 75-78.
• Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2017. "Tail event driven networks of SIFIs," SFB 649 Discussion Papers 2017-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

2016

• Anne Péguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 123-124, pages 77-101.
  • Anne Peguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Post-Print hal-04218472, HAL.
• Simone Borghesi & Andrea Flori, 2016. "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," Working Papers 2016.08, Fondazione Eni Enrico Mattei.
  • Borghesi, Simone & Flori, Andrea, 2016. "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," MITP: Mitigation, Innovation and Transformation Pathways 232214, Fondazione Eni Enrico Mattei (FEEM).
• Jozwiak, Akos & Milkovics, Matyas & Lakner, Zoltan, 2016. "A Network-Science Support System for Food Chain Safety: A Case from Hungarian Cattle Production," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(A), pages 1-26, June.
• Yaroslav I. VYKLYUK & Valeriy K. YEVDOKYMENKO & Ihor V. YASKAL, 2016. "The Proportions And Rates Of Economic Activities As A Factor Of Gross Value Added Maximization In Transition Economy," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 63(1), pages 55-64, March.
• Gintaras CERNIUS & Liucija BIRSKYTE & Arturas BALKEVICIUS, 2016. "Influence Of Rules For Computing Corporate Income Tax On The Accuracy Of Financial Statements Of Lithuanian Companies," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 63(1), pages 65-81, March.
• Özcan Mutlu & Muhammed Ordu & Olcay Polat, 2016. "Comparison of Individual Pension System and Bank's Deposit System for Low-Risk Investors," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 95-114, September.
• Evgeniya Kozlova & Vladimir Volynsky, 2016. "A fuzzy model for the evaluation of suppliers of material resources to machine-building enterprises," International Economics, University of Lodz, Faculty of Economics and Sociology, issue 15, pages 245-277, September.
• A?da Kammoun & Imen Triki, 2016. "Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 61-78, February.
• Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
  • León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Discussion Paper 2017-009, Tilburg University, Center for Economic Research.
  • León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Other publications TiSEM 75d8648e-9855-4c5c-9aa9-0, Tilburg University, School of Economics and Management.
• Creel, Michael, 2017. "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, vol. 2(C), pages 36-49.
  • Michael Creel, 2016. "Neural Nets for Indirect Inference," Working Papers 942, Barcelona School of Economics.
• Jonnathan R. Cáceres Santos, 2016. "Pronóstico de la actividad económica con base en el volumen transaccional - caso boliviano," Revista de Análisis del BCB, Banco Central de Bolivia, vol. 24(1), pages 115-145, June.
• Molina, José Alberto & Alcolea, Alberto & Ferrer, Alfredo & Iñiguez, David & Rivero, Alejandro & Ruiz, Gonzalo & Tarancón, Alfonso, 2016. "Co-authorship and Academic Productivity in Economics: Interaction Maps from the Complex Networks Approach," IZA Discussion Papers 10008, Institute of Labor Economics (IZA).
  • José Alberto Molina & Alberto Alcolea & Alfredo Ferrer & Alberto Alcolea & David Iñiguez & Alejandro Rivero & Gonzalo Ruiz & Alfonso Tarancón, 2016. "Co-authorship and Academic Productivity in Economics: Interaction Maps from the Complex Networks Approach," Boston College Working Papers in Economics 914, Boston College Department of Economics.
• Oscar Claveria & Enric Monte & Salvador Torra, 2016. "A self-organizing map analysis of survey-based agents? expectations before impending shocks for model selection: The case of the 2008 financial crisis," International Economics, CEPII research center, issue 146, pages 40-58.
• Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
• Andrej Srakar & Petja Grafenauer & Marilena Vecco, 2016. "Being Central and Productive? Evidence from Slovenian Visual Artists in the 19th and 20th Century," ACEI Working Paper Series AWP-09-2016, Association for Cultural Economics International, revised Sep 2016.
• Vasile GEORGESCU, 2016. "Using Nature-Inspired Metaheuristics to Train Predictive Machines," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 5-24.
• Cătălina-Lucia COCIANU & Hakob GRIGORYAN, 2016. "Machine Learning Techniques For Stock Market Prediction.Acase Study Of Omv Petrom," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 63-82.
• Vergote, Olivier, 2016. "Credit risk spillover between financials and sovereigns in the euro area during 2007-2015," Working Paper Series 1898, European Central Bank.
• Montagna, Mattia & Kok, Christoffer, 2013. "Multi-layered interbank model for assessing systemic risk," Kiel Working Papers 1873, Kiel Institute for the World Economy (IfW Kiel).
  • Kok, Christoffer & Montagna, Mattia, 2016. "Multi-layered interbank model for assessing systemic risk," Working Paper Series 1944, European Central Bank.
• Endrész, Marianna & Skudelny, Frauke, 2016. "Crisis severity and the international trade network," Working Paper Series 1971, European Central Bank.
• Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
  • Jozef Barunik & Barbora Malinska, 2015. "Forecasting the term structure of crude oil futures prices with neural networks," Papers 1504.04819, arXiv.org.
  • Jozef Barunik & Barbora Malinska, 2015. "Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks," Working Papers IES 2015/25, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2015.
• Kiani, Khurshid M., 2016. "On business cycle fluctuations in USA macroeconomic time series," Economic Modelling, Elsevier, vol. 53(C), pages 179-186.
• Ductor, Lorenzo & Leiva-Leon, Danilo, 2016. "Dynamics of global business cycle interdependence," Journal of International Economics, Elsevier, vol. 102(C), pages 110-127.
  • Lorenzo Ductor & Danilo Leiva-Leon, 2015. "Dynamics of Global Business Cycles Interdependence," Working Papers Central Bank of Chile 763, Central Bank of Chile.
• Claveria, Oscar & Monte, Enric & Torra, Salvador, 2016. "A self-organizing map analysis of survey-based agents׳ expectations before impending shocks for model selection: The case of the 2008 financial crisis," International Economics, Elsevier, vol. 146(C), pages 40-58.
• Borghesi, Simone & Flori, Andrea, 2016. "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," MITP: Mitigation, Innovation and Transformation Pathways 232214, Fondazione Eni Enrico Mattei (FEEM).
  • Simone Borghesi & Andrea Flori, 2016. "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," Working Papers 2016.08, Fondazione Eni Enrico Mattei.
• Ben Craig & Martín Saldías, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," IMF Working Papers 2016/184, International Monetary Fund.
  • Ben R. Craig & Martin Saldias Zambrana, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," Working Papers (Old Series) 1627, Federal Reserve Bank of Cleveland.
• David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2016. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Risks, MDPI, vol. 4(1), pages 1-14, March.
  • David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2015. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Tinbergen Institute Discussion Papers 15-125/III, Tinbergen Institute.
  • Allen, D.E. & McAleer, M.J. & Peiris, S. & Singh, A.K., 2015. "Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies," Econometric Institute Research Papers EI2015-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Leoni Eleni Oikonomikou, 2016. "Forecasting the Market Risk Premium with Artificial Neural Networks," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 202, Courant Research Centre PEG.
• Leoni Eleni Oikonomikou, 2016. "Comparing the market risk premia forecasts in JSE and NYSE equity markets," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 203, Courant Research Centre PEG.
• Anne Péguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 123-124, pages 77-101.
  • Anne Péguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Post-Print hal-01448238, HAL.
• Anne Péguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 123-124, pages 77-101.
  • Anne Peguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Post-Print hal-04218472, HAL.
• Fahima Charef & Fethi Ayachi, 2016. "A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 6(1), pages 244-253, January.
  • Fahima Charef & Fethi Ayachi, 2016. "A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 6(1), pages 94-99, January.
• Fahima Charef & Fethi Ayachi, 2016. "A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 6(1), pages 94-99, January.
  • Fahima Charef & Fethi Ayachi, 2016. "A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 6(1), pages 244-253, January.
• Candace Blayney & Karen Blotnicky, 2016. "Career Strategies Of Hotel Managers In Canada," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 10(2), pages 33-48.
• Stephanie Valdivia & Arturo Morales, 2016. "Determinants Of The Index Of Prices And Quotations On The Mexican Stock Exchange: Sensitivity Analysis Based On Artificial Neural Networks," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 10(2), pages 27-32.
• Ben R. Craig & Martin Saldias Zambrana, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," Working Papers (Old Series) 1627, Federal Reserve Bank of Cleveland, revised 02 Dec 2016.
  • Ben Craig & Martín Saldías, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," IMF Working Papers 16/184, International Monetary Fund.
• Kireyev, A., 2019. "A Network Model of Multilateral Equilibrium Exchange Rates," Journal of the New Economic Association, New Economic Association, vol. 41(1), pages 12-33.
  • Mr. Alexei P Kireyev & Andrei Leonidov, 2016. "A Network Model of Multilaterally Equilibrium Exchange Rates," IMF Working Papers 2016/130, International Monetary Fund.
• Ben R. Craig & Martin Saldias Zambrana, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," Working Papers (Old Series) 1627, Federal Reserve Bank of Cleveland.
  • Ben Craig & Martín Saldías, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," IMF Working Papers 2016/184, International Monetary Fund.
• Michel Philipp & Achim Zeileis & Carolin Strobl, 2016. "A Toolkit for Stability Assessment of Tree-Based Learners," Working Papers 2016-11, Faculty of Economics and Statistics, Universität Innsbruck.
• Florian Wickelmaier & Achim Zeileis, 2016. "Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models," Working Papers 2016-26, Faculty of Economics and Statistics, Universität Innsbruck.
• José Alberto Molina & Alberto Alcolea & Alfredo Ferrer & Alberto Alcolea & David Iñiguez & Alejandro Rivero & Gonzalo Ruiz & Alfonso Tarancón, 2016. "Co-authorship and Academic Productivity in Economics: Interaction Maps from the Complex Networks Approach," Boston College Working Papers in Economics 914, Boston College Department of Economics.
  • Molina, José Alberto & Alcolea, Alberto & Ferrer, Alfredo & Iñiguez, David & Rivero, Alejandro & Ruiz, Gonzalo & Tarancón, Alfonso, 2016. "Co-authorship and Academic Productivity in Economics: Interaction Maps from the Complex Networks Approach," IZA Discussion Papers 10008, Institute of Labor Economics (IZA).
• Julian Hagenauer, 2016. "Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks," Journal of Geographical Systems, Springer, vol. 18(1), pages 1-15, January.
• Julian Hagenauer, 2016. "Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks," Journal of Geographical Systems, Springer, vol. 18(1), pages 1-15, January.
• Badal-Valero, Elena & García-Cárceles, Belén, 2016. "Detección de fraude financiero mediante redes neuronales de clasificación en un caso real español /Detecting Financial Fraud using Neural Network Classification Models in a Real Spanish Case," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 34, pages 683-700, Agosto.
• Salehi, Mehdi & Hamidehpour, Kiana & Khadem, Hamid, 2016. "Comparison of Forecasting the Index Price Movement in Financial Institutions using Artificial Intelligence (in Persian)," Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی), Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 9(27), pages 131-170, April.
• Áron Horváth & Blanka Imre & Zoltán Sápi, 2016. "The International Practice of Statistical Property Valuation Methods and the Possibilities of Introducing Automated Valuation Models in Hungary," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 15(4), pages 45-64.
• Ricardo Artemio Chávez Meza & Arturo Ángel Lara Rivero, 2016. "The diversity of agents and evolution of overlapping patents on electric vehicles," Contaduría y Administración, Accounting and Management, vol. 61(4), pages 603-628, Octubre-D.
• Daniel Goetz, 2016. "Broadband Mergers and Dynamic Bargaining: An Application to Netflix," Working Papers 16-07, NET Institute.
• Xiao Liu & Dokyun Lee & Kannan Srinivasan, 2016. "The Effect of Word of Mouth on Sales: New Answers from the Comprehensive Consumer Journey Data," Working Papers 16-09, NET Institute.
• Monika Hadas-Dyduch & Adam P. Balcerzak & Michal Bernard Pietrzak, 2016. "Wavelet Analisis of Unemployment Rate in Visegrad Countries," Chapters, in: Tomas Kliestik (ed.),16th International Scientific Conference Globalization and Its Socio-Economic Consequences. University of Zilina, The Faculty of Operation and Economi, edition 1, volume 0, pages 595-602, Institute of Economic Research.
  • Monika Hadas-Dyduch & Michal Bernard Pietrzak & Adam P. Balcerzak, 2016. "Wavelet Analysis of Unemployment Rate in Visegrad Countries," Working Papers 37/2016, Institute of Economic Research, revised Sep 2016.
• Monika Hadas-Dyduch & Adam P. Balcerzak & Michal Bernard Pietrzak, 2016. "Wavelet Analisis of Unemployment Rate in Visegrad Countries," Chapters, in: Tomas Kliestik (ed.),16th International Scientific Conference Globalization and Its Socio-Economic Consequences. University of Zilina, The Faculty of Operation and Economi, edition 1, volume 0, pages 595-602, Institute of Economic Research.
  • Monika Hadas-Dyduch & Michal Bernard Pietrzak & Adam P. Balcerzak, 2016. "Wavelet Analysis of Unemployment Rate in Visegrad Countries," Working Papers 37/2016, Institute of Economic Research, revised Sep 2016.
• Gawlik, Remigiusz, 2016. "Methodological Aspects of Qualitative-Quantitative Analysis of Decision-Making Processes," MPRA Paper 72362, University Library of Munich, Germany.
• Alsayyed, Nidal & Zhu, Weihang, 2016. "Neural Network Models of Regulating Natural Capital Funds for Renewable Energy," MPRA Paper 74191, University Library of Munich, Germany.
• Basihos, Seda, 2016. "Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey," MPRA Paper 75553, University Library of Munich, Germany, revised 09 Sep 2016.
• 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, vol. 47(C), pages 304-310.
  • Aviral Kumar Tiwari & Rangan Gupta & Stelios Bekiros, 2016. "Chaos in G7 Stock Markets using Over One Century of Data: A Note," Working Papers 201678, University of Pretoria, Department of Economics.
• Peter F. Kaznacheev (Казначеев, Петр) & Regina V. Samoilova (Самойлова, Регина) & Nikola V. Kjurchiski (Курчиски, Никола), 2016. "Improving Efficiency of the Oil and Gas Sector and Other Extractive Industries by Applying Methods of Artificial Intelligence [Применение Методов Искусственного Интеллекта Для Повышения Эффективнос," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 5, pages 188-197, October.
• Loretta Mastroeni & Pierluigi Vellucci, 2016. "“Butterfly Effect" vs Chaos in Energy Futures Markets," Departmental Working Papers of Economics - University 'Roma Tre' 0209, Department of Economics - University Roma Tre.
• Chun Wei R. Lin & Yun-Jiuan Melody Parng & Hong-Yi Chen, 2016. "A Fuzzy-Neural Performance Evaluation Approach of Selecting Outsource International Logistic Company," Proceedings of Economics and Finance Conferences 3205849, International Institute of Social and Economic Sciences.
• Şerafettin SEVİM & Birol YILDIZ & Nilüfer DALKILIÇ, 2016. "Risk Assessment for Accounting Professional Liability Insurance," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
• Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
• Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
• Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
  • Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques," CREATES Research Papers 2011-27, Department of Economics and Business Economics, Aarhus University.
• Vladyslav Rashkovan & Dmytro Pokidin, 2016. "Ukrainian Banks' Business Models Clustering: Application of Kohonen Neural Networks," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 238, pages 13-38.
• Alberto José Hurtado Briceño, 2016. "Measuring the impact of Mision Alimentacion in Merida by means of fuzzy logic," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 41(42), pages 105-132, july-dece.
• Francis Bismans & Igor N. Litvine, 2016. "Forecasting with Neural Networks Models," Working Papers of BETA 2016-28, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
• EMAMVERDI, Ghodratollah & KARIMI, Mohammad Sharif & KHAKIE, Sima & KARIMI, Mojtaba, 2016. "Forecasting The Total Index Of Tehran Stock Exchange," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 20(1), pages 54-68.
• Muczyński Andrzej & Walacik Marek, 2016. "Neural Networks Modelling of Municipal Real Estate Market Rent Rates," Folia Oeconomica Stetinensia, Sciendo, vol. 16(2), pages 17-28, December.
• Kaczmarczyk Paweł, 2016. "Integrated Model of Demand for Telephone Services in Terms of Microeconometrics," Folia Oeconomica Stetinensia, Sciendo, vol. 16(2), pages 72-83, December.
• Ćorić, Ivica, 2016. "Comparison of Multivariate Statistical Analysis and Machine Learning Methods in Retailing: Research Framework Proposition," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2016), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016, pages 76-82, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
• Bilal Zorić, Alisa, 2016. "Determinants of Efficacy of Studying in the Republic Croatia - Comparing Neural Networks and Decision Trees: Research Framework Proposition," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2016), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016, pages 123-129, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
• Alisa Bilal Zoric, 2016. "Predicting customer churn in banking industry using neural networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 14(2), pages 116-124.

2015

• Anne Péguin-Feissolle & Bilel Sanhaji, 2015. "Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)," Working Papers halshs-01133751, HAL.
  • Anne Péguin-Feissolle & Bilel Sanhaji, 2015. "Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)," AMSE Working Papers 1516, Aix-Marseille School of Economics, France.
• Sihem Khemakhem & Younes Boujelbene, 2015. "Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 14(1), pages 60-78, March.
• Aida Krichene Abdelmoula, 2015. "Bank Credit Risk Analysis with K-Nearest-Neighbor Classifier: Case of Tunisian Banks," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 14(1), pages 79-106, March.
• Vesile Sinem Arıkan Kargı, 2015. "A Comparison of Artificial Neural Networks and Multiple Linear Regression Models As Predictors of Discard Rates In Plastic Injection Molding," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(2), pages 65-72, December.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," IREA Working Papers 201502, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," AQR Working Papers 201502, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques”," IREA Working Papers 201503, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques”," AQR Working Papers 201503, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," IREA Working Papers 201507, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," AQR Working Papers 201506, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
• Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
  • Jozef Barunik & Barbora Malinska, 2015. "Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks," Working Papers IES 2015/25, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2015.
  • Jozef Barunik & Barbora Malinska, 2015. "Forecasting the term structure of crude oil futures prices with neural networks," Papers 1504.04819, arXiv.org.
• Ferdi SONMEZ & Metin ZONTUL & Sahamet BULBUL, 2015. "Estimating Deposit Banks Profitability with Artificial Neural Networks: A Software Model Design," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 9(1), pages 9-46.
• Jasna Soldić-Aleksić & Rade Stankić, 2015. "A Comparative Analysis Of Serbia And The Eu Member States In The Context Of The Networked Readiness Index Values," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 60(206), pages 45-86, July - Se.
• S Battiston & G di Iasio & L Infante & F Pierobon, 2015. "Capital and contagion in financial networks," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.
  • di Iasio, Giovanni & Battiston, Stefano & Infante, Luigi & Pierobon, Federico, 2013. "Capital and Contagion in Financial Networks," MPRA Paper 52141, University Library of Munich, Germany.
• Zekić-Sušac Marijana & Has Adela, 2015. "Data Mining as Support to Knowledge Management in Marketing," Business Systems Research, Sciendo, vol. 6(2), pages 18-30, September.
• Margherita Comola & Mariapia Mendola, 2015. "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(2), pages 592-618, April.
  • Margherita Comola & Mariapia Mendola, 2013. "The Formation of Migrant Networks," Development Working Papers 353, Centro Studi Luca d'Agliano, University of Milano.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Post-Print hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint) hal-00977544, HAL.
  • Comola, Margherita & Mendola, Mariapia, 2014. "The Formation of Migrant Networks," IZA Discussion Papers 7981, Institute of Labor Economics (IZA).
• Jonnathan Cáceres Santos, 2015. "Identificación de instituciones financieras sistémicamente importantes en Bolivia a través de la construcción de mapas auto-organizados. Aproximaciones macro y microprudencial," Serie de Documentos de Trabajo 2015/01, Banco Central de Bolivia.
• David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
• Ductor, Lorenzo & Leiva-Leon, Danilo, 2016. "Dynamics of global business cycle interdependence," Journal of International Economics, Elsevier, vol. 102(C), pages 110-127.
  • Lorenzo Ductor & Danilo Leiva-Leon, 2015. "Dynamics of Global Business Cycles Interdependence," Working Papers Central Bank of Chile 763, Central Bank of Chile.
• Mihaela GHEORGHE, 2015. "A Support Vector Machine Approach For Developing Telemedicine Solutions: Medical Diagnosis," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 5, pages 43-48, June.
• Gustavo Peralta, 2015. "Network-based Measures as Leading Indicators of Market Instability: The case of the Spanish Stock," CNMV Working Papers CNMV Working Papers no 59, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
• Þebnem KOLTAN YILMAZ & M. Mustafa YÜCEL, 2015. "Concrete strength control charts pattern recognition based on Linear Vector Quantization neural networks," Eurasian Eononometrics, Statistics and Emprical Economics Journal, Eurasian Academy Of Sciences, vol. 2(2), pages 1-15, October.
• Samir Elhedhli & Canan Akdemir & Thomas Astebro, 2014. "Classification models via Tabu search: An application to early stage venture classification," Post-Print hal-01066492, HAL.
  • Astebro , Thomas & Akdemir , Canan & Elhedhli , Samir, 2015. "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," HEC Research Papers Series 1097, HEC Paris.
  • Samir Elhedhli & Canan Akdemir & Thomas Astebro, 2015. "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," Working Papers hal-02002758, HAL.
• Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.
• Castelli, Mauro & Vanneschi, Leonardo & De Felice, Matteo, 2015. "Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case," Energy Economics, Elsevier, vol. 47(C), pages 37-41.
• Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
• Arundina, Tika & Azmi Omar, Mohd. & Kartiwi, Mira, 2015. "The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 273-292.
• Khediri, Karim Ben & Charfeddine, Lanouar & Youssef, Slah Ben, 2015. "Islamic versus conventional banks in the GCC countries: A comparative study using classification techniques," Research in International Business and Finance, Elsevier, vol. 33(C), pages 75-98.
• David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2016. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Risks, MDPI, vol. 4(1), pages 1-14, March.
  • David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2015. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Tinbergen Institute Discussion Papers 15-125/III, Tinbergen Institute.
  • Allen, D.E. & McAleer, M.J. & Peiris, S. & Singh, A.K., 2015. "Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies," Econometric Institute Research Papers EI2015-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Antonio Blanco-Oliver & Ana Irimia-Dieguez & María Oliver-Alfonso & Nicholas Wilson, 2015. "Systemic Sovereign Risk and Asset Prices: Evidence from the CDS Market, Stressed European Economies and Nonlinear Causality Tests," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(2), pages 144-166, April.
• Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
  • Jozef Barunik & Barbora Malinska, 2015. "Forecasting the term structure of crude oil futures prices with neural networks," Papers 1504.04819, arXiv.org.
  • Jozef Barunik & Barbora Malinska, 2015. "Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks," Working Papers IES 2015/25, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2015.
• Càmara-Turull, X. & Fernández Izquierdo, M.A. & Sorrosal Forradellas, M.T., 2015. "How Do Different Time Spans Affect The Prediction Accuracy Of Business Failure?," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 71-89, May.
• Margherita Comola & Mariapia Mendola, 2015. "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(2), pages 592-618, April.
  • Margherita Comola & Mariapia Mendola, 2013. "The Formation of Migrant Networks," Development Working Papers 353, Centro Studi Luca d'Agliano, University of Milano.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Post-Print hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint) hal-00977544, HAL.
  • Comola, Margherita & Mendola, Mariapia, 2014. "The Formation of Migrant Networks," IZA Discussion Papers 7981, Institute of Labor Economics (IZA).
• Margherita Comola & Mariapia Mendola, 2015. "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(2), pages 592-618, April.
  • Margherita Comola & Mariapia Mendola, 2013. "The Formation of Migrant Networks," Development Working Papers 353, Centro Studi Luca d'Agliano, University of Milano.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Post-Print hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint) hal-00977544, HAL.
  • Comola, Margherita & Mendola, Mariapia, 2014. "The Formation of Migrant Networks," IZA Discussion Papers 7981, Institute of Labor Economics (IZA).
• Margherita Comola & Mariapia Mendola, 2015. "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(2), pages 592-618, April.
  • Margherita Comola & Mariapia Mendola, 2013. "The Formation of Migrant Networks," Development Working Papers 353, Centro Studi Luca d'Agliano, University of Milano.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE - Labex "OSE-Ouvrir la Science Economique" hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Post-Print hal-00977544, HAL.
  • Comola, Margherita & Mendola, Mariapia, 2014. "The Formation of Migrant Networks," IZA Discussion Papers 7981, Institute of Labor Economics (IZA).
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint) hal-00977544, HAL.
• Margherita Comola & Mariapia Mendola, 2015. "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(2), pages 592-618, April.
  • Margherita Comola & Mariapia Mendola, 2013. "The Formation of Migrant Networks," Development Working Papers 353, Centro Studi Luca d'Agliano, University of Milano.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Post-Print hal-00977544, HAL.
  • Comola, Margherita & Mendola, Mariapia, 2014. "The Formation of Migrant Networks," IZA Discussion Papers 7981, Institute of Labor Economics (IZA).
• Samir Elhedhli & Canan Akdemir & Thomas Astebro, 2014. "Classification models via Tabu search: An application to early stage venture classification," Post-Print hal-01066492, HAL.
  • Samir Elhedhli & Canan Akdemir & Thomas Astebro, 2015. "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," Working Papers hal-02002758, HAL.
  • Astebro , Thomas & Akdemir , Canan & Elhedhli , Samir, 2015. "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," HEC Research Papers Series 1097, HEC Paris.
• Anne Péguin-Feissolle & Bilel Sanhaji, 2015. "Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)," AMSE Working Papers 1516, Aix-Marseille School of Economics, France.
  • Anne Péguin-Feissolle & Bilel Sanhaji, 2015. "Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)," Working Papers halshs-01133751, HAL.
• Green, Rikard, 2015. "A Power Market Forward Curve with Hydrology Dependence An Approach based on Artificial Neural Networks," Knut Wicksell Working Paper Series 2015/1, Lund University, Knut Wicksell Centre for Financial Studies.
• Kasa, Richard, 2015. "Approximating Innovation Potential With Neurofuzzy Robust Model / Aproximación Al Potencial Innovador Con Un Modelo Robusto De Neuro-Fuzzy," Investigaciones Europeas de Dirección y Economía de la Empresa (IEDEE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 21(1), pages 35-46.
• Jitendra Aswani, 2015. "Analyzing the impact of global financial crisis on the interconnectedness of Asian stock markets using network science," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2015-020, Indira Gandhi Institute of Development Research, Mumbai, India.
• Marjolein Fokkema & Niels Smits & Achim Zeileis & Torsten Hothorn & Henk Kelderman, 2015. "Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees," Working Papers 2015-10, Faculty of Economics and Statistics, Universität Innsbruck.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," AQR Working Papers 201502, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," IREA Working Papers 201502, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques”," AQR Working Papers 201503, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques”," IREA Working Papers 201503, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," AQR Working Papers 201506, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," IREA Working Papers 201507, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
• M. Kudic & Wilfried Ehrenfeld & T. Pusch, 2015. "Isolation and Innovation – Two Contradictory Concepts? Explorative Findings from the German Laser Industry," IWH Discussion Papers 1, Halle Institute for Economic Research.
• Meysam Effati & Jean-Claude Thill & Shahin Shabani, 2015. "Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor," Journal of Geographical Systems, Springer, vol. 17(2), pages 107-135, April.
• Muhammad Ramzan Sheikh & Muhammad Aslam, 2015. "Is There an Arms Race Between Pakistan and India? An Application of GMM," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 20(2), pages 35-51, July-Dec.
• Ádám Banai & András Kollarik & András Szabó-Solticzky, 2015. "Topology of the foreign currency/forint swap market," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 14(2), pages 28-157.
• Wenyun Tang & Lin Cheng, 2015. "Analyzing Multiday Route Choice Behavior using GPS Data," Working Papers 000135, University of Minnesota: Nexus Research Group.
• Goldstone, Robert L., 2015. "Homo Economicus and Homo Sapiens," Review of Behavioral Economics, now publishers, vol. 2(1-2), pages 77-87, July.
• Tomasz Jasinski & Agnieszka Scianowska, 2015. "Security Assessment And Optimization Of Energy Supply (Neural Networks Approach)," Oeconomia Copernicana, Institute of Economic Research, vol. 6(2), pages 129-141, June.
• Kulaksizoglu, Tamer, 2015. "Measuring the Core Inflation in Turkey with the SM-AR Model," MPRA Paper 62653, University Library of Munich, Germany.
• Matkovskyy, Roman & Bouraoui, Taoufik & Hammami, Helmi, 2015. "Estimation and prediction of an Index of Financial Safety of Tunisia," MPRA Paper 74573, University Library of Munich, Germany, revised 2016.
• Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
• Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
  • Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras & Rangan Gupta, 2015. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," Working Papers 201548, University of Pretoria, Department of Economics.
  • Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
• Ramaprasad Bhar & A.G. Malliaris & Mary Malliaris, 2015. "Quantitative Easing and the U.S. Stock Market: A Decision Tree Analysis," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 7(2), pages 135-156, December.
• Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.
• Muhamed Kudic & Wilfried Ehrenfeld & Toralf Pusch, 2015. "On the trail of core–periphery patterns in innovation networks: measurements and new empirical findings from the German laser industry," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 55(1), pages 187-220, October.
• Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2015. "Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns," Computational Statistics, Springer, vol. 30(3), pages 821-843, September.
• Harun Özkan & M. Yazgan, 2015. "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, vol. 48(2), pages 609-626, March.
• David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2016. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Risks, MDPI, vol. 4(1), pages 1-14, March.
  • Allen, D.E. & McAleer, M.J. & Peiris, S. & Singh, A.K., 2015. "Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies," Econometric Institute Research Papers EI2015-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2015. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Tinbergen Institute Discussion Papers 15-125/III, Tinbergen Institute.
• Kim Ristolainen, 2015. "Were the Scandinavian Banking Crises Predictable? A Neural Network Approach," Discussion Papers 99, Aboa Centre for Economics.
• Dmytro Pokidin, 2015. "National Bank of Ukraine Econometric Model for the Assessment of Banks’ Credit Risk and Support Vector Machine Alternative," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 234, pages 52-72.
• Bilal Zorić, Alisa, 2015. "Case Study in Banking Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 251-257, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
• Kudic, Muhamed & Ehrenfeld, Wilfried & Pusch, Toralf, 2015. "Isolation and Innovation – Two Contradictory Concepts? Explorative Findings from the German Laser Industry," IWH Discussion Papers 1/2015, Halle Institute for Economic Research (IWH).
• Kreuchauff, Florian & Korzinov, Vladimir, 2015. "A patent search strategy based on machine learning for the emerging field of service robotics," Working Paper Series in Economics 71, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
• Kephart, Curtis & Friedman, Daniel & Baumer, Matt, 2015. "Emergence of networks and market institutions in a large virtual economy," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2015-502, WZB Berlin Social Science Center.

2014

• Mary Violeta Bar, 2014. "The Computational Intelligence Techniques For Predictions - Artificial Neural Networks," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 2(42), pages 184-190.
• Miklós Virág & Tamás Nyitrai, 2014. "Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 64(4), pages 419-440, December.
• Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," AQR Working Papers 201410, University of Barcelona, Regional Quantitative Analysis Group, revised May 2014.
• Leiva-Leon, Danilo, 2013. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," MPRA Paper 54452, University Library of Munich, Germany.
  • Danilo Leiva-Leon, 2014. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," Staff Working Papers 14-38, Bank of Canada.
• Zekić-Sušac Marijana & Pfeifer Sanja & Šarlija Nataša, 2014. "A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem," Business Systems Research, Sciendo, vol. 5(3), pages 82-96, September.
• David Matesanz & Benno Torgler & Germán Dabat & Guillermo J. Ortega, 2014. "Co-movements in commodity prices: a note based on network analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 45(S1), pages 13-21, November.
  • David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler & German Dabat, 2011. "Co-movements in commodity prices: A note based on network analysis," CREMA Working Paper Series 2011-21, Center for Research in Economics, Management and the Arts (CREMA).
• Julien Boelaert, 2014. "Une seule fonction de demande ?. Une enquête sur la stabilité des préférences par mélanges discrets de réseaux de neurones," Revue économique, Presses de Sciences-Po, vol. 65(4), pages 515-535.
• Charle Augusto Londono & Juan Carlos Correa & Mauricio Lopera, 2014. "Estimación bayesiana del valor en riesgo: una aplicación para el mercado de valores colombiano," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, August.
• Mauricio Lopera & Ramón Javier Mesa & Charle Londoño, 2014. "Evaluando las intervenciones cambiarias en Colombia: 2004-2012," Estudios Gerenciales, Universidad Icesi, March.
• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
• Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
• Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.
• Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
• Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
  • Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009," CREATES Research Papers 2011-28, Department of Economics and Business Economics, Aarhus University.
• Abbruzzo, Antonino & Brida, Juan Gabriel & Scuderi, Raffaele, 2014. "Determinants of individual tourist expenditure as a network: Empirical findings from Uruguay," Tourism Management, Elsevier, vol. 43(C), pages 36-45.
  • Antonio Abbruzzo & Juan Gabriel Brida & Raffaele Scuderi, 2013. "Determinants of Individual Tourist Expenditure as a Network: Empirical Findings from Uruguay," BEMPS - Bozen Economics & Management Paper Series BEMPS09, Faculty of Economics and Management at the Free University of Bozen.
• Mahmut ZORTUK & Berna BESER, 2014. "Disa Aciklik ve Demokratik Yapinin Kamu Kesimi Buyuklugu Uzerindeki Etkisi: Rodrik Hipotezine Gecis Ekonomilerinden Kanit," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 14(3), pages 345-359.
• Selahattin YAVUZ & Muhammet DEVECI, 2014. "Bulanik TOPSIS ve Bulanik VIKOR Yontemleriyle Alisveris Merkezi Kurulus Yeri Secimi ve Bir Uygulama," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 14(3), pages 463-479.
• Meltem KARAATLI & Serpil SENAL & Mahmut Sami OZTURK, 2014. "Suleyman Demirel Universitesi, Iktisadi ve Idari Bilimler Fakultesi, Isletme Bolumu," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 14(4), pages 637-648.
• Bozena Bobkova, 2014. "On Estimation of Gravity Equation: A Cluster Analysis," Working Papers IES 2014/37, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Dec 2014.
• Arifovic, Jasmina & Yıldızoğlu, Murat, 2019. "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 191-208.
  • Jasmina ARIFOVIC & Murat YILDIZOGLU, 2014. "Learning the Ramsey outcome in a Kydland & Prescott economy," Cahiers du GREThA (2007-2019) 2014-06, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
  • Jasmina Arifovic & Murat Yildizoglu, 2019. "Learning the Ramsey Outcome in a Kydland & Prescott Economy," Post-Print hal-03428629, HAL.
• Samir Elhedhli & Canan Akdemir & Thomas Astebro, 2014. "Classification models via Tabu search: An application to early stage venture classification," Post-Print hal-01066492, HAL.
  • Astebro , Thomas & Akdemir , Canan & Elhedhli , Samir, 2015. "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," HEC Research Papers Series 1097, HEC Paris.
  • Samir Elhedhli & Canan Akdemir & Thomas Astebro, 2015. "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," Working Papers hal-02002758, HAL.
• Selin Devrim ÖZDEMİR & Işıl AKGÜL, 2014. "Hisse Senedi Piyasalarının Kaotik Yapısı ve Yapay Sinir Ağları ile öngörüsü: IMKB-100 örneği," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 29(336), pages 31-58.
• Torsten Hothorn & Achim Zeileis, 2014. "partykit: A Modular Toolkit for Recursive Partytioning in R," Working Papers 2014-10, Faculty of Economics and Statistics, Universität Innsbruck.
• Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," AQR Working Papers 201410, University of Barcelona, Regional Quantitative Analysis Group, revised May 2014.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
• Margherita Comola & Mariapia Mendola, 2015. "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(2), pages 592-618, April.
  • Margherita Comola & Mariapia Mendola, 2013. "The Formation of Migrant Networks," Development Working Papers 353, Centro Studi Luca d'Agliano, University of Milano.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Post-Print hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint) hal-00977544, HAL.
  • Comola, Margherita & Mendola, Mariapia, 2014. "The Formation of Migrant Networks," IZA Discussion Papers 7981, Institute of Labor Economics (IZA).
• Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
  • Thomas Grubinger & Achim Zeileis & Karl-Peter Pfeiffer, 2011. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Working Papers 2011-20, Faculty of Economics and Statistics, Universität Innsbruck.
• Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 183-197, February.
• Gheorghe H. Popescu & Elvira Nica, 2014. "Neuroeconomic Models of Decision-Making," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 6(1), pages 63-66, March.
• Ömer Akgöbek & Emre Yakut, 2014. "Efficiency measurement in Turkish manufacturing sector using Data Envelopment Analysis (DEA) and Artificial Neural Networks (ANN)," Journal of Economic and Financial Studies (JEFS), LAR Center Press, vol. 2(3), pages 35-45, June.
• Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, LAR Center Press, vol. 4(11), pages 60-70, November.
• Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(11), pages 60-70, November.
• Ricardo Figueiredo Summa & Leonardo Macrini, 2014. "Os determinantes da inflação brasileira recente: estimações utilizando redes neurais [Determinants of recent Brazilian inflation: Estimates using neural networks]," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 24(2), pages 279-296, May-Augus.
• Dan Farhat, 2014. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand:," Working Papers 1404, University of Otago, Department of Economics, revised Mar 2014.
• Dan Farhat, 2014. "Information Processing, Pattern Transmission and Aggregate Consumption Patterns in New Zealand:," Working Papers 1405, University of Otago, Department of Economics, revised Mar 2014.
• Tomasz Jasinski & Agnieszka Scianowska, 2014. "Security Assessment and Optimization of Energy Supply," Working Papers 30/2014, Institute of Economic Research, revised Dec 2014.
• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Mishra, Sudhanshu K, 2014. "What happens if in the principal component analysis the Pearsonian is replaced by the Brownian coefficient of correlation?," MPRA Paper 56861, University Library of Munich, Germany.
• Mishra, SK, 2014. "A note on Poincaré recurrence in Anosov diffeomorphic transformation of discretized outline of some plant leaves," MPRA Paper 57594, University Library of Munich, Germany.
• Gawlik, Remigiusz & Gołębiowski, Kamil, 2014. "Incorporating Qualitative Indicators of Well - Being into Quantitative Economic Research," MPRA Paper 58587, University Library of Munich, Germany.
• Gawlik, Remigiusz, 2014. "Application of Artificial Intelligence Methods for Analysis of Material and Non-material Determinants of Functioning of Young Europeans in Times of Crisis in the Eurozone," MPRA Paper 62444, University Library of Munich, Germany, revised 2014.
• Alpaslan YARAR & Mustafa ONÜÇYILDIZ & Nuri PEKÇET?N, 2014. "Forecasting The Runoff Data Using Adaptive Neuro Fuzzy Inference Systems (ANFIS)," Proceedings of International Academic Conferences 0201599, International Institute of Social and Economic Sciences.
• Oswaldo García Salgado & Arturo Morales Castro, 2014. "Empresas exitosas y no exitosas que cotizan en la BMV del Sector Comercial: Una clasificación con Análisis Discriminante Múltiple, Modelos Logit y Redes Neuronales Artificiales," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 4(1), pages 33-62, enero-jun.
• Manuel Rodríguez & Carlos Piñeiro & Pablo De Llano, 2014. "Determinación del riesgo de fracaso financiero mediante la utilización de modelos paramétricos, de inteligencia artificial, y de información de auditoría," Estudios de Economia, University of Chile, Department of Economics, vol. 41(2 Year 20), pages 187-217, December.
• DIMITRIU, Mihail, 2014. "A Point Of View On The Logic Modelling Of The Financial Network," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(1), pages 8-19.
• DIMITRIU, Mihail, 2014. "Particularities Of Transfer Channel In The Financial Network Modeling," Journal of Financial and Monetary Economics, Centre of Financial and Monetary Research "Victor Slavescu", vol. 1(1), pages 239-243.

2013

• Cagdas Hakan ALADAG & Miruna MAZURENCU MARINESCU, 2013. "Tl/Euro And Leu/Euro Exchange Rates Forecasting With Artificial Neural Network," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 2(2), pages 1-6, DECEMBER.
• Romano, Severino & Cozzi, Mario & Giglio, Paolo & Catullo, Giovanna, 2013. "Post-2013 EU Common Agricultural Policy: predictive models of land use change," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 2(2), pages 1-22, August.
• Härdle, Wolfgang Karl & Prastyo, Dedy Dwi & Hafner, Christian, 2012. "Support vector machines with evolutionary feature selection for default prediction," SFB 649 Discussion Papers 2012-030, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Hardle, Wolfgang Karl & Prastyo, Dedy Dwi & Hafner, Christian, 2013. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," LIDAM Discussion Papers ISBA 2013040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
• Miklós Virag & Tamás Nyitrai, 2013. "Application of support vector machines on the basis of the first Hungarian bankruptcy model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 35(2), pages 227-248, August.
• Laura Maria BADEA (STROIE), 2013. "Supporting Management Decisions by Using Artificial Neural Networks for Exchange Rate Prediction," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 12(4), pages 578-594, December.
• Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," IREA Working Papers 201321, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," AQR Working Papers 201313, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
• Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 761, Banco de la Republica de Colombia.
• Franz Alonso Hamann Salcedo & Rafael Hernández & Luisa Fernanda Silva Escobar & Fernando Tenjo Galarza, 2013. "Credit Pro-cyclicality and Bank Balance Sheet in Colombia," Borradores de Economia 10695, Banco de la Republica.
  • Franz Alonso Hamann Salcedo & Rafael Hernández & Luisa Fernanda Silva EScobar & Fernando Tenjo Galarza, 2013. "Credit Pro-cyclicality and Bank Balance Sheet in Colombia," Borradores de Economia 762, Banco de la Republica de Colombia.
• Juan Amador & José Gómez-González & Andrés Pabón, 2013. "Loan growth and bank risk: new evidence," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(4), pages 365-379, December.
  • Juan Sebastián Amador Torres & José Eduardo Gómez G. & Andrés Murcia Pabón, 2013. "Loans Growth and Banks´ Risk: New Evidence," Borradores de Economia 10710, Banco de la Republica.
  • Juan Sebastián Amador Torres & José EDuardo Gómez G. & Andrés Murcia Pabón, 2013. "Loans Growth and Banks’ Risk: New Evidence," Borradores de Economia 763, Banco de la Republica de Colombia.
• CIOBANU Dumitru & BAR Mary Violeta, 2013. "On The Prediction Of Exchange Rate Dollar/Euro With An Svm Model," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 65(2), pages 91-109.
• Hill Jonathan B., 2013. "Stochastically weighted average conditional moment tests of functional form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 121-139, April.
• Abbruzzo, Antonino & Brida, Juan Gabriel & Scuderi, Raffaele, 2014. "Determinants of individual tourist expenditure as a network: Empirical findings from Uruguay," Tourism Management, Elsevier, vol. 43(C), pages 36-45.
  • Antonio Abbruzzo & Juan Gabriel Brida & Raffaele Scuderi, 2013. "Determinants of Individual Tourist Expenditure as a Network: Empirical Findings from Uruguay," BEMPS - Bozen Economics & Management Paper Series BEMPS09, Faculty of Economics and Management at the Free University of Bozen.
• Komsan Suriya, 2013. "Airline market segments after low cost airlines in Thailand: Passengerclassification using Neural Networks and Logit model with selective learning," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 2(4), pages 21-32, December.
• Mauricio Lopera Castano & Ramón Javier Mesa Callejas & Sergio Iván Restrepo Ochoa & Charle Augusto Londono Henao, 2013. "Modelando el esquema de intervenciones del tipo de cambio para Colombia. una aplicación empírica de la técnica de regresión del cuantil bajo redes neu," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, May.
• Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 10502, Banco de la Republica.
• Franz Alonso Hamann Salcedo & Rafael Hernández & Luisa Fernanda Silva EScobar & Fernando Tenjo Galarza, 2013. "Credit Pro-cyclicality and Bank Balance Sheet in Colombia," Borradores de Economia 762, Banco de la Republica de Colombia.
  • Franz Alonso Hamann Salcedo & Rafael Hernández & Luisa Fernanda Silva Escobar & Fernando Tenjo Galarza, 2013. "Credit Pro-cyclicality and Bank Balance Sheet in Colombia," Borradores de Economia 10695, Banco de la Republica.
• Juan Amador & José Gómez-González & Andrés Pabón, 2013. "Loan growth and bank risk: new evidence," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(4), pages 365-379, December.
  • Juan Sebastián Amador Torres & José EDuardo Gómez G. & Andrés Murcia Pabón, 2013. "Loans Growth and Banks’ Risk: New Evidence," Borradores de Economia 763, Banco de la Republica de Colombia.
  • Juan Sebastián Amador Torres & José Eduardo Gómez G. & Andrés Murcia Pabón, 2013. "Loans Growth and Banks´ Risk: New Evidence," Borradores de Economia 10710, Banco de la Republica.
• Lopera C., Mauricio & González, Favián & Augusto Londoño, Charle, 2013. "Efectos de la política monetaria sobre la valoración de activos en el mercado accionario colombiano (2004-2012)," Perfil de Coyuntura Económica, Universidad de Antioquia, CIE, issue 22, pages 179-196, July.
• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Jackson, Matthew O. & López-Pintado, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," Network Science, Cambridge University Press, vol. 1(1), pages 49-67, April.
  • Matthew O. Jackson & Dunia López Pintado, 2011. "Diffusion and contagion in networks with heterogeneous agents and homophily," Working Papers 11.14, Universidad Pablo de Olavide, Department of Economics.
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Reprints CORE 2561, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2012. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Discussion Papers CORE 2012012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Margherita Comola & Mariapia Mendola, 2015. "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(2), pages 592-618, April.
  • Margherita Comola & Mariapia Mendola, 2013. "The Formation of Migrant Networks," Development Working Papers 353, Centro Studi Luca d'Agliano, University of Milano.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," Post-Print hal-00977544, HAL.
  • Margherita Comola & M. Mendola, 2015. "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint) hal-00977544, HAL.
  • Comola, Margherita & Mendola, Mariapia, 2014. "The Formation of Migrant Networks," IZA Discussion Papers 7981, Institute of Labor Economics (IZA).
• Jackson, Matthew O. & López-Pintado, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," Network Science, Cambridge University Press, vol. 1(1), pages 49-67, April.
  • Matthew O. Jackson & Dunia López Pintado, 2011. "Diffusion and contagion in networks with heterogeneous agents and homophily," Working Papers 11.14, Universidad Pablo de Olavide, Department of Economics.
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2012. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Discussion Papers CORE 2012012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Reprints CORE 2561, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Malte Sundkötter & Daniel Ziegler, 2013. "Perfect Competition vs. Riskaverse Agents: Technology Portfolio Choice in Electricity Markets," EWL Working Papers 1303, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Apr 2013.
• Peltonen, Tuomas A., 2006. "Are emerging market currency crises predictable? A test," Working Paper Series 0571, European Central Bank.
• Majid Delavari & Nadiya Gandali Alikhani & Esmaeil Naderi, 2013. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 466-475.
  • Delavari, Majid & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," MPRA Paper 45977, University Library of Munich, Germany.
• Zhang, Mingzhu & He, Changzheng & Gu, Xin & Liatsis, Panos & Zhu, Bing, 2013. "D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy," Economic Modelling, Elsevier, vol. 30(C), pages 514-520.
• Dong, Chaohua & Gao, Jiti, 2013. "Solving replication problems in a complete market by orthogonal series expansion," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 306-317.
  • Chaohua Dong & Jiti Gao, 2012. "Solving Replication Problems in Complete Market by Orthogonal Series Expansion," Monash Econometrics and Business Statistics Working Papers 7/12, Monash University, Department of Econometrics and Business Statistics.
• Ozer Ozdemir & Memmedaga Memmedli & Akhlitdin Nizamitdinov, 2013. "ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price," International Econometric Review (IER), Econometric Research Association, vol. 5(2), pages 53-69, September.
• Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
• Akçay, Belgin, 2013. "Avro Bölgesi Borç Krizi: GIIPS," EY International Congress on Economics I (EYC2013), October 24-25, 2013, Ankara, Turkey 203, Ekonomik Yaklasim Association.
• Eren Tatari & Zeynep Teymuroglu, 2013. "A Network Analysis of Minority Representation in the British Local Government: Tower Hamlets Council (2006-2010)," European Journal of Economic and Political Studies, Fatih University, vol. 6(2), pages 27-56.
• Anders Bredahl Kock & Timo Teräsvirta, 2013. "Forecasting the Finnish Consumer Price Inflation Using Artificial Neural Network Models and Three Automated Model Selection Techniques," Finnish Economic Papers, Finnish Economic Association, vol. 26(1), pages 13-24, Spring.
• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Linda Margarita Medina Herrera & Ernesto Armando Pacheco Velazquez, 2013. "Spectral Analysis And Networks In Financial Correlation Matrices, Analisis Espectral Y Redes En Matrices De Correlacion Financiera," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 6(6), pages 15-28.
• Patricia Cortez & Paúl Medina, 2013. "Factores determinantes de la migración de los ecuatorianos," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 5(1), pages 25-35, Junio.
• Elsy Gómez-Ramos & Francisco Venegas-Martínez, 2013. "A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 6(2), pages 7-15, Diciembre.
• Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," AQR Working Papers 201313, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
  • Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," IREA Working Papers 201321, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
• Christoffer Kok & Mattia Montagna, 2013. "Multi-layered Interbank Model for Assessing Systemic Risk," Kiel Working Papers 1873, Kiel Institute for the World Economy.
• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Julien Boelaert, 2013. "A Neural Network Demand System," Documents de travail du Centre d'Economie de la Sorbonne 13081, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
• Núñez Tabales, Julia M. & Caridad y Ocerin, José María & Rey Carmona, Francisco J., 2013. "Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios," 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, vol. 15(1), pages 29-44, June.
• de Rigo, Daniele & Corti, Paolo & Caudullo, Giovanni & McInerney, Daniel & Di Leo, Margherita & San-Miguel-Ayanz, Jesús, 2013. "Toward open science at the European scale: geospatial semantic array programming for integrated environmental modelling," MPRA Paper 44194, University Library of Munich, Germany.
• de Rigo, Daniele, 2013. "Software uncertainty in integrated environmental modelling: the role of semantics and open science," MPRA Paper 45960, University Library of Munich, Germany.
  • de Rigo, Daniele, 2013. "Software uncertainty in integrated environmental modelling: the role of semantics and open science," MPRA Paper 44201, University Library of Munich, Germany.
• Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45860, University Library of Munich, Germany.
  • Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45615, University Library of Munich, Germany.
• Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45615, University Library of Munich, Germany.
  • Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45860, University Library of Munich, Germany.
• de Rigo, Daniele, 2013. "Software uncertainty in integrated environmental modelling: the role of semantics and open science," MPRA Paper 44201, University Library of Munich, Germany.
  • de Rigo, Daniele, 2013. "Software uncertainty in integrated environmental modelling: the role of semantics and open science," MPRA Paper 45960, University Library of Munich, Germany.
• Nazarian, Rafik & Gandali Alikhani, Nadiya & Naderi, Esmaeil & Amiri, Ashkan, 2013. "Forecasting Stock Market Volatility: A Forecast Combination Approach," MPRA Paper 46786, University Library of Munich, Germany.
• Gawlik, Remigiusz, 2013. "Material and Non-material Determinants of European Youth's Life Quality," MPRA Paper 48065, University Library of Munich, Germany.
• S Battiston & G di Iasio & L Infante & F Pierobon, 2015. "Capital and contagion in financial networks," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.
  • di Iasio, Giovanni & Battiston, Stefano & Infante, Luigi & Pierobon, Federico, 2013. "Capital and Contagion in Financial Networks," MPRA Paper 52141, University Library of Munich, Germany.
• Leiva-Leon, Danilo, 2013. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," MPRA Paper 54452, University Library of Munich, Germany.
  • Danilo Leiva-Leon, 2014. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," Staff Working Papers 14-38, Bank of Canada.
• Oancea, Bogdan & Dragoescu, Raluca & Ciucu, Stefan, 2013. "Predicting students’ results in higher education using a neural network," MPRA Paper 72041, University Library of Munich, Germany.
• Lin, Fengyi & Yeh, Ching Chiang & Lee, Meng Yuan, 2013. "A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 82-97, March.
• Panchenko, Valentyn & Gerasymchuk, Sergiy & Pavlov, Oleg V., 2013. "Asset price dynamics with heterogeneous beliefs and local network interactions," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2623-2642.
  • Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2013. "Asset Price Dynamics with Heterogeneous Beliefs and Local Network Interactions," Discussion Papers 2013-18, School of Economics, The University of New South Wales.
• Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
• Jakub Nowotarski & Jakub Tomczyk & Rafal Weron, 2013. "Modeling and forecasting of the long-term seasonal component of the EEX and Nord Pool spot prices," HSC Research Reports HSC/13/02, Hugo Steinhaus Center, Wroclaw University of Technology.
• Montagna, Mattia & Kok, Christoffer, 2013. "Multi-layered interbank model for assessing systemic risk," Kiel Working Papers 1873, Kiel Institute for the World Economy (IfW Kiel).
  • Kok, Christoffer & Montagna, Mattia, 2016. "Multi-layered interbank model for assessing systemic risk," Working Paper Series 1944, European Central Bank.

2012

• Adrián Fernández-Pérez & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2012. "Genetic algorithm for arbitrage with more than three currencies," Working Papers 12-04, Asociación Española de Economía y Finanzas Internacionales.
• Tamás Kristóf & Miklós Virág, 2012. "Data reduction and univariate splitting — Do they together provide better corporate bankruptcy prediction?," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 62(2), pages 205-228, June.
• Adel KARAA & Aida KRICHENE, 2012. "Credit–Risk Assessment Using Support Vectors Machine and Multilayer Neural Network Models: A Comparative Study Case of a Tunisian Bank," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 11(4), pages 587-620, December.
• Richard Kasa, 2012. "Measuring Innovation Potential at SME Level with a Neurofuzzy Hybrid Model," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
• Ferrara, L., 2012. "Prévoir le cycle économique. Synthèse du huitième séminaire de l’International Institute of Forecasters organisé par la Banque de France les 1er et 2 décembre 2011 à Paris," Bulletin de la Banque de France, Banque de France, issue 187, pages 63-69.
• STEFAN Raluca-Mariana & SERBAN Mariuta, 2012. "Neural Network Principles To Classify Economic Data," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 63(4-5), pages 223-233.
• Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal Densities," Boston College Working Papers in Economics 584, Boston College Department of Economics.
  • Giacomini, Raffaella & Haefke, Christian & White, Halbert & Gottschling, Andreas, 2002. "Hypernormal Densities," University of California at San Diego, Economics Working Paper Series qt9wr373nt, Department of Economics, UC San Diego.
  • Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal densities," Economics Working Papers 638, Department of Economics and Business, Universitat Pompeu Fabra.
• Emperatriz Londono Aldana - María Eugenia Navas Ríos, 2012. "Estrategias De Las Mypymes De Comercio: Un Análisis Basado En La Aplicación De Las Redes Neuronales Artificiales," Revista de Economía y Administración, Universidad Autónoma de Occidente, October.
• Barrientos Marín, Jorge & Rodas, Edwin & Velilla, Esteban & Lopera, Mauricio, 2012. "Modelo para el pronóstico del precio de la energía eléctrica en Colombia," Revista Lecturas de Economía, Universidad de Antioquia, CIE, October.
• Cristian Picón, 2012. "¿Son más corruptos los países menos abiertos a los mercados internacionales? Aplicación de un modelo predictivo de clasificación basado en Redes Neuro," Revista de Economía del Caribe 10274, Universidad del Norte.
• Jackson, Matthew O. & López-Pintado, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," Network Science, Cambridge University Press, vol. 1(1), pages 49-67, April.
  • Matthew O. Jackson & Dunia López Pintado, 2011. "Diffusion and contagion in networks with heterogeneous agents and homophily," Working Papers 11.14, Universidad Pablo de Olavide, Department of Economics.
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2012. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Discussion Papers CORE 2012012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Reprints CORE 2561, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2012. "Synchronization and Diversity in Business Cycles: A Network Approach Applied to the European Union," CREMA Working Paper Series 2012-01, Center for Research in Economics, Management and the Arts (CREMA).
• Raquel Carrasco & J. Ignacio García Pérez, 2012. "Economic Conditions and Employment Dynamics of Immigrants versus Natives: Who Pays the Costs of the “Great Recession”?," Working Papers 12.13, Universidad Pablo de Olavide, Department of Economics.
  • García-Pérez, J. Ignacio, 2012. "Economic Conditions and Employment Dynamics of Immigrants versus Natives: Who Pays the Costs of the "Great Recession"?," UC3M Working papers. Economics we1232, Universidad Carlos III de Madrid. Departamento de Economía.
• Mioara CHIRITA, 2012. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-66.
• Thomas Fellmann & Myrna van Leeuwen & Petra Salamon & Ali Koc & Gulden Boluk, 2012. "EU Enlargement to Turkey: Potential Effects on Turkey’s Agricultural Income and Markets," Eurasian Economic Review, Eurasia Business and Economics Society, vol. 2(2), pages 1-16, Fall.
• Eleftherios Giovanis, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," Economic Analysis and Policy, Elsevier, vol. 42(1), pages 79-96, March.
  • Giovanis, Eleftherios, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," MPRA Paper 71218, University Library of Munich, Germany.
• Bax, Eric & Kuratti, Anand & Mcafee, Preston & Romero, Julian, 2012. "Comparing predicted prices in auctions for online advertising," International Journal of Industrial Organization, Elsevier, vol. 30(1), pages 80-88.
• Abdou, Hussein A. & Pointon, John & El-Masry, Ahmed & Olugbode, Moji & Lister, Roger J., 2012. "A variable impact neural network analysis of dividend policies and share prices of transportation and related companies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 796-813.
• Nuray GUNERI TOSUNOGLU & Yasemin KESKIN BENLI, 2012. "Morgan Stanley Capital International Turkiye Endeksinin Yapay Sinir Aglari ile Ongorusu," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 12(4), pages 541-547.
• Belgin AKÇAY, 2012. "Türkiye’de Cari Açığın Sürdürülebilirliği: Borç Krizindeki Yunanistan İle Bir Karşılaştırma," Ekonomik Yaklasim, Ekonomik Yaklasim Association, vol. 23(84), pages 1-38.
• Belgin AKÇAY, 2012. "Borç Krizindeki GIIPS," Ekonomik Yaklasim, Ekonomik Yaklasim Association, vol. 23(85), pages 23-56.
• Sorrosal Forradellas, M. T. & Barberà-Mariné, M. G. & Fernández Bariviera, Aurelio & Garbajosa-Cabello, M. J., 2012. "Advantages Of Using Self-Organizing Maps To Analyse Student Evaluations Of Teaching," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 53-71, May.
• Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Hardle, Wolfgang Karl & Prastyo, Dedy Dwi & Hafner, Christian, 2013. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," LIDAM Discussion Papers ISBA 2013040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
• Linda Margarita Medina Herrera & Ernesto Armando Pacheco Velázquez, 2012. "Distances And Networks: The Case Of Mexico," Accounting & Taxation, The Institute for Business and Finance Research, vol. 4(2), pages 39-48.
• Julia M. Nunez Tabales & Jose Mª Caridad y Ocerin & Nuria Ceular Villamandos & Francisco Jose Rey Carmona, 2012. "Implicit Prices Associated To The Main Causal Attributes In Real Estate Valuation, Obtencion De Precios Implicitos Para Atributos Determinantes En La Valoracion De Una Vivienda," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 5(3), pages 41-54.
• William A. Barnett & A. Ronald Gallant & Melvin J. Hinich & Jochen A. Jungeilges & Daniel T. Kaplan, 2004. "A Single-Blind Controlled Competition Among Tests for Nonlinearity and Chaos," Contributions to Economic Analysis, in: Functional Structure and Approximation in Econometrics, pages 581-615, Emerald Group Publishing Limited.
  • Barnett, William A. & Gallant, A. Ronald & Hinich, Melvin J. & Jungeilges, Jochen A. & Kaplan, Daniel T. & Jensen, Mark J., 1997. "A single-blind controlled competition among tests for nonlinearity and chaos," Journal of Econometrics, Elsevier, vol. 82(1), pages 157-192.
  • William A. Barnett & A. Ronald Gallant & Melvin J. Hinich & Jochen A. Jungeilges & Daniel T. Kaplan & Mark J. Jensen, 1996. "A Single-Blind Controlled Competition among Tests for Nonlinearity and Chaos," Econometrics 9602005, University Library of Munich, Germany, revised 29 Jan 1997.
  • William Barnett & A. Ronald Gallant & Melvin J. Hinich & Jochen A. Jungeilges & Daniel T. Kaplan & Mark J. Jensen, 2012. "A Single-Blind Controlled Competition Among Tests For Nonlinearity And Chaos," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201219, University of Kansas, Department of Economics, revised Sep 2012.
• Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
• Apostolos Serletis & Anastasios Malliaris & Melvin Hinich & Periklis Gogas, 2012. "Episodic Nonlinearity in Leading Global Currencies," Open Economies Review, Springer, vol. 23(2), pages 337-357, April.
  • Serletis, Apostolos & Malliaris, Anastasios & Hinich, Melvin & Gogas, Periklis, 2010. "Episodic Nonlinearity in Leading Global Currencies," DUTH Research Papers in Economics 3-2010, Democritus University of Thrace, Department of Economics.
• David Bessler & Zijun Wang, 2012. "D-separation, forecasting, and economic science: a conjecture," Theory and Decision, Springer, vol. 73(2), pages 295-314, August.
• Jorge Barrientos & Edwin Rodas & Esteban Velilla & Mauricio Lopera & Fernando Villada, 2012. "A model for forecasting electricity prices in Colombia," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 77, pages 91-127.
• Dong, Chaohua & Gao, Jiti, 2013. "Solving replication problems in a complete market by orthogonal series expansion," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 306-317.
  • Chaohua Dong & Jiti Gao, 2012. "Solving Replication Problems in Complete Market by Orthogonal Series Expansion," Monash Econometrics and Business Statistics Working Papers 7/12, Monash University, Department of Econometrics and Business Statistics.
• Alessandro Giovannelli, 2012. "Nonlinear Forecasting Using a Large Number of Predictors," Rivista italiana degli economisti, Società editrice il Mulino, issue 1, pages 143-150.
• Montero-Romero, Teresa & López-Martín, María del Carmen & Becerra-Alonso, David & Martínez-Estudillo, Francisco José, 2012. "Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions || Análisis de la morosidad de las entidades financieras españolas mediante Extreme Learning Machine," 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, vol. 13(1), pages 3-23, June.
• Kaizoji, Taisei, 2012. "A Note on Stability of Self-Consistent Equilibrium in an Asynchronous Model of Discrete-Choice with Social Interaction," MPRA Paper 38730, University Library of Munich, Germany.
• Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
• Djennas, Meriem & Benbouziane, Mohamed & Djennas, Mustapha, 2012. "Agent-Based Modeling in Supply Chain Management:A Genetic Algorithm and Fuzzy Logic Approach," MPRA Paper 41782, University Library of Munich, Germany.
• Matkovskyy, Roman, 2012. "Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks," MPRA Paper 42153, University Library of Munich, Germany.
• Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
  • Jakub Nowotarski & Jakub Tomczyk & Rafal Weron, 2012. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," HSC Research Reports HSC/12/06, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
  • Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafal, 2012. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," MPRA Paper 42563, University Library of Munich, Germany.
• du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
• Majid Delavari & Nadiya Gandali Alikhani & Esmaeil Naderi, 2013. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 466-475.
  • Delavari, Majid & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," MPRA Paper 45977, University Library of Munich, Germany.
• Abounoori, Abbas Ali & Mohammadali, Hanieh & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Comparative study of static and dynamic neural network models for nonlinear time series forecasting," MPRA Paper 46466, University Library of Munich, Germany.
• Eleftherios Giovanis, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," Economic Analysis and Policy, Elsevier, vol. 42(1), pages 79-96, March.
  • Giovanis, Eleftherios, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," MPRA Paper 71218, University Library of Munich, Germany.
• David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2012. "Synchronization and Diversity in Business Cycles: A Network Approach Applied to the European Union," CREMA Working Paper Series 2012-01, Center for Research in Economics, Management and the Arts (CREMA).
  • David Matesanz Gomez & Guillermo J Ortega & Benno Torgler, 2012. "Synchronization and Diversity in Business Cycles: A Network Approach Applied to the European Union," School of Economics and Finance Discussion Papers and Working Papers Series 277, School of Economics and Finance, Queensland University of Technology.
• Ozkan, Filiz, 2012. "A Comparison of the Monetary Model and Artificial Neural Networks in Exchange Rate Forecasting," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 3(1), pages 1-27, January.
• Kozo Mayumi & Mario Giampietro & Jesus Ramos-Martin, 2012. "Reconsideration of Dimensions and Curve Fitting Practice in View of Georgescu-Roegen’s Epistemology in Economics," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 17-35, December.
• Vadim DUMITRASCU, 2012. "Positive Feedbacks, Diffusion Phenomenon and Competition between Standards on the Knowledge Markets," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 60(4), pages 73-79, November.
• Alessandro Giovannelli, 2012. "Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies," CEIS Research Paper 255, Tor Vergata University, CEIS, revised 08 Nov 2012.
• Ortíz Arango, Francsco & Cabrera Llanos, Agustín I. & Cruz Aranda, Fernando, 2012. "Modelado del comportamiento del tipo de cambio peso-dólar mediante redes neuronales diferenciales / Peso-Dollar Exchange Rate Behavior Modelling by means of Differential Neural Networks," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 2(1), pages 49-64, enero-jun.
• Thomas Fellmann & Myrna Leeuwen & Petra Salamon & Ali Koc & Gulden Boluk, 2012. "EU Enlargement to Turkey: Potential Effects on Turkey’s Agricultural Income and Markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 2(2), pages 1-16, December.
• Efstathios KIRKOS, 2012. "Predicting Auditor Switches By Applying Data Mining," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 7(3(21)/ Fa), pages 246-261.
• Nijkamp Peter, 2012. "Behaviour of Humans and Behaviour of Models in Dynamic Space," Quaestiones Geographicae, Sciendo, vol. 31(2), pages 7-19, June.
  • Peter Nijkamp, 2011. "Behaviour of Humans and Behaviour of Models in Dynamic Space," Tinbergen Institute Discussion Papers 11-105/3, Tinbergen Institute.
• Andrew Sheng & Kian Teng Kwek & Cho Wai Cho, 2012. "Patterns Of Exchange Rates And Current Accounts: The East Asian Waltz," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 57(02), pages 1-34.
• Andrew Sheng & Kian Teng Kwek & Cho Wai Cho, 2012. "Patterns Of Exchange Rates And Current Accounts: The East Asian Waltz," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 57(02), pages 1-34.
• Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
  • Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafal, 2012. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," MPRA Paper 42563, University Library of Munich, Germany.
  • Jakub Nowotarski & Jakub Tomczyk & Rafal Weron, 2012. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," HSC Research Reports HSC/12/06, Hugo Steinhaus Center, Wroclaw University of Technology.
• Härdle, Wolfgang Karl & Prastyo, Dedy Dwi & Hafner, Christian, 2012. "Support vector machines with evolutionary feature selection for default prediction," SFB 649 Discussion Papers 2012-030, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Hardle, Wolfgang Karl & Prastyo, Dedy Dwi & Hafner, Christian, 2013. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," LIDAM Discussion Papers ISBA 2013040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

2011

• Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
  • Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques," CREATES Research Papers 2011-27, Department of Economics and Business Economics, Aarhus University.
• Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
  • Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009," CREATES Research Papers 2011-28, Department of Economics and Business Economics, Aarhus University.
• Michele Bagella & Francesco Busato, 2011. "Defining Green Finance and Green intermediaries," BANCARIA, Bancaria Editrice, vol. 10, pages 14-24, October.
• Charle Augusto Llondono, 2011. "Regresión del cuantil aplicada al modelo de redes neuronales artificiales. Una aproximación de la estructura CAVIAR para el mercado de valores colombiano," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 29(64), pages 62-109, July.
  • Charle Augusto Londoño, 2011. "Regresión del cuantil aplicada al modelo de redes neuronales artificiales," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(64), pages 62-109, July.
• L. Ferrara., 2011. "Forecasting the business cycle. Summary of the 8th International Institute of Forecasters workshop hosted by the Banque de France on 1-2 December 2011 in Paris," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 24, pages 135-144, Winter.
• Panayotis G. Michaelides & Angelos T. Vouldis & Efthymios G. Tsionas, 2011. "Returns to scale, productivity and efficiency in US banking (1989-2000): the neural distance function revisited," Working Papers 126, Bank of Greece.
• Kock Anders Bredahl, 2011. "Forecasting with Universal Approximators and a Learning Algorithm," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
  • Anders Bredahl Kock, 2009. "Forecasting with Universal Approximators and a Learning Algorithm," CREATES Research Papers 2009-18, Department of Economics and Business Economics, Aarhus University.
• Charle Augusto Londoño, 2011. "Regresión del cuantil aplicada al modelo de redes neuronales artificiales," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(64), pages 62-109, July.
  • Charle Augusto Llondono, 2011. "Regresión del cuantil aplicada al modelo de redes neuronales artificiales. Una aproximación de la estructura CAVIAR para el mercado de valores colombiano," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 29(64), pages 62-109, July.
• Londono Henao, Charle Augusto & Cuan Jaramillo, Yaneth María, 2011. "Modelos de precios de los activos: un ejercicio comparativo basado en redes neuronales aplicado al mercado de valores colombiano," Revista Lecturas de Economía, Universidad de Antioquia, CIE, November.
• David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2011. "Measuring globalization: A hierarchical network approach," CREMA Working Paper Series 2011-11, Center for Research in Economics, Management and the Arts (CREMA).
• David Matesanz & Benno Torgler & Germán Dabat & Guillermo J. Ortega, 2014. "Co-movements in commodity prices: a note based on network analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 45(S1), pages 13-21, November.
  • David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler & German Dabat, 2011. "Co-movements in commodity prices: A note based on network analysis," CREMA Working Paper Series 2011-21, Center for Research in Economics, Management and the Arts (CREMA).
• Greta Falavigna, 2011. "An artificial neural network approach for assigning rating judgements to Italian Small Firms," CERIS Working Paper 201104, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
• Hamid Abrishami & Vida Varahrami, 2011. "Different methods for gas price forecasting," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(96), pages 137-144, Diciembre.
• Jiri Krtek & Miloslav Vošvrda, 2011. "Comparing Neural Networks and ARMA Models in Artificial Stock Market," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 18(28).
• Linda Margarita Medina Herrera & Ernesto Pacheco Velázquez, 2011. "Comparando distancias en los mercados financieros mundiales," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 6(2), pages 88-98.
• Mustapha Djennas & Mohamed Benbouziane & Meriem Djennas, 2011. "An Approach of Combining Empirical Mode Decomposition and Neural Network Learning for Currency Crisis Forecasting," Working Papers 627, Economic Research Forum, revised 09 Jan 2011.
• Timotej Jagric & Vita Jagric & Davorin Kracun, 2011. "Does Non-linearity Matter in Retail Credit Risk Modeling?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(4), pages 384-402, August.
• Maciel, Leandro S., 2011. "Pricing Brazilian exchange rate options using an adaptive network-based fuzzy inference system," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 59-73, November.
• Russ Moro & Wolfgang Härdle & Saeideh Aliakbari & Linda Hoffmann, 2011. "Forecasting Corporate Distress in the Asian and Pacific Region," SFB 649 Discussion Papers SFB649DP2011-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Aracely Madrid & Adrian Chaparro & Raime Bustos & Antonio Rios, 2011. "Designing A Strategic Planning Tool Applying Artificial Neural Network Theory, Diseno De Una Herramienta De Planificacion Estrategica Aplicando Teoria De Redes Neuronales Artificiales," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 4(4), pages 31-44.
• Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
  • Thomas Grubinger & Achim Zeileis & Karl-Peter Pfeiffer, 2011. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Working Papers 2011-20, Faculty of Economics and Statistics, Universität Innsbruck.
• Gómez-Ramos, Elsy L. & Venegas-Martínez, Francisco & Allier-Campuzano, Héctor, 2011. "Análisis comparativo entre modelos GARCH y redes neuronales en el pronóstico de los índices bursatiles IPC y Dow Jones," eseconomía, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(32), pages 3-22, cuarto tr.
• Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
• Charle Londoño & Yaneth Cuan, 2011. "Asset Pricing Models: A Comparative Exercise Using Neural Networks to the Colombian Stock Market," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 75, pages 59-87.
• Michał Rubaszek & Paweł Skrzypczyński & Grzegorz Koloch, 2010. "Forecasting the Polish Zloty with Non-Linear Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(2), pages 151-167, March.
  • Michal Rubaszek & Pawel Skrzypczynski & Grzegorz Koloch, 2011. "Forecasting the Polish zloty with non-linear models," NBP Working Papers 81, Narodowy Bank Polski.
• Jackson, Matthew O. & López-Pintado, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," Network Science, Cambridge University Press, vol. 1(1), pages 49-67, April.
  • Matthew O. Jackson & Dunia López Pintado, 2011. "Diffusion and contagion in networks with heterogeneous agents and homophily," Working Papers 11.14, Universidad Pablo de Olavide, Department of Economics.
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2012. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Discussion Papers CORE 2012012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • JACKSON, Matthew O. & LOPEZ-PINTADO, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," LIDAM Reprints CORE 2561, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Erick Lahura & Marco Vega, 2011. "Evaluation of Wavelet-based Core Inflation Measures: Evidence from Peru," Documentos de Trabajo / Working Papers 2011-320, Departamento de Economía - Pontificia Universidad Católica del Perú.
• Nguefack-Tsague, Georges & Zucchini, Walter, 2011. "Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach," MPRA Paper 28232, University Library of Munich, Germany.
• Filippou, Miltiades & Zervopoulos, Panagiotis, 2011. "Developing a short-term comparative optimization forecasting model for operational units’ strategic planning," MPRA Paper 30766, University Library of Munich, Germany.
• Pugalendhi, Subburethina Bharathi & Nakkeeran, Senthil kumar, 2011. "Mind mapping management," MPRA Paper 33366, University Library of Munich, Germany, revised 12 Aug 2011.
• Malliaris, A.G. & Malliaris, Mary, 2011. "Are foreign currency markets interdependent? evidence from data mining technologies," MPRA Paper 35261, University Library of Munich, Germany.
• Su, EnDer & Fen, Yu-Gin, 2011. "Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market," MPRA Paper 35474, University Library of Munich, Germany.
• Filippou, Miltiades & Zervopoulos, Panagiotis, 2011. "Developing a hybrid comparative optimization model for short-term forecasting: an ‘idle time interval’ roadmap for operational units’ strategic planning," MPRA Paper 41573, University Library of Munich, Germany.
• Jiří Trešl, 2011. "Srovnání vybraných metod predikce změn trendu indexu PX [Selected Methods of the Prediction of PX Index Trend Reversal]," Politická ekonomie, Prague University of Economics and Business, vol. 2011(2), pages 184-204.
• David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2011. "Measuring globalization: A hierarchical network approach," CREMA Working Paper Series 2011-11, Center for Research in Economics, Management and the Arts (CREMA).
  • David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2011. "Measuring Globalization: A hierarchical network approach," School of Economics and Finance Discussion Papers and Working Papers Series 267, School of Economics and Finance, Queensland University of Technology.
• David Matesanz & Benno Torgler & Germán Dabat & Guillermo J. Ortega, 2014. "Co-movements in commodity prices: a note based on network analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 45(S1), pages 13-21, November.
  • David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler & German Dabat, 2011. "Co-movements in commodity prices: A note based on network analysis," CREMA Working Paper Series 2011-21, Center for Research in Economics, Management and the Arts (CREMA).
  • David M Gomez & Guillermo J Ortega & Benno Torgler & German Debat, 2011. "Co-movements in commodity prices: a note based on network analysis," School of Economics and Finance Discussion Papers and Working Papers Series 274, School of Economics and Finance, Queensland University of Technology.
• Lahura, Erick & Vega, Marco, 2011. "Wavelet-based Core Inflation Measures: Evidence from Peru," Working Papers 2011-019, Banco Central de Reserva del Perú.
• Nikola Gradojevic & Dragan Kukolj & Ramazan Gencay, 2011. "Clustering and Classification in Option Pricing," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 3(2), pages 109-128, October.
• Sanjinés, Gimmy Nardó, 2011. "Análisis y pronóstico de la demanda de potencia eléctrica en Bolivia: una aplicación de redes neuronales," Revista Latinoamericana de Desarrollo Economico, Carrera de Economía de la Universidad Católica Boliviana (UCB) "San Pablo", issue 15, pages 45-77, Mayo.
• Sanjines, Gimmy Nardó, 2011. "Amenazas ambientales y vulnerabilidad en un contexto de variabilidad climática en Bolivia," Revista Latinoamericana de Desarrollo Economico, Carrera de Economía de la Universidad Católica Boliviana (UCB) "San Pablo", issue 16, pages 81-130, Noviembre.
• Shih, Kuang Hsun & Cheng, Ching Chan & Wang, Yi Hsien, 2011. "Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-71, March.
• Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
• Tonatiuh Peña & Serafín Martínez & Bolanle Abudu, 2011. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 109-131, Springer.
  • Peña Tonatiuh & Martínez Serafín & Abudu Bolanle, 2009. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Working Papers 2009-18, Banco de México.
• Mustapha DJENNAS & Mohamed BENBOUZIANE & Meriem DJENNAS, 2011. "An Approach Of Combining Empirical Mode Decomposition And Neural Network Learning For Currency Crisis Forecasting," Journal of Applied Research in Finance Bi-Annually, ASERS Publishing, vol. 0(2), pages 170-184, December.
  • Mustapha Djennas & Mohamed Benbouziane & Meriem Djennas, 2011. "An Approach of Combining Empirical Mode Decomposition and Neural Network Learning for Currency Crisis Forecasting," Working Papers 627, Economic Research Forum, revised 09 Jan 2011.
• Andreas S. Andreou & George A. Zombanakis, 2011. "Financial Versus Human Resources In The Greek--Turkish Arms Race 10 Years On: A Forecasting Investigation Using Artificial Neural Networks," Defence and Peace Economics, Taylor & Francis Journals, vol. 22(4), pages 459-469, November.
  • Zombanakis, George A. & Andreou, Andreas A., 2010. "Financial versus human Resources in the Greek - Turkish Arms Race 10 Years on: A forecasting Investigation using Artificial Neural Networks," MPRA Paper 38408, University Library of Munich, Germany, revised 08 Nov 2010.
• A. Nazif Çatik & Mehmet Karaçuka, 2011. "A comparative analysis of alternative univariate time series models in forecasting Turkish inflation," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(2), pages 275-293, April.
  • Catik, A. Nazif & Karaçuka, Mehmet, 2011. "A comparative analysis of alternative univariate time series models in forecasting Turkish inflation," DICE Discussion Papers 20, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
• Nijkamp Peter, 2012. "Behaviour of Humans and Behaviour of Models in Dynamic Space," Quaestiones Geographicae, Sciendo, vol. 31(2), pages 7-19, June.
  • Peter Nijkamp, 2011. "Behaviour of Humans and Behaviour of Models in Dynamic Space," Tinbergen Institute Discussion Papers 11-105/3, Tinbergen Institute.
• Alberto José Hurtado Briceño & Jaime Tinto Arandes & Sadcidi Zerpa, 2011. "Measuring the quality of life in Merida by means of fuzzy logic," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 36(32), pages 67-94, july-dece.
• Sylvain Amisse & Paul Muller & Caroline Hussler & Patrick Rondé, 2011. "Do birds of a feather flock together? Proximities and inter-clusters network," ERSA conference papers ersa11p1896, European Regional Science Association.
• Witte, Björn-Christopher, 2011. "Removing systematic patterns in returns in a financial market model by artificially intelligent traders," BERG Working Paper Series 82, Bamberg University, Bamberg Economic Research Group.
• A. Nazif Çatik & Mehmet Karaçuka, 2011. "A comparative analysis of alternative univariate time series models in forecasting Turkish inflation," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(2), pages 275-293, April.
  • Catik, A. Nazif & Karaçuka, Mehmet, 2011. "A comparative analysis of alternative univariate time series models in forecasting Turkish inflation," DICE Discussion Papers 20, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
• Moro, Russ & Härdle, Wolfgang Karl & Aliakbari, Saeideh & Hoffmann, Linda, 2011. "Forecasting corporate distress in the Asian and Pacific region," SFB 649 Discussion Papers 2011-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

2010

• Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
• Wanfeng YAN & Ryan WOODARD & Didier SORNETTE, 2010. "Diagnosis and Prediction of Market Rebounds in Financial Markets," Swiss Finance Institute Research Paper Series 10-15, Swiss Finance Institute.
  • Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Diagnosis and Prediction of Market Rebounds in Financial Markets," Papers 1003.5926, arXiv.org, revised Mar 2011.
• Stanimir Kabaivanov, 2010. "Preventive Detection of Economic Problems by means of Neuron Networks," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 103-115.
• Huseyin Ince & Bora Aktan, 2010. "A Comparative Analysis of Individual and Ensemble Credit Scoring Techniques in Evaluating Credit Card Loan Applications," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 4(1), pages 74-90.
• Chia-Lin Chang & Michael McAleer & Les Oxley, 2011. "What makes a great journal great in the sciences? Which came first, the chicken or the egg?," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(1), pages 17-40, April.
  • Chia-Lin Chang & Michael McAleer & Les Oxley, 2010. "What Makes a Great Journal Great in the Sciences? Which Came First, the Chicken or the Egg?," KIER Working Papers 746, Kyoto University, Institute of Economic Research.
  • Chia-Lin Chang & Philip Hans Franses & Michael McAleer & Les Oxley, 2010. "What Makes a Great Journal Great in the Sciences? Which Came First, the Chicken or the Egg?," Working Papers in Economics 10/75, University of Canterbury, Department of Economics and Finance.
  • Chang, C-L. & McAleer, M.J. & Oxley, L., 2010. "What Makes a Great Journal Great in the Sciences? Which Came First, the Chicken or the Egg?," Econometric Institute Research Papers EI 2010-75, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Joao A. Bastos, 2010. "Predicting bank loan recovery rates with neural networks," CEMAPRE Working Papers 1003, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
• Charle Londono & Mauricio Lopera & Sergio Restrepo, 2010. "Teoría de precios de arbitraje. Evidencia empírica para Colombia a través de redes neuronales," Revista de Economía del Rosario, Universidad del Rosario, May.
• Jaime Enrique Arrieta Bechara & Juan Camilo Torres Cruz & Hermilson Velásquez Ceballos, 2010. "Predicciones de modelos econométricos y redes neuronales: el caso de la acción de SURAMINV," Revista Semestre Económico, Universidad de Medellín, September.
• Alonso, Pablo J., 2010. "Non-linear models of disability and age applied to census data," DES - Working Papers. Statistics and Econometrics. WS ws102410, Universidad Carlos III de Madrid. Departamento de Estadística.
• Vasile MAZILESCU, 2010. "Incorporating the Basic Elements of a First-degree Fuzzy Logic and Certain Elments of Temporal Logic for Dynamic Management Applications," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 43-52.
• Zhang, Junni L. & Härdle, Wolfgang K., 2010. "The Bayesian Additive Classification Tree applied to credit risk modelling," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1197-1205, May.
• Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
  • Yuliya Demyanyk & Iftekhar Hasan, 2009. "Financial crises and bank failures: a review of prediction methods," Working Papers (Old Series) 0904, Federal Reserve Bank of Cleveland.
• Wolfgang Karl Härdle & Rouslan Moro & Linda Hoffmann, 2010. "Learning Machines Supporting Bankruptcy Prediction," SFB 649 Discussion Papers SFB649DP2010-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Birol Yildiz & Ari Yezegel, 2010. "Fundamental Analysis With Artificial Neural Network," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(1), pages 149-158.
• Jörg Uffen & Prof. Dr. Michael H. Breitner, 2009. "Stärkung des IT-Sicherheitsbewusstseins unter Berücksichtigung psychologischer und pädagogischer Merkmale," IWI Discussion Paper Series 36, Institut für Wirtschaftsinformatik, Universität Hannover.
• Aysu İNSEL & M. Nedim SUALP & Mesut KARAKAŞ, 2010. "A comparative analysis of the ARMA and Neural Network Models: A case of Turkish economy," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 25(290), pages 35-64.
• Chih Ming Tan, 2010. "No one true path: uncovering the interplay between geography, institutions, and fractionalization in economic development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1100-1127, November/.
  • Chih Ming Tan, 2005. "No One True Path: Uncovering the Interplay between Geography, Institutions, and Fractionalization in Economic Development," Discussion Papers Series, Department of Economics, Tufts University 0512, Department of Economics, Tufts University.
• Dev, Pritha, 2010. "Identity and Fragmentation in Networks," MPRA Paper 21632, University Library of Munich, Germany.
• Chan, Tze-Haw & Lye, Chun Teck & Hooy, Chee-Wooi, 2010. "Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?," MPRA Paper 26326, University Library of Munich, Germany.
• Saltoglu, Burak & Yenilmez, Taylan, 2010. "Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash," MPRA Paper 26684, University Library of Munich, Germany.
• Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
• Elena B. Pokryshevskaya & Evgeny A. Antipov, 2011. "Applying a CART-based approach for the diagnostics of mass appraisal models," Economics Bulletin, AccessEcon, vol. 31(3), pages 2521-2528.
  • Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Applying a CART-based approach for the diagnostics of mass appraisal models," MPRA Paper 27646, University Library of Munich, Germany.
• Andreas S. Andreou & George A. Zombanakis, 2011. "Financial Versus Human Resources In The Greek--Turkish Arms Race 10 Years On: A Forecasting Investigation Using Artificial Neural Networks," Defence and Peace Economics, Taylor & Francis Journals, vol. 22(4), pages 459-469, November.
  • Zombanakis, George A. & Andreou, Andreas A., 2010. "Financial versus human Resources in the Greek - Turkish Arms Race 10 Years on: A forecasting Investigation using Artificial Neural Networks," MPRA Paper 38408, University Library of Munich, Germany, revised 08 Nov 2010.
• Andreou, Andreas S. & Zombanakis, George A., 2010. "Financial vs human resources in the Greek-Turkish arms race 10 years on," MPRA Paper 38505, University Library of Munich, Germany.
• du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
• du Jardin, Philippe & Séverin, Eric, 2010. "Dynamic analysis of the business failure process: A study of bankruptcy trajectories," MPRA Paper 44379, University Library of Munich, Germany.
• Michał Rubaszek & Paweł Skrzypczyński & Grzegorz Koloch, 2010. "Forecasting the Polish Zloty with Non-Linear Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(2), pages 151-167, March.
  • Michal Rubaszek & Pawel Skrzypczynski & Grzegorz Koloch, 2011. "Forecasting the Polish zloty with non-linear models," NBP Working Papers 81, Narodowy Bank Polski.
• Barrera, Carlos R., 2010. "Redes neuronales para predecir el tipo de cambio diario," Working Papers 2010-001, Banco Central de Reserva del Perú.
• Maria Plotnikova & Chokri Dridi, 2010. "A Cellular Automata Simulation of the 1990s Russian Housing Privatization Decision," Economics Discussion Papers em-dp2010-05, Department of Economics, University of Reading.
• Apostolos Serletis & Anastasios Malliaris & Melvin Hinich & Periklis Gogas, 2012. "Episodic Nonlinearity in Leading Global Currencies," Open Economies Review, Springer, vol. 23(2), pages 337-357, April.
  • Serletis, Apostolos & Malliaris, Anastasios & Hinich, Melvin & Gogas, Periklis, 2010. "Episodic Nonlinearity in Leading Global Currencies," DUTH Research Papers in Economics 3-2010, Democritus University of Thrace, Department of Economics.
• Song, Wonho, 2010. "Building an Early Warning System for Crude Oil Price Using Neural Network," East Asian Economic Review, Korea Institute for International Economic Policy, vol. 14(2), pages 79-109, December.
• Manish Kumar, 2010. "Modelling Exchange Rate Returns Using Non-linear Models," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(1), pages 101-125, January.
• Mathias Staudigl, 2010. "On a General class of stochastic co-evolutionary dynamics," Vienna Economics Papers 1001, University of Vienna, Department of Economics.
• Mathias Staudigl, 2010. "On a General class of stochastic co-evolutionary dynamics," Vienna Economics Papers vie1001, University of Vienna, Department of Economics.
• Härdle, Wolfgang Karl & Moro, Rouslan A. & Hoffmann, Linda, 2010. "Learning machines supporting bankruptcy prediction," SFB 649 Discussion Papers 2010-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

2009

• Kock Anders Bredahl, 2011. "Forecasting with Universal Approximators and a Learning Algorithm," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
  • Anders Bredahl Kock, 2009. "Forecasting with Universal Approximators and a Learning Algorithm," CREATES Research Papers 2009-18, Department of Economics and Business Economics, Aarhus University.
• Domagoj Sajter, 2009. "The Survey Of Certain Methods And Business Difficulties Or Bankruptcy Prediction Researches," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 18(2), pages 429-452, december.
• Tonatiuh Peña & Serafín Martínez & Bolanle Abudu, 2011. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 109-131, Springer.
  • Peña Tonatiuh & Martínez Serafín & Abudu Bolanle, 2009. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Working Papers 2009-18, Banco de México.
• Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
  • Yuliya Demyanyk & Iftekhar Hasan, 2009. "Financial crises and bank failures: a review of prediction methods," Working Papers (Old Series) 0904, Federal Reserve Bank of Cleveland.
  • Demyanyk, Yuliya & Hasan, Iftekhar, 2009. "Financial crises and bank failures : a review of prediction methods," Research Discussion Papers 35/2009, Bank of Finland.
• George A. Zombanakis & Constantinos Stylianou & Andreas S. Andreou, 2009. "The Greek Current Account Deficit:Is it Sustainable after all?," Working Papers 98, Bank of Greece.
• Bruno Ferreira Frascaroli & Luciano da Costa Silva & Osvaldo Cândido da Silva Filho, 2009. "Ratings of Sovereign Risk and the Macroeconomics Fundamentals of the countries: a Study Using Artificial Neural Networks," Brazilian Review of Finance, Brazilian Society of Finance, vol. 7(1), pages 73-106.
• Jaime Villamil, 2009. "Aproximación no lineal al modelo de overshooting usando redes neuronales multicapa para el tipo de cambio dólar-peso," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, June.
• José Mauricio Salazar Sáenz, 2009. "Evaluación de pronóstico de una red neuronal sobre el PIB en Colombia," Borradores de Economia 5934, Banco de la Republica.
• Juan David Velásquez & Carlos Jaime Franco & Hernán Alonso García, 2009. "Un Modelo No Lineal Para La Predicción De La Demanda Mensual De Electricidad En Colombia," Estudios Gerenciales, Universidad Icesi, September.
• Vasile MAZILESCU & Cornelia NOVAC-UDUDEC & Daniela SARPE & Mihaela NECULITA, 2009. "New Research Perspectives in the Emerging Field of Computational Intelligence to Economic Modeling," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 127-138.
• Kiani, K.M., 2009. "Neural Networks to Detect Nonlinearities in Time Series: Analysis of Business Cycle in France and the United Kingdom," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1).
• Coca Carasila, Andrés Milton & Villagómez Méndez, Juan, 2009. "La demanda de telefonía fija y móvil: Una aplicación de redes neuronales artificiales," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).
• Bart Baesens, 2009. "Data Mining. New Trends, Applications and Challenges," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(1), pages 46-61.
• Michaela Vlasáková Baruníková, 2009. "Option Pricing: The empirical tests of the Black-Scholes pricing formula and the feed-forward networks," Working Papers IES 2009/16, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2009.
• Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
  • Yuliya Demyanyk & Iftekhar Hasan, 2009. "Financial crises and bank failures: a review of prediction methods," Working Papers (Old Series) 0904, Federal Reserve Bank of Cleveland.
• Alexandre Souza & Gabriel Porcile, 2009. "Aplicação da lógica fuzzy em processos de decisão econômica," Working Papers 0084, Universidade Federal do Paraná, Department of Economics.
• Kayhan Koleyni, 2009. "Using Artificial Neural Networks For Income Convergence," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 3(2), pages 141-152.
• Khurshid M. Kiani, 2009. "Asymmetries in Macroeconomic Time Series in Eleven Asian Economies," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 8(1), pages 37-54, April.
• Cem Kadilar & Muammer Simsek & Cagdas Hakan Aladag, 2009. "Forecasting The Exchange Rate Series With Ann: The Case Of Turkey," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 9(1), pages 17-29, May.
• Marc Ryser & Stefan Denzler, 2009. "Selecting credit rating models: a cross-validation-based comparison of discriminatory power," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(2), pages 187-203, June.
• Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
• Mishra, SK, 2009. "The most representative composite rank ordering of multi-attribute objects by the particle swarm optimization," MPRA Paper 12723, University Library of Munich, Germany.
• Spiliopoulos, Leonidas, 2009. "Neural networks as a learning paradigm for general normal form games," MPRA Paper 16765, University Library of Munich, Germany.
• Deetz, Marcus & Poddig, Thorsten & Varmaz, Armin, 2009. "Klassifizierung von Hedge-Fonds durch das k-means Clustering von Self-Organizing Maps: eine renditebasierte Analyse zur Selbsteinstufungsgüte und Stiländerungsproblematik [Classifying Hedge Funds u," MPRA Paper 16939, University Library of Munich, Germany.
• Leonidas, Spiliopoulos, 2009. "Learning backward induction: a neural network agent approach," MPRA Paper 17267, University Library of Munich, Germany.
• Pena Centeno, Tonatiuh & Martinez Jaramillo, Serafin & Abudu, Bolanle, 2009. "Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora," MPRA Paper 19560, University Library of Munich, Germany.
• Giovanis, Eleftherios, 2009. "Bootstrapping Fuzzy-GARCH Regressions on the Day of the Week Effect in Stock Returns: Applications in MATLAB," MPRA Paper 22326, University Library of Munich, Germany.
• Kahloul, Ines & Ben Mabrouk, Anouar & Hallara, Salah-Eddine, 2009. "Wavelet-Based Prediction for Governance, Diversi cation and Value Creation Variables," MPRA Paper 26484, University Library of Munich, Germany.
• Mateou, Nicos H. & Zombanakis, George A., 2009. "Fuzzy cognitive maps face the question of the Greek current account deficit sustainability," MPRA Paper 38574, University Library of Munich, Germany.
• du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
• Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
  • Patuelli, R. & Reggiani, A. & Nijkamp, P. & Schanne, N., 2009. "Neural networks for cross-sectional employment forecasts: a comparison of model specifications for germany," Serie Research Memoranda 0014, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?," Working Paper series 07_09, Rimini Centre for Economic Analysis, revised Feb 2010.
  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Cross-Sectional Employment Forecasts: A Comparison of Model Specifications for Germany," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0903, USI Università della Svizzera italiana.
• Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.
• Egorova, Natalya & Bakhtizin, Albert & Xuan, Yang, 2009. "Application of Statistical Methods and Neural Networks to Analysis of Factors of Small Business Development (the Case of China)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 15(3), pages 3-15.
• Nastac, Iulian & Bacivarov, Angelica & Costea, Adrian, 2009. "A Neuro-Classification Model for Socio-Technical Systems," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(3), pages 100-109, September.
• Morariu, Nicolae & Iancu, Eugenia & Vlad, Sorin, 2009. "A Neural Network Model for Time-Series Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 213-223, December.
• Cristian BÃLAN, 2009. "The Use Of Neural Networks In The Operational Risk Data Modeling," Proceedings of the 4th International Conference on Knowledge Management: Projects, Systems and Technologies,Bucharest, November 6-7 2009 47, Faculty of Economic Cybernetics, Statistics and Informatics, Academy of Economic Studies and National Defence University "Carol I", DEPARTMENT FOR MANAGEMENT OF THE DEFENCE RESOURCES AND EDUCATION.
• Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
  • Silvia SALINI & Ron S. KENETT, 2007. "Bayesian networks of customer satisfaction survey data," Departmental Working Papers 2007-33, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
• Luca Grilli & Angelo Sfrecola, 2005. "A Neural Networks approach to Minority Game," Quaderni DSEMS 13-2005, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
  • Luca Grilli & Angelo Sfrecola, 2009. "A Neural Networks approach to Minority Game," Quaderni DSEMS lg_nca_2009, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
• Alberto J. Hurtado & Jaime Tinto Arandes, 2009. "A new technique to measure poverty using the theory of fuzzy logic," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 34(28), pages 213-237, july-dece.
• Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?," Working Paper series 07_09, Rimini Centre for Economic Analysis, revised Feb 2010.
  • Patuelli, R. & Reggiani, A. & Nijkamp, P. & Schanne, N., 2009. "Neural networks for cross-sectional employment forecasts: a comparison of model specifications for germany," Serie Research Memoranda 0014, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Cross-Sectional Employment Forecasts: A Comparison of Model Specifications for Germany," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0903, USI Università della Svizzera italiana.

2008

• Christian M. Dahl & Yu Qin, 2008. "The limiting behavior of the estimated parameters in a misspecified random field regression model," CREATES Research Papers 2008-45, Department of Economics and Business Economics, Aarhus University.
• Eduardo Mendes & Les Oxley & Marco Reale, 2008. "Some New Approaches to Forecasting the Price of Electricity: A Study of Californian Market," Working Papers in Economics 08/05, University of Canterbury, Department of Economics and Finance.
• Juan Camilo Santana, 2008. "La curva de rendimientos: una revisión metodológica y nuevas aproximaciones de estimación," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, July.
• George Atsalakis & Dimitrios Nezis & George Matalliotakis & Camelia Ioana Ucenic & Christos Skiadas, 2008. "Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System," Working Papers 0806, University of Crete, Department of Economics.
• George Atsalakis & Camelia Ioana Ucenic & Christos Skiadas, 2008. "Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System," Working Papers 0823, University of Crete, Department of Economics.
• Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
• Giacomini, Raffaella & Gottschling, Andreas & Haefke, Christian & White, Halbert, 2008. "Mixtures of t-distributions for finance and forecasting," Journal of Econometrics, Elsevier, vol. 144(1), pages 175-192, May.
  • Giacomini, Raffaella & Gottschling, Andreas & Haefke, Christian & White, Halbert, 2007. "Mixtures of t-distributions for Finance and Forecasting," Economics Series 216, Institute for Advanced Studies.
• Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
  • Marco Fioramanti, 2006. "Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach," ISAE Working Papers 72, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
• Alper Ozun & Atilla Cifter, 2008. "Modeling long‐term memory effect in stock prices," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 25(1), pages 38-48, March.
• Jozef Baruník, 2008. "How Do Neural Networks Enhance the Predictability of Central European Stock Returns?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 58(07-08), pages 358-376, Oktober.
• Dominique Guégan, 2010. "Effect of Noise Filtering on Predictions :on the Routes of Chaos," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 53(2), pages 255-272.
  • Dominique Guegan, 2008. "Effect of noise filtering on predictions: on the routes of chaos," Documents de travail du Centre d'Economie de la Sorbonne b08008, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  • Dominique Guegan, 2008. "Effect of noise filtering on predictions : on the routes of chaos," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00235448, HAL.
• Emmanuel F. Jurczenko & Bertrand Maillet & Paul M. Merlin, 2008. "Efficient frontier for robust higher-order moment portfolio selection," Documents de travail du Centre d'Economie de la Sorbonne bla08062, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  • Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00336475, HAL.
• Junni L. Zhang & Wolfgang Härdle, 2008. "The Bayesian Additive Classification Tree Applied to Credit Risk Modelling," SFB 649 Discussion Papers SFB649DP2008-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2007. "The Default Risk of Firms Examined with Smooth Support Vector Machines," Discussion Papers of DIW Berlin 757, DIW Berlin, German Institute for Economic Research.
  • Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2008. "The Default Risk of Firms Examined with Smooth Support Vector Machines," SFB 649 Discussion Papers SFB649DP2008-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Sigbert Klinke & Cornelia Wagner, 2008. "Visualizing exploratory factor analysis models," SFB 649 Discussion Papers SFB649DP2008-012, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2008. "Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns," SFB 649 Discussion Papers SFB649DP2008-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Shiyi Chen & Kiho Jeong & Wolfgang K. Härdle, 2008. "Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns," SFB 649 Discussion Papers SFB649DP2008-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Emin AVCI & Murat ÇİNKO, 2008. "Endeks getirilerinin yapay sinir agları modelleri ile tahmin edilmesi: Gelismekte olan Avrupa borsaları uygulaması," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 23(266), pages 114-137.
• F.Münevver YILANCI & Birol YILDIZ, 2008. "Control risk assessment in auditing: Artificial neural network approach," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 23(273), pages 119-132.
• Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 383-406, November.
• Kristóf, Tamás, 2008. "A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről [Some methodological questions of bankruptcy prediction and probability of default estimation]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 441-461.
• Dániel Holló & Mónika Papp, 2008. "Assessing household credit risk: evidence from a household survey," MNB Occasional Papers 2008/70, Magyar Nemzeti Bank (Central Bank of Hungary).
• Dominique Guégan, 2010. "Effect of Noise Filtering on Predictions :on the Routes of Chaos," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 53(2), pages 255-272.
  • Dominique Guegan, 2008. "Effect of noise filtering on predictions : on the routes of chaos," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00235448, HAL.
  • Dominique Guegan, 2008. "Effect of noise filtering on predictions: on the routes of chaos," Documents de travail du Centre d'Economie de la Sorbonne b08008, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
• Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00336475, HAL.
  • Emmanuel F. Jurczenko & Bertrand Maillet & Paul M. Merlin, 2008. "Efficient frontier for robust higher-order moment portfolio selection," Documents de travail du Centre d'Economie de la Sorbonne bla08062, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
• Dunia López-Pintado, 2008. "The Spread of Free-Riding Behavior in a Social Network," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 464-479.
  • Dunia Lopez Pintado, 2007. "The Spread of Free-Riding Behavior in a Social Network," UFAE and IAE Working Papers 718.07, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
• Giovanis, Eleftherios, 2008. "A panel data analysis for the greenhouse effects in fifteen countries of European Union," MPRA Paper 10321, University Library of Munich, Germany.
• Giovanis, Eleftherios, 2008. "An algorithm using GARCH process , Monte-Carlo simulation and wavelets analysis for stock prediction," MPRA Paper 10674, University Library of Munich, Germany.
• Klein, A. & Urbig, D. & Kirn, S., 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 14433, University Library of Munich, Germany.
  • Klein, Achim & Urbig, Diemo, 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 116175, University Library of Munich, Germany, revised 30 Apr 2011.
• Klein, Achim & Urbig, Diemo, 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 116175, University Library of Munich, Germany, revised 30 Apr 2011.
  • Klein, A. & Urbig, D. & Kirn, S., 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 14433, University Library of Munich, Germany.
• Ciuiu, Daniel, 2008. "Pattern classification using polynomial and linear regression," MPRA Paper 15355, University Library of Munich, Germany.
• Ciuiu, Daniel, 2008. "Pattern classification using principal components regression," MPRA Paper 15360, University Library of Munich, Germany.
• Giovanis, Eleftherios, 2008. "Neuro-Fuzzy approach for the predictions of economic crisis," MPRA Paper 24656, University Library of Munich, Germany.
• Giovanis, Eleftherios, 2008. "Additional Smoothing Transition Autoregressive Models," MPRA Paper 24657, University Library of Munich, Germany.
• Giovanis, Eleftherios, 2008. "Applications of Least Mean Square (LMS) Algorithm Regression in Time-Series Analysis," MPRA Paper 24658, University Library of Munich, Germany.
• Giovanis, eleftheios, 2008. "A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods," MPRA Paper 24659, University Library of Munich, Germany.
• Giovanis, Eleftherios, 2008. "Smoothing Transition Autoregressive (STAR) Models with Ordinary Least Squares and Genetic Algorithms Optimization," MPRA Paper 24660, University Library of Munich, Germany.
• du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.
• Mengov, George & Egbert, Henrik & Pulov, Stefan & Georgiev, Kalin, 2008. "Emotional Balances in Experimental Consumer Choice," MPRA Paper 56689, University Library of Munich, Germany.
• Tsionas, Efthymios G. & Michaelides, Panayotis G. & Vouldis, Angelos, 2008. "Neural Networks for Approximating the Cost and Production Functions," MPRA Paper 74448, University Library of Munich, Germany.
• Russ A. Moro, 2008. "Analysis of the Predictors of Default for Portuguese Firms," Working Papers w200822, Banco de Portugal, Economics and Research Department.
• Ciuiu, Daniel, 2008. "Pattern Classification Using Secondary Components Perceptron and Economic Applications," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 5(2), pages 51-66, June.
• Fernandez, Paula & Teixeira, Joao & Ferreira, Joao & Azevedo, Susana G., 2008. "Modelling Tourism Demand: A Comparative Study Between Artificial Neural Networks And The Box-Jenkins Methodology," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 5(3), pages 30-50, Septembe2.
• Ingo Mierswa & Katharina Morik, 2008. "About the non-convex optimization problem induced by non-positive semidefinite kernel learning," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 2(3), pages 241-258, December.
• Lennart Hoogerheide & Herman K. van Dijk, 2008. "Possibly Ill-behaved Posteriors in Econometric Models," Tinbergen Institute Discussion Papers 08-036/4, Tinbergen Institute, revised 18 Apr 2008.
• Caleiro, António, 2008. "How Can Voters Classify an Incumbent under Output Persistence," Economics Discussion Papers 2008-16, Kiel Institute for the World Economy (IfW Kiel).
• Zhang, Junni L. & Härdle, Wolfgang Karl, 2008. "The bayesian additive classification tree applied to credit risk modelling," SFB 649 Discussion Papers 2008-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2007. "The Default Risk of Firms Examined with Smooth Support Vector Machines," Discussion Papers of DIW Berlin 757, DIW Berlin, German Institute for Economic Research.
  • Härdle, Wolfgang Karl & Lee, Yuh-Jye & Schäfer, Dorothea & Yeh, Yi-Ren, 2008. "The default risk of firms examined with smooth support vector machines," SFB 649 Discussion Papers 2008-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Klinke, Sigbert & Wagner, Cornelia, 2008. "Visualizing exploratory factor analysis models," SFB 649 Discussion Papers 2008-012, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Chen, Shiyi & Jeong, Kiho & Härdle, Wolfgang Karl, 2008. "Support vector regression based GARCH model with application to forecasting volatility of financial returns," SFB 649 Discussion Papers 2008-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Chen, Shiyi & Jeong, Kiho & Härdle, Wolfgang Karl, 2008. "Recurrent support vector regression for a nonlinear ARMA model with applications to forecasting financial returns," SFB 649 Discussion Papers 2008-051, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

2007

• Dunia López-Pintado, 2008. "The Spread of Free-Riding Behavior in a Social Network," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 464-479.
  • Dunia Lopez Pintado, 2007. "The Spread of Free-Riding Behavior in a Social Network," UFAE and IAE Working Papers 718.07, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
• Claudia Biancotti & Leandro D'Aurizio & Raffaele Tartaglia-Polcini, 2007. "A neural network architecture for data editing in the Bank of Italy�s business surveys," Temi di discussione (Economic working papers) 612, Bank of Italy, Economic Research and International Relations Area.
• Goetz von Peter, 2007. "International banking centres: a network perspective," BIS Quarterly Review, Bank for International Settlements, December.
• Andrew P. Blake & George Kapetanios, 2007. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 807-826, November.
  • Andrew P. Blake & George Kapetanios, 2004. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Working Papers 508, Queen Mary University of London, School of Economics and Finance.
• Hoogerheide, L.F. & van Dijk, H.K. & van Oest, R.D., 2007. "Simulation based bayesian econometric inference: principles and some recent computational advances," Econometric Institute Research Papers EI 2007-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • HOOGERHEIDE, Lennart F. & VAN DIJK, Herman K. & VAN OEST, Rutger D., 2007. "Simulation based Bayesian econometric inference: principles and some recent computational advances," LIDAM Discussion Papers CORE 2007015, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2007. "The Default Risk of Firms Examined with Smooth Support Vector Machines," Discussion Papers of DIW Berlin 757, DIW Berlin, German Institute for Economic Research.
  • Härdle, Wolfgang Karl & Lee, Yuh-Jye & Schäfer, Dorothea & Yeh, Yi-Ren, 2008. "The default risk of firms examined with smooth support vector machines," SFB 649 Discussion Papers 2008-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Blake, Andrew P. & Kapetanios, George, 2007. "Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean," Journal of Econometrics, Elsevier, vol. 137(2), pages 472-488, April.
  • Andrew P. Blake & George Kapetanios, 2003. "Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean," Working Papers 496, Queen Mary University of London, School of Economics and Finance.
• Hoogerheide, Lennart F. & Kaashoek, Johan F. & van Dijk, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," Journal of Econometrics, Elsevier, vol. 139(1), pages 154-180, July.
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & VAN DIJK, Herman K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," LIDAM Discussion Papers CORE 2005029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & van DIJK, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," LIDAM Reprints CORE 1922, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Hoogerheide, L.F. & Kaashoek, J.F. & van Dijk, H.K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," Econometric Institute Research Papers EI 2005-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
  • PREMINGER, Arie & FRANCK, Raphael, 2005. "Forecasting exchange rates: a robust regression approach," LIDAM Discussion Papers CORE 2005025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • PREMINGER, Arie & FRANCK, Raphael, 2007. "Forecasting exchange rates: a robust regression approach," LIDAM Reprints CORE 1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• HOOGERHEIDE, Lennart F. & VAN DIJK, Herman K. & VAN OEST, Rutger D., 2007. "Simulation based Bayesian econometric inference: principles and some recent computational advances," LIDAM Discussion Papers CORE 2007015, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Hoogerheide, L.F. & van Dijk, H.K. & van Oest, R.D., 2007. "Simulation based bayesian econometric inference: principles and some recent computational advances," Econometric Institute Research Papers EI 2007-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• van Eck, N.J.P. & Waltman, L., 2007. "Bibliometric Mapping of the Computational Intelligence Field," ERIM Report Series Research in Management ERS-2007-027-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
• Jürgen Franke & Jean-Pierre Stockis & Joseph Tadjuidje, 2007. "Quantile Sieve Estimates For Time Series," SFB 649 Discussion Papers SFB649DP2007-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," Discussion Paper Series 2: Banking and Financial Studies 2007,18, Deutsche Bundesbank.
  • Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2007. "Estimating Probabilities of Default With Support Vector Machines," SFB 649 Discussion Papers SFB649DP2007-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Ronald Franken, 2007. "Ein Vergleich des binären Logit-Modells mit künstlichen neuronalen Netzen zur Insolvenzprognose anhand relativer Bilanzkennzahlen," SFB 649 Discussion Papers SFB649DP2007-044, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Giacomini, Raffaella & Gottschling, Andreas & Haefke, Christian & White, Halbert, 2008. "Mixtures of t-distributions for finance and forecasting," Journal of Econometrics, Elsevier, vol. 144(1), pages 175-192, May.
  • Giacomini, Raffaella & Gottschling, Andreas & Haefke, Christian & White, Halbert, 2007. "Mixtures of t-distributions for Finance and Forecasting," Economics Series 216, Institute for Advanced Studies.
• Khurshid M. Kiani, 2007. "Asymmetric Business Cycle Fluctuations and Contagion Effects in G7 Countries," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 6(3), pages 237-253, December.
• Elena Olmedo & Ricardo Gimeno & Lorenzo Escot & Ruth Mateos, 2007. "Convergencia y Estabilidad de los Tipos de Cambio Europeos: Una Aplicación de Exponentes de Lyapunov," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 44(129), pages 91-108.
• Hailin Liao & Bin Wang & Tom Weyman-Jones, 2007. "Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition," Global Economic Review, Taylor & Francis Journals, vol. 36(4), pages 361-384.
  • Hailin Liao & Bin Wang & Tom Weyman-Jones, 2007. "Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition," Discussion Paper Series 2007_24, Department of Economics, Loughborough University, revised Nov 2007.
• Olmedo,E. & Velasco, F. & Valderas, J.M., 2007. "Caracterización no lineal y predicción no paramétrica en el IBEX35/Nonlinear Characterization and Predictions of IBEX 35," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 25, pages 815-842, Diciembre.
• Silvia SALINI & Ron S. KENETT, 2007. "Bayesian networks of customer satisfaction survey data," Departmental Working Papers 2007-033, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
• Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
  • Silvia SALINI & Ron S. KENETT, 2007. "Bayesian networks of customer satisfaction survey data," Departmental Working Papers 2007-33, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
• Martínez Estudillo, Francisco José & Hervás Martínez, César & Torres Jiménez, Mariano & Martínez Estudillo, Andrés Carlos, 2007. "Modelo no lineal basado en redes neuronales de unidades producto para clasificación. Una aplicación a la determinación del riesgo en tarjetas de crédito = Non-linear model for classification based on ," 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, vol. 3(1), pages 40-62, June.
• K. K., Suresh & K., Pradeepa Veerakumari, 2007. "Construction and Evaluation of Performance Measures for Bayesian Chain Sampling Plan (BChSP-1)," MPRA Paper 10105, University Library of Munich, Germany, revised 2007.
• Ozun, Alper & Cifter, Atilla, 2007. "Modeling Long-Term Memory Effect in Stock Prices: A Comparative Analysis with GPH Test and Daubechies Wavelets," MPRA Paper 2481, University Library of Munich, Germany.
• Cifter, Atilla & Ozun, Alper, 2007. "The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets," MPRA Paper 2482, University Library of Munich, Germany.
• Cifter, Atilla & Ozun, Alper, 2007. "Estimating the Effects of Interest Rates on Share Prices Using Multi-scale Causality Test in Emerging Markets: Evidence from Turkey," MPRA Paper 2485, University Library of Munich, Germany.
• Gelhausen, Marc Christopher, 2007. "A Generalized Neural Logit Model for Airport and Access Mode Choice in Germany," MPRA Paper 4313, University Library of Munich, Germany, revised 2007.
• Nastac, Iulian & Dobrescu, Emilian & Pelinescu, Elena, 2007. "Neuro-Adaptive Model for Financial Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 4(3), pages 19-41, September.
• Patuelli, Roberto & Longhi, Simonetta & Reggiani, Aura & Nijkamp, Peter & Blien, Uwe, 2007. "A Rank-Order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts," The Review of Regional Studies, Southern Regional Science Association, vol. 37(1), pages 64-81.
  • Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2005. "A Rank-order Analysis of Learning Models for Regional Labor Market Forecasting," Urban/Regional 0511004, University Library of Munich, Germany.
• Hailin Liao & Bin Wang & Tom Weyman-Jones, 2007. "Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition," Global Economic Review, Taylor & Francis Journals, vol. 36(4), pages 361-384.
  • Hailin Liao & Bin Wang & Tom Weyman-Jones, 2007. "Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition," Discussion Paper Series 2007_24, Department of Economics, Loughborough University, revised Nov 2007.
• Crescenzio Gallo, 2007. "Reti Neurali Artificiali: Teoria ed Applicazioni Finanziarie," Quaderni DSEMS 28-2007, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
• Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2007. "Asset price dynamics with small world interactions under hetereogeneous beliefs," Working Papers 149, Department of Applied Mathematics, Università Ca' Foscari Venezia.
• Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," SFB 649 Discussion Papers 2007-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," Discussion Paper Series 2: Banking and Financial Studies 2007,18, Deutsche Bundesbank.
• Franke, Jürgen & Stockis, Jean-Pierre & Tadjuidje, Joseph, 2007. "Quantile sieve estimates for time series," SFB 649 Discussion Papers 2007-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," Discussion Paper Series 2: Banking and Financial Studies 2007,18, Deutsche Bundesbank.
  • Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," SFB 649 Discussion Papers 2007-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Franken, Ronald, 2007. "Ein Vergleich des binären Logit-Modells mit künstlichen neuronalen Netzen zur Insolvenzprognose anhand relativer Bilanzkennzahlen," SFB 649 Discussion Papers 2007-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

2006

• Andrea BONFIGLIO, 2006. "Comparing Environmental Impact of Alternative CAP Scenarios Estimated Through an Artificial Neural Network," Working Papers 269, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
• José Luis Torres, 2006. "Modelos para la Inflación Básica de Bienes Transables y No Transables en Colombia," Borradores de Economia 3246, Banco de la Republica.
  • José Luis Torres, 2006. "Modelos Para La Inflación Básica de Bienes Transables y No Transables en Colombia," Borradores de Economia 365, Banco de la Republica de Colombia.
• Thomas H. Noe & Michael J. Rebello & Jun Wang, 2006. "The Evolution of Security Designs," Journal of Finance, American Finance Association, vol. 61(5), pages 2103-2135, October.
  • Noe, Thomas H. & Rebello, Michael J. & Wang, Jun, 2004. "The Evolution of Security Designs," SIFR Research Report Series 26, Institute for Financial Research.
• José Luis Torres, 2006. "Modelos Para La Inflación Básica de Bienes Transables y No Transables en Colombia," Borradores de Economia 365, Banco de la Republica de Colombia.
  • José Luis Torres, 2006. "Modelos para la Inflación Básica de Bienes Transables y No Transables en Colombia," Borradores de Economia 3246, Banco de la Republica.
• María del Mar Criado & José Luis Arroyo Barriguete & José Ignacio López Sánchez, 2006. "Organizaciones virtuales y redes neuronales. Algunas similitudes," Estudios Gerenciales, Universidad Icesi, January.
• Greta Falavigna, 2006. "Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks," CERIS Working Paper 200610, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
• Ata Assaf, 2006. "Nonlinear Trend Stationarity in Real Exchange Rates: Evidence from Nonlinear ADF tests," Annals of Economics and Finance, Society for AEF, vol. 7(2), pages 283-294, November.
• Shaun K. Roache, 2006. "Domestic Investment and the Cost of Capital in the Caribbean," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 6(3).
• Peltonen, Tuomas A., 2006. "Are emerging market currency crises predictable? A test," Working Paper Series 571, European Central Bank.
• Fioretti, Guido, 2006. "Recognising investment opportunities at the onset of recoveries," Research in Economics, Elsevier, vol. 60(2), pages 69-84, June.
  • Guido Fioretti, "undated". "Recognizing Investment Opportunities at the Onset of Recoveries," Modeling, Computing, and Mastering Complexity 2003 07, Society for Computational Economics.
  • Guido Fioretti, 2002. "Recognizing Investment Opportunities at the Onset of Recoveries," Macroeconomics 0207008, University Library of Munich, Germany.
• Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2006. "Graphical Data Representation in Bankruptcy Analysis," SFB 649 Discussion Papers SFB649DP2006-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Antony Unwin & Martin Theus & Wolfgang Härdle, 2006. "Exploratory Graphics of a Financial Dataset," SFB 649 Discussion Papers SFB649DP2006-031, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Shiyi Chen & Wolfgang Härdle & Rouslan Moro, 2006. "Estimation of Default Probabilities with Support Vector Machines," SFB 649 Discussion Papers SFB649DP2006-077, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
• Hüseyin İNCE, 2006. "Yapay sinir ağlarının portföy yönetiminde kullanılması," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 21(241), pages 114-127.
• Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
  • Marco Fioramanti, 2006. "Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach," ISAE Working Papers 72, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
• Dorota Witkowska, 2006. "Discrete Choice Model Application to the Credit Risk Evaluation," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 12(1), pages 33-42, February.
• Gelhausen, Marc Christopher & Wilken, Dieter, 2006. "Airport and Access Mode Choice : A Generalized Nested Logit Model Approach," MPRA Paper 4311, University Library of Munich, Germany, revised 2006.
• Fischer, Manfred M., 2006. "Neural Networks. A General Framework for Non-Linear Function Approximation," MPRA Paper 77776, University Library of Munich, Germany.
• Lebreton, Marie & Melnik, Katia, 2006. "Voluntary Participation as a Determinant of Social Capital in France Allowing for Parameter Heterogeneity," European Journal of Economic and Social Systems, Lavoisier, vol. 19(2), pages 229-255.
• Dobrescu, Emilian & Nastac, Iulian & Pelinescu, Elena, 2006. "An Adaptive Retraining Method for the Exchange Rate Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 3(1), pages 5-23, March.
• Alessandro Ludovici, 2006. "The Application of Neural Networks to the Pricing of Credit Derivatives," Rivista di Politica Economica, SIPI Spa, vol. 96(6), pages 187-221, November-.
• Bruce Mizrach, 2006. "Nonlinear Time Series Analysis," Departmental Working Papers 200604, Rutgers University, Department of Economics.
• Goutam Dutta & Pankaj Jha & Arnab Kumar Laha & Neeraj Mohan, 2006. "Artificial Neural Network Models for Forecasting Stock Price Index in the Bombay Stock Exchange," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 5(3), pages 283-295, December.
• Chakradhara Panda & V. Narasimhan, 2006. "Predicting Stock Returns," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 7(2), pages 205-218, September.
• Carole Siani & Christian de Peretti, 2006. "Bootstrapping Neural tests for conditional heteroskedasticity," Computing in Economics and Finance 2006 301, Society for Computational Economics.
• Andreas Mitschele & Stephan Chalup & Frank Schlottmann & Detlef Seese, 2006. "Applications of Kernel Methods in Financial Risk Management," Computing in Economics and Finance 2006 317, Society for Computational Economics.
• Serge Hayward, 2006. "Genetically Optimised Artificial Neural Network for Financial Time Series Data Mining," Computing in Economics and Finance 2006 417, Society for Computational Economics.
• Michele La Rocca & Cira Perna, 2006. "A multiple testing procedure for neural network model selection," Computing in Economics and Finance 2006 497, Society for Computational Economics.
• L.F. Hoogerheide & H.K. van Dijk, 2006. "Modelling option prices using neural networks," Computing in Economics and Finance 2006 78, Society for Computational Economics.
• Yannis Ioannides, 2006. "Topologies of social interactions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 28(3), pages 559-584, August.
  • Yannis Ioannides, 2001. "Topologies of Social Interactions," Discussion Papers Series, Department of Economics, Tufts University 0104, Department of Economics, Tufts University.
  • Yannis M. Ioannides, 2004. "Topologies Of Social Interactions," Econometric Society 2004 North American Winter Meetings 287, Econometric Society.
• Cars Hommes & Sebastiano Manzan, 2006. "Testing for Nonlinear Structure and Chaos in Economic Time. A Comment," Tinbergen Institute Discussion Papers 06-030/1, Tinbergen Institute.
• Douglas Rivas & José Luciano Maldonado & Rafael Borges & Gerardo Colmenares, 2006. "Application of genetics algorithms for estimating the parameters of a Cox regression model," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 31(22), pages 57-74, january-d.
• Francesco Bertoluzzo & Marco Corazza, 2006. "Financial trading systems: Is recurrent reinforcement the via?," Working Papers 141, Department of Applied Mathematics, Università Ca' Foscari Venezia.
• M. Mete Doğanay & Nildağ Başak Ceylan & Ramazan Aktaş, 2006. "Predicting Financial Failure Of The Turkish Banks," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 1-19.
• M. Mete Doğanay & Nildağ Başak Ceylan & Ramazan Aktaş, 2006. "Predicting Financial Failure Of The Turkish Banks," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 1-19.
• Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2006. "Graphical data representation in bankruptcy analysis," SFB 649 Discussion Papers 2006-015, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
• Chen, Shiyi & Härdle, Wolfgang Karl & Moro, Rouslan A., 2006. "Estimation of default probabilities with Support Vector Machines," SFB 649 Discussion Papers 2006-077, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

2005

• Marusia Ivanova, 2005. "Sales Forecasting Using Artificial Neural Networks," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 6, pages 60-81.
• Heather M. Anderson & Farshid Vahid, 2005. "Nonlinear Correlograms and Partial Autocorrelograms," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 957-982, December.
  • Heather M. Anderson & Farshid Vahid, 2003. "Nonlinear Correlograms and Partial Autocorrelograms," Monash Econometrics and Business Statistics Working Papers 19/03, Monash University, Department of Econometrics and Business Statistics.
• Marek Hlavacek & Michael Konak & Josef Cada, 2005. "The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation," Working Papers 2005/11, Czech National Bank.
• Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
  • PREMINGER, Arie & FRANCK, Raphael, 2005. "Forecasting exchange rates: a robust regression approach," LIDAM Discussion Papers CORE 2005025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • PREMINGER, Arie & FRANCK, Raphael, 2007. "Forecasting exchange rates: a robust regression approach," LIDAM Reprints CORE 1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Hoogerheide, Lennart F. & Kaashoek, Johan F. & van Dijk, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," Journal of Econometrics, Elsevier, vol. 139(1), pages 154-180, July.
  • Hoogerheide, L.F. & Kaashoek, J.F. & van Dijk, H.K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," Econometric Institute Research Papers EI 2005-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & van DIJK, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," LIDAM Reprints CORE 1922, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & VAN DIJK, Herman K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," LIDAM Discussion Papers CORE 2005029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Johnson, Christian A. & Padilla, Miguel A., 2005. "Regularidades no lineales en índices accionarios. Una aproximación con redes neuronales," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(288), pages 765-821, octubre-d.
• Hoogerheide, Lennart F. & Kaashoek, Johan F. & van Dijk, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," Journal of Econometrics, Elsevier, vol. 139(1), pages 154-180, July.
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & VAN DIJK, Herman K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," LIDAM Discussion Papers CORE 2005029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & van DIJK, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," LIDAM Reprints CORE 1922, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Hoogerheide, L.F. & Kaashoek, J.F. & van Dijk, H.K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," Econometric Institute Research Papers EI 2005-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Caleiro, António, 2005. "How to Classify a Government? Can a Neural Network do it?," EconStor Preprints 142736, ZBW - Leibniz Information Centre for Economics.
  • António Caleiro, 2005. "How to Classify a Government? Can a Neural Network do it?," Economics Working Papers 9_2005, University of Évora, Department of Economics (Portugal).
• Cuellar, M. P. & Delgado, M. & Pegalajar, M. C., 2005. "Multiobjective Evolutionary Optimization For Elman Recurrent Neural Networks, Applied To Time Series Prediction," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 17-33, May.
• Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
  • Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
• Eliasson, Gunnar & Johansson, Dan & Taymaz, Erol, 2005. "Firm Tunrover and the Rate of Macroeconomic Growth - Simulating the Macroeconomic Effects of Schumpeterian Creative Destruction," Ratio Working Papers 66, The Ratio Institute.
• Atanas Christev, 2005. "The Hyperinflation Model of Money Demand (or Cagan Revisited): Some New Empirical Evidence from the 1990s," CERT Discussion Papers 0507, Centre for Economic Reform and Transformation, Heriot Watt University.
• Christian A. Johnson & Rodrigo Vergara, 2005. "The implementation of monetary policy in an emerging economy: the case of Chile," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 45-62, June.
  • Christian A Johnson & Rodrigo Vergara, 2005. "The Implementation of Monetary Policy in an Emerging Economy: The Case of Chile," Documentos de Trabajo 291, Instituto de Economia. Pontificia Universidad Católica de Chile..
• Christian A. Johnson, 2005. "Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 95-121, June.
• Christian A. Johnson & Rodrigo Vergara, 2005. "The implementation of monetary policy in an emerging economy: the case of Chile," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 45-62, June.
  • Christian A Johnson & Rodrigo Vergara, 2005. "The Implementation of Monetary Policy in an Emerging Economy: The Case of Chile," Documentos de Trabajo 291, Instituto de Economia. Pontificia Universidad Católica de Chile..
• Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 65-89, August.
• Dorota Witkowska & Edyta Marcinkiewicz, 2005. "Construction and Evaluation of Trading Systems: Warsaw Index Futures," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 11(1), pages 83-92, February.
• Dorota Witkowska & Mariola Chrzanowska, 2005. "Prediction of Loan Redemption: Logit Models and Artificial Neural Networks," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 11(3), pages 343-343, August.
• Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
• Juliana Yim & Heather Mitchell, 2005. "A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 15(1), pages 73-93, January-A.
• Cakir, Murat, 2005. "Firma Başarısızlığının Dinamiklerinin Belirlenmesinde Makina Öğrenmesi Teknikleri: Ampirik Uygulamalar ve Karşılaştırmalı Analiz [Machine Learning Techniques in Determining the Dynamics of Corporat," MPRA Paper 55975, University Library of Munich, Germany.
• Wilken, Dieter & Berster, Peter & Gelhausen, Marc Christopher, 2005. "Airport Choice in Germany - New Empirical Evidence of the German Air Traveller Survey 2003," MPRA Paper 5631, University Library of Munich, Germany.
• Serge Hayward, 2005. "Multiscale Representation of Agents Heterogeneous Beliefs in Analysis of CAC40 Prices with Frequency Decomposition," Computing in Economics and Finance 2005 285, Society for Computational Economics.
• Chiu-Che Tseng & Yu-Chieh Lin, 2005. "Financial Computational Intelligence," Computing in Economics and Finance 2005 42, Society for Computational Economics.
• Chih Ming Tan, 2010. "No one true path: uncovering the interplay between geography, institutions, and fractionalization in economic development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1100-1127, November/.
  • Chih Ming Tan, 2005. "No One True Path: Uncovering the Interplay between Geography, Institutions, and Fractionalization in Economic Development," Discussion Papers Series, Department of Economics, Tufts University 0512, Department of Economics, Tufts University.
• Louise C. Keely & Chih Ming Tan, 2005. "Understanding Divergent Views on Redistribution Policy in the United States," Discussion Papers Series, Department of Economics, Tufts University 0515, Department of Economics, Tufts University.
• Luca Grilli & Angelo Sfrecola, 2005. "A Neural Networks approach to Minority Game," Quaderni DSEMS 13-2005, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
  • Luca Grilli & Angelo Sfrecola, 2009. "A Neural Networks approach to Minority Game," Quaderni DSEMS lg_nca_2009, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
• Luca Grilli & Angelo Sfrecola, 2005. "Neural Networks to Predict Financial Time Series in a Minority Game Context," Quaderni DSEMS 14-2005, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
• Shapour Mohammadi & Hossein Abbasi- Nejad, 2005. "A Matlab Code for Univariate Time Series Forecasting," Computer Programs 0505001, University Library of Munich, Germany.
• António Caleiro, 2005. "How to Classify a Government? Can a Neural Network do it?," Economics Working Papers 9_2005, University of Évora, Department of Economics (Portugal).
  • Caleiro, António, 2005. "How to Classify a Government? Can a Neural Network do it?," EconStor Preprints 142736, ZBW - Leibniz Information Centre for Economics.

2004

• Martha A. Misas A. & Enrique López E. & Carlos A. Arango A. & uan Nicolás Hernández A., 2004. "No-linealidades en la demanda de efectivo en Colombia: las redes neuronales como herramienta de pronóstico," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 10-57, June.
  • Martha A. Misas A. & Enrique López E. & Carlos A. Arango A. & Juan Nicolás Hernández A., 2004. "No-linealidades en la demanda de efectivo en Colombia: las redes neuronales como herramienta de pronóstico," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 10-57, June.
• Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Money Affairs, CEMLA, vol. 0(1), pages 1-24, January-J.
  • Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 80-115, June.
  • Martha López P., 2004. "Efficient policy rule for inflation targeting in Colombia," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 80-115, June.
  • Martha López P., 2003. "Efficient Policy Rule for Inflation Targeting in Colombia," Borradores de Economia 240, Banco de la Republica de Colombia.
  • Martha López P., 2003. "Efficient Policy Rule For Inflation Targeting In Colombia," Borradores de Economia 2437, Banco de la Republica.
• Rómulo Chumacero E., 2004. "Forecasting Chilean Industrial Production and Sales With Automated Procedures," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 47-56, December.
  • Rómulo Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Working Papers Central Bank of Chile 260, Central Bank of Chile.
  • Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
• Rómulo Chumacero E., 2004. "Forecasting Chilean Industrial Production and Sales With Automated Procedures," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 47-56, December.
  • Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
  • Rómulo Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Working Papers Central Bank of Chile 260, Central Bank of Chile.
• Martha López P., 2004. "Efficient policy rule for inflation targeting in Colombia," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 80-115, June.
  • Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Money Affairs, CEMLA, vol. 0(1), pages 1-24, January-J.
  • Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 80-115, June.
  • Martha López P., 2003. "Efficient Policy Rule for Inflation Targeting in Colombia," Borradores de Economia 240, Banco de la Republica de Colombia.
  • Martha López P., 2003. "Efficient Policy Rule For Inflation Targeting In Colombia," Borradores de Economia 2437, Banco de la Republica.
• Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 80-115, June.
  • Martha López P., 2004. "Efficient policy rule for inflation targeting in Colombia," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 80-115, June.
  • Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Money Affairs, CEMLA, vol. 0(1), pages 1-24, January-J.
  • Martha López P., 2003. "Efficient Policy Rule for Inflation Targeting in Colombia," Borradores de Economia 240, Banco de la Republica de Colombia.
  • Martha López P., 2003. "Efficient Policy Rule For Inflation Targeting In Colombia," Borradores de Economia 2437, Banco de la Republica.
• Martha A. Misas A. & Enrique López E. & Carlos A. Arango A. & Juan Nicolás Hernández A., 2004. "No-linealidades en la demanda de efectivo en Colombia: las redes neuronales como herramienta de pronóstico," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 10-57, June.
  • Martha A. Misas A. & Enrique López E. & Carlos A. Arango A. & uan Nicolás Hernández A., 2004. "No-linealidades en la demanda de efectivo en Colombia: las redes neuronales como herramienta de pronóstico," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 10-57, June.
• Nadia D'Annunzio & Greta Falavigna, 2004. "Analysis and forecasting models for default risk. A survey of applied methodologies," CERIS Working Paper 200417, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
• Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Rating Companies with Support Vector Machines," Discussion Papers of DIW Berlin 416, DIW Berlin, German Institute for Economic Research.
• Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
• Jonathan B. Hill, 2004. "Consistent LM-Tests for Linearity Against Compound Smooth Transition Alternatives," Econometric Society 2004 North American Summer Meetings 42, Econometric Society.
• Yannis Ioannides, 2006. "Topologies of social interactions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 28(3), pages 559-584, August.
  • Yannis Ioannides, 2001. "Topologies of Social Interactions," Discussion Papers Series, Department of Economics, Tufts University 0104, Department of Economics, Tufts University.
  • Yannis M. Ioannides, 2004. "Topologies Of Social Interactions," Econometric Society 2004 North American Winter Meetings 287, Econometric Society.
• Vroomen, Bjorn & Hans Franses, Philip & van Nierop, Erjen, 2004. "Modeling consideration sets and brand choice using artificial neural networks," European Journal of Operational Research, Elsevier, vol. 154(1), pages 206-217, April.
  • Vroomen, B.L.K. & Franses, Ph.H.B.F. & van Nierop, J.E.M., 2001. "Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks," ERIM Report Series Research in Management ERS-2001-10-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
• Eliasson, Gunnar & Johansson, Dan & Taymaz, Erol, 2004. "Simulating the New Economy," Structural Change and Economic Dynamics, Elsevier, vol. 15(3), pages 289-314, September.
  • Eliasson, Gunnar & Johansson, Dan & Taymaz, Erol, 2004. "Simulating the New Economy," Ratio Working Papers 52, The Ratio Institute.
• Hoogerheide, L.F. & Kaashoek, J.F. & van Dijk, H.K., 2004. "Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models," Econometric Institute Research Papers EI 2004-19, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Segovia-Vargas, María Jesús & Salcedo-Sanz, Sancho & Bousoño-Calzón, Carlos, 2004. "Prediction Of Insolvency In Non-Life Insurance Companies Using Support Vector Machines, Genetic Algorithms And Simulated Annealing," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 79-94, May.
• Eliasson, Gunnar & Johansson, Dan & Taymaz, Erol, 2004. "Simulating the New Economy," Structural Change and Economic Dynamics, Elsevier, vol. 15(3), pages 289-314, September.
  • Eliasson, Gunnar & Johansson, Dan & Taymaz, Erol, 2004. "Simulating the New Economy," Ratio Working Papers 52, The Ratio Institute.
• Thomas H. Noe & Michael J. Rebello & Jun Wang, 2006. "The Evolution of Security Designs," Journal of Finance, American Finance Association, vol. 61(5), pages 2103-2135, October.
  • Noe, Thomas H. & Rebello, Michael J. & Wang, Jun, 2004. "The Evolution of Security Designs," SIFR Research Report Series 26, Institute for Financial Research.
• Andrew P. Blake & George Kapetanios, 2007. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 807-826, November.
  • Andrew P. Blake & George Kapetanios, 2004. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Working Papers 508, Queen Mary University of London, School of Economics and Finance.
• Andrew P. Blake & George Kapetanios, 2007. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 807-826, November.
  • Andrew P. Blake & George Kapetanios, 2004. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Working Papers 508, Queen Mary University of London, School of Economics and Finance.
  • Andrew P. Blake & George Kapetanios, 2004. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Working Papers 508, Queen Mary University of London, School of Economics and Finance.
• Gaubert, Patrice, 2004. "Neural networks. Editorial," European Journal of Economic and Social Systems, Lavoisier, vol. 17(1-2), pages 7-9.
• Joya, Gonzalo & Garcìa-Lagos, Francisco & Atencia, Miguel A. & Sandoval, Francisco, 2004. "Artificial Neural Networks for Energy Management System Applicability and Limitations of the Main Paradigms," European Journal of Economic and Social Systems, Lavoisier, vol. 17(1-2), pages 11-28.
• Boné, Romuald & Crucianu, Michel, 2004. "Multi-Step-Ahead Prediction with Neural Networks," European Journal of Economic and Social Systems, Lavoisier, vol. 17(1-2), pages 85-98.
• Sandoval, Francisco & Garcìa-Lagos, Francisco & Joya, Gonzalo, 2004. "Design of Artificial Neural Networks using Evolutionary Computation," European Journal of Economic and Social Systems, Lavoisier, vol. 17(1-2), pages 99-123.
• de Bodt, Eric & Rynkiewicz, Joseph & Cottrell, Marie, 2004. "Some Known Facts about Financial Data," European Journal of Economic and Social Systems, Lavoisier, vol. 17(1-2), pages 167-182.
• Landasse, Amaury & Cardon, Pierre & Wertz, Vincent & de Bodt, Eric & Verleysen, Michel, 2004. "Self-Organizing Feature Maps for the Classification of Investment Funds," European Journal of Economic and Social Systems, Lavoisier, vol. 17(1-2), pages 183-195.
• Perez, Muriel, 2004. "SME's Performance and Neural Classification," European Journal of Economic and Social Systems, Lavoisier, vol. 17(1-2), pages 197-210.
• Rómulo Chumacero E., 2004. "Forecasting Chilean Industrial Production and Sales With Automated Procedures," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 47-56, December.
  • Rómulo Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Working Papers Central Bank of Chile 260, Central Bank of Chile.
  • Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
• Chueh-Yung Tsao & Shu-Heng Chen, 2004. "Discovering Financial Patterns in the Foreign Exchange Markets," Computing in Economics and Finance 2004 203, Society for Computational Economics.
• Serge Hayward, 2004. "Heterogeneous Agents Past and Forward Time Horizons in Setting Up a Computational Model," Computing in Economics and Finance 2004 241, Society for Computational Economics.
• Alicia Gazely & Jane Binner & Graham Kendall, 2004. "Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money," Computing in Economics and Finance 2004 258, Society for Computational Economics.
• Stefan Fink & Janette F. Walde, 2004. "Money makes the world go round ... about the necessity of nonlinear techniques in interest rate forecasting," Computing in Economics and Finance 2004 344, Society for Computational Economics.
• Lennart F. Hoogerheide & Johan F. Kaashoek, 2004. "Functional Approximations to Likelihoods/Posterior Densities: A Neural Network Approach to Efficient Sampling," Computing in Economics and Finance 2004 74, Society for Computational Economics.
• Francisco J. Delgado, 2004. "Efficiency in Public Sector: A Neural Network Approach," Computing in Economics and Finance 2004 81, Society for Computational Economics.
• John R. Roy & Jean-Claude Thill, 2004. "Spatial interaction modelling," Advances in Spatial Science, in: Raymond J. G. M. Florax & David A. Plane (ed.), Fifty Years of Regional Science, pages 339-361, Springer.
• Jonathan B. Hill, 2004. "Consistent Model Specification Tests Against Smooth Transition Alternatives," Econometrics 0402004, University Library of Munich, Germany, revised 05 Aug 2005.

2003

• Gordon H. Dash & Nina Kajiji, 2003. "New Evidence on the Predictability of South Africa FX Volatility in Heterogeneous Bilateral Markets," The African Finance Journal, Africagrowth Institute, vol. 5(1), pages 1-15.
• Shahiem Ganief & Nicholas Biekpe, 2003. "Measuring Market Risk Using Extreme Value Theory: An Empirical Study Using South African Rand/Dollar One-Year Futures Contract," The African Finance Journal, Africagrowth Institute, vol. 5(1), pages 68-86.
• Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Money Affairs, CEMLA, vol. 0(1), pages 1-24, January-J.
  • Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 80-115, June.
  • Martha López P., 2004. "Efficient policy rule for inflation targeting in Colombia," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 80-115, June.
  • Martha López P., 2003. "Efficient Policy Rule for Inflation Targeting in Colombia," Borradores de Economia 240, Banco de la Republica de Colombia.
  • Martha López P., 2003. "Efficient Policy Rule For Inflation Targeting In Colombia," Borradores de Economia 2437, Banco de la Republica.
• Goyal, Sanjeev & Joshi, Sumit, 2003. "Networks of collaboration in oligopoly," Games and Economic Behavior, Elsevier, vol. 43(1), pages 57-85, April.
  • Sanjeev Goyal & Sumit Joshi, 2000. "Networks of Collaboration in Oligopoly," Tinbergen Institute Discussion Papers 00-092/1, Tinbergen Institute.
  • Sumit Joshi, 2000. "Networks of Collaboration in Oligopoly," Econometric Society World Congress 2000 Contributed Papers 0623, Econometric Society.
  • Goyal, S. & Joshi, S., 2000. "Networks of Collaboration in Oligopoly," Econometric Institute Research Papers EI 9952-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2003. "Financial Markets Analysis by Probabilistic Fuzzy Modelling," ERIM Report Series Research in Management ERS-2003-036-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
• Lambert, J. & Schneider, M. & Kandel, A., 2003. "An Overview Of Techniques For Genetic Evolution Of Fuzzy Systems," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 3-12, May.
• Heather M. Anderson & Farshid Vahid, 2005. "Nonlinear Correlograms and Partial Autocorrelograms," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 957-982, December.
  • Heather M. Anderson & Farshid Vahid, 2003. "Nonlinear Correlograms and Partial Autocorrelograms," Monash Econometrics and Business Statistics Working Papers 19/03, Monash University, Department of Econometrics and Business Statistics.
• Photis, Yorgos N. & Grekoussis, George, 2003. "Assesing demand in stochastic locational planning problems: An Artificial Intelligence approach for emergency service systems," MPRA Paper 20678, University Library of Munich, Germany.
• Photis, Yorgos N. & Manetos, Panos & Grekoussis, George, 2003. "Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece," MPRA Paper 20756, University Library of Munich, Germany, revised 2003.
• Andreou, Andreas S. & Zombanakis, George A., 2003. "Intelligent information systems for defence problems," MPRA Paper 38637, University Library of Munich, Germany.
• Timotej Jagric, 2003. "Forecasting with leading economic indicators - a non-linear approach," Prague Economic Papers, Prague University of Economics and Business, vol. 2003(1), pages 68-83.
• Blake, Andrew P. & Kapetanios, George, 2007. "Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean," Journal of Econometrics, Elsevier, vol. 137(2), pages 472-488, April.
  • Andrew P. Blake & George Kapetanios, 2003. "Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean," Working Papers 496, Queen Mary University of London, School of Economics and Finance.
• Blake, Andrew P. & Kapetanios, George, 2007. "Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean," Journal of Econometrics, Elsevier, vol. 137(2), pages 472-488, April.
  • Andrew P. Blake & George Kapetanios, 2003. "Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean," Working Papers 496, Queen Mary University of London, School of Economics and Finance.
  • Andrew P. Blake & George Kapetanios, 2003. "Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean," Working Papers 496, Queen Mary University of London, School of Economics and Finance.
• Chueh-Yung Tsao & Shu-Heng Chen, 2003. "Financial Modeling based on the Trajectory Domain," Computing in Economics and Finance 2003 36, Society for Computational Economics.
• Chokri Dridi & Geoffrey J.D. Hewings, 2003. "Sectors associations and similarities in input-output systems: An application of dual scaling and fuzzy logic to Canada and the United States," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 37(4), pages 629-656, December.
• Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 424-443, May.
  • PERRON, Benoît, 1999. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," Cahiers de recherche 9901, Universite de Montreal, Departement de sciences economiques.
  • Benoit Perron, 2002. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," CIRANO Working Papers 2002s-88, CIRANO.
  • Benoit Perron, 2000. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-return Trade-off," Econometric Society World Congress 2000 Contributed Papers 1576, Econometric Society.

2002

• Ching‐To Albert Ma & Thomas G. Mcguire, 2002. "Network Incentives in Managed Health Care," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(1), pages 1-35, March.
  • Ching-to Albert Ma & Thomas G. McGuire, 1998. "Network Incentives in Managed Health Care," Papers 0094, Boston University - Industry Studies Programme.
  • Ma, C.-t.A. & McGuirem T.G., 1998. "Network Incentives in Managed Health Care," Papers 94, Boston University - Department of Economics.
• Giacomini, Raffaella & Haefke, Christian & White, Halbert & Gottschling, Andreas, 2002. "Hypernormal Densities," University of California at San Diego, Economics Working Paper Series qt9wr373nt, Department of Economics, UC San Diego.
  • Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal Densities," Boston College Working Papers in Economics 584, Boston College Department of Economics.
  • Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal densities," Economics Working Papers 638, Department of Economics and Business, Universitat Pompeu Fabra.
• Juan Manuel Julio & Silvia Juliana Mera & Alejandro Revéiz Hérault, "undated". "La curva Spot (Cero Cupón)," Lecturas en Finanzas 002960, Banco de la Republica de Colombia.
  • Juan Manuel Julio & Silvia Juliana Mera & Alejandro Revéiz Hérault, 2002. "La curva Spot (Cero Cupón)," Lecturas en Finanzas 2960, Banco de la República.
• Hoogerheide, L.F. & Kaashoek, J.F. & van Dijk, H.K., 2002. "Functional approximations to posterior densities: a neural network approach to efficient sampling," Econometric Institute Research Papers EI 2002-48, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Manfred M. Fischer, 2002. "Learning in neural spatial interaction models: A statistical perspective," Journal of Geographical Systems, Springer, vol. 4(3), pages 287-299, October.
  • Fischer, Manfred M., 2002. "Learning in Neural Spatial Interaction Models: A Statistical Perspective," MPRA Paper 77788, University Library of Munich, Germany.
• Heather M. Anderson, 2002. "Choosing Lag Lengths in Nonlinear Dynamic Models," Monash Econometrics and Business Statistics Working Papers 21/02, Monash University, Department of Econometrics and Business Statistics.
• ANDREOU, A. S. & PARSOPOULOS, K. E. & VRACHATIS, M. N. & Zombanakis, George A., 2002. "Searching for the Optimal Defence Expenditure: An Answer in the Context of the Greek – Turkish Arms Race," MPRA Paper 51580, University Library of Munich, Germany, revised 15 Aug 2002.
• Manfred M. Fischer, 2002. "Learning in neural spatial interaction models: A statistical perspective," Journal of Geographical Systems, Springer, vol. 4(3), pages 287-299, October.
  • Fischer, Manfred M., 2002. "Learning in Neural Spatial Interaction Models: A Statistical Perspective," MPRA Paper 77788, University Library of Munich, Germany.
• Fischer, Manfred M. & Reismann, Martin, 2002. "A methodology for neural spatial interaction modelling," ERSA conference papers ersa02p034, European Regional Science Association.
  • Fischer, Manfred M. & Reismann, Martin, 2002. "A Methodology for Neural Spatial Interaction Modeling," MPRA Paper 77794, University Library of Munich, Germany.
• George Kapetanios, 2002. "Testing for Structural Breaks in Nonlinear Dynamic Models Using Artificial Neural Network Approximations," Working Papers 470, Queen Mary University of London, School of Economics and Finance.
• George Kapetanios, 2002. "Testing for Structural Breaks in Nonlinear Dynamic Models Using Artificial Neural Network Approximations," Working Papers 470, Queen Mary University of London, School of Economics and Finance.
  • George Kapetanios, 2002. "Testing for Structural Breaks in Nonlinear Dynamic Models Using Artificial Neural Network Approximations," Working Papers 470, Queen Mary University of London, School of Economics and Finance.
• Leonardo Souza & Alvaro Veiga & Marcelo C. Medeiros, 2002. "Evaluating the performance of GARCH models using White´s Reality Check," Textos para discussão 453, Department of Economics PUC-Rio (Brazil).
• Chris Birchenhall & David S. Bree & Rahim Lakha, 2002. "The Empirics of Financial Development and Economic Growth: Using Unsupervised Learning to Detect Multiple Steady States," Computing in Economics and Finance 2002 142, Society for Computational Economics.
• Luc Bauwens & Charles S. Bos & Herman K. van Dijk & Rutger D. van Oest, 2002. "Adaptive Polar Sampling," Computing in Economics and Finance 2002 307, Society for Computational Economics.
• Sebastien Page & Anne-Sophie Vanroyen, 2002. "The Multiple Dimensions of Asset Allocation:Countries, Sectors or Factors?," Computing in Economics and Finance 2002 65, Society for Computational Economics.
• Harald Hruschka & Werner Fettes & Markus Probst, 2002. "Die Bewährung von Ankerpreismodellen bei der Erklärung der Markenwahl," Schmalenbach Journal of Business Research, Springer, vol. 54(5), pages 426-441, August.
• Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal Densities," Boston College Working Papers in Economics 584, Boston College Department of Economics.
  • Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal densities," Economics Working Papers 638, Department of Economics and Business, Universitat Pompeu Fabra.
  • Giacomini, Raffaella & Haefke, Christian & White, Halbert & Gottschling, Andreas, 2002. "Hypernormal Densities," University of California at San Diego, Economics Working Paper Series qt9wr373nt, Department of Economics, UC San Diego.
• Fischer, Manfred M. & Reismann, Martin, 2002. "A Methodology for Neural Spatial Interaction Modeling," MPRA Paper 77794, University Library of Munich, Germany.
  • Fischer, Manfred M. & Reismann, Martin, 2002. "A methodology for neural spatial interaction modelling," ERSA conference papers ersa02p034, European Regional Science Association.
• Fioretti, Guido, 2006. "Recognising investment opportunities at the onset of recoveries," Research in Economics, Elsevier, vol. 60(2), pages 69-84, June.
  • Guido Fioretti, "undated". "Recognizing Investment Opportunities at the Onset of Recoveries," Modeling, Computing, and Mastering Complexity 2003 07, Society for Computational Economics.
  • Guido Fioretti, 2002. "Recognizing Investment Opportunities at the Onset of Recoveries," Macroeconomics 0207008, University Library of Munich, Germany.
• Doherr, Thorsten & Czarnitzki, Dirk, 2002. "Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications," ZEW Discussion Papers 02-41, ZEW - Leibniz Centre for European Economic Research.

2001

• Yoshihisa Suzuki, 2001. "An Artificial Neural Network Test For Structural Change With Unspecified Parametric Form," The Japanese Economic Review, Japanese Economic Association, vol. 52(3), pages 339-365, September.
• Vroomen, Bjorn & Hans Franses, Philip & van Nierop, Erjen, 2004. "Modeling consideration sets and brand choice using artificial neural networks," European Journal of Operational Research, Elsevier, vol. 154(1), pages 206-217, April.
  • Vroomen, B.L.K. & Franses, Ph.H.B.F. & van Nierop, J.E.M., 2001. "Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks," ERIM Report Series Research in Management ERS-2001-10-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
• Delaigle, A. & Gijbels, I., 2001. "Bootstrap Bandwidth Selection in Kernel Density Estimation from a Contaminated Sample," Papers 0116, Catholique de Louvain - Institut de statistique.
• Levasseur, Michel & Bodt, Eric De & Severin, Eric, 2001. "Debt: A Factor Of Both "Good" And "Bad" Stress During An Economic Recession: Evidence From France," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 89-107, May.
• Schenker, Adam & Last, Mark & Kandel, Abraham, 2001. "Evaluation of Fuzzy Rules Extracted from Data," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 3-21, November.
• Riechmann, Thomas, 2001. "Two Notes on Replication in Evolutionary Modelling," Hannover Economic Papers (HEP) dp-239, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
• Caridad Y Ocerín, J.M. & Ceular Villamandos, N., 2001. "Un análisis del mercado de la vivienda a través de redes neuronales artificiales," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 18, pages 67-81, Agosto.
• Andreou, Andreas S. & Zombanakis, George A., 2001. "A Neural Network Measurement of Relative Military Security: The Case of Greece and Cyprus," MPRA Paper 14539, University Library of Munich, Germany, revised 2001.
• de Rigo, Daniele & Rizzoli, Andrea Emilio & Soncini-Sessa, Rodolfo & Weber, Enrico & Zenesi, Pietro, 2001. "Neuro-dynamic programming for the efficient management of reservoir networks," MPRA Paper 42233, University Library of Munich, Germany.
• Harald Hruschka, 2001. "An Artificial Neural Net Attraction Model (Annam) To Analyze Market Share Effects Of Marketing Instruments," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 53(1), pages 27-40, January.
• Shu-Heng Chen, 2001. "John Holland's Legacy in Economics: Artificial Adaptive Economic Agents --From 1986 to the Present in Retrospect," Computing in Economics and Finance 2001 158, Society for Computational Economics.
• Mario Eboli, 2001. "Imitation and the diffusion of innovation in e-commerce," Computing in Economics and Finance 2001 237, Society for Computational Economics.
• Neil F. Johnson, David Lamper, Paul Jefferies, Michael Hart and Sam Howison, 2001. "Profit opportunities, crash prediction and risk minimization in artificial and real-world markets," Computing in Economics and Finance 2001 86, Society for Computational Economics.
• Yannis Ioannides, 2006. "Topologies of social interactions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 28(3), pages 559-584, August.
  • Yannis Ioannides, 2001. "Topologies of Social Interactions," Discussion Papers Series, Department of Economics, Tufts University 0104, Department of Economics, Tufts University.
  • Yannis M. Ioannides, 2004. "Topologies Of Social Interactions," Econometric Society 2004 North American Winter Meetings 287, Econometric Society.

2000

• Nikola Gradojevic & Jing Yang, 2000. "The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables," Staff Working Papers 00-23, Bank of Canada.
• Goyal, Sanjeev & Joshi, Sumit, 2003. "Networks of collaboration in oligopoly," Games and Economic Behavior, Elsevier, vol. 43(1), pages 57-85, April.
  • Sumit Joshi, 2000. "Networks of Collaboration in Oligopoly," Econometric Society World Congress 2000 Contributed Papers 0623, Econometric Society.
• Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 424-443, May.
  • PERRON, Benoît, 1999. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," Cahiers de recherche 9901, Universite de Montreal, Departement de sciences economiques.
  • Benoit Perron, 2000. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-return Trade-off," Econometric Society World Congress 2000 Contributed Papers 1576, Econometric Society.
  • Benoit Perron, 2002. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," CIRANO Working Papers 2002s-88, CIRANO.
• Mohamad Shaaf, 2000. "Predicting Recession Using the Yield Curve: An Artificial Intelligence and Econometric Comparison," Eastern Economic Journal, Eastern Economic Association, vol. 26(2), pages 171-190, Spring.
• Goyal, Sanjeev & Joshi, Sumit, 2003. "Networks of collaboration in oligopoly," Games and Economic Behavior, Elsevier, vol. 43(1), pages 57-85, April.
  • Sanjeev Goyal & Sumit Joshi, 2000. "Networks of Collaboration in Oligopoly," Tinbergen Institute Discussion Papers 00-092/1, Tinbergen Institute.
  • Goyal, S. & Joshi, S., 2000. "Networks of Collaboration in Oligopoly," Econometric Institute Research Papers EI 9952-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Ghattas, B., 2000. "Importance des variables dans les methodes CART," G.R.E.Q.A.M. 00b04, Universite Aix-Marseille III.
• Aubin, J.-P. & Haddad, G., 2000. "Systemes impulsionnels et hybrides impulsionnels avec memoire," Papiers d'Economie Mathématique et Applications 2000.126, Université Panthéon-Sorbonne (Paris 1).
• A. S. Andreou & G. A. Zombanakis & E. F. Georgopoulos & S. D. Likothanassis, 2000. "In search of a warning strategy against exchange-rate attacks: Forecasting tactics using artificial neural networks," Discrete Dynamics in Nature and Society, Hindawi, vol. 5, pages 1-17, January.
  • Andreou, Andreas S. & Zombanakis, George A. & Georgopoulos, E. F. & Likothanassis, S. D., 2000. "In Search of a Warning Strategy Against Exchange-rate Attacks: Forecasting Tactics Using Artificial Neural Networks," MPRA Paper 18197, University Library of Munich, Germany.
• Pereira, Robert, 2000. "Genetic Algorithm Optimisation for Finance and Investments," MPRA Paper 8610, University Library of Munich, Germany.
  • Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
  • Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
• Andreou, Andreas S. & Zombanakis, George A., 2000. "Financial Versus Human Resources in the Greek-Turkish Arms Race: A Forecasting Investigation Using Artificial Neural Networks," MPRA Paper 13892, University Library of Munich, Germany.
• A. S. Andreou & G. A. Zombanakis & E. F. Georgopoulos & S. D. Likothanassis, 2000. "In search of a warning strategy against exchange-rate attacks: Forecasting tactics using artificial neural networks," Discrete Dynamics in Nature and Society, Hindawi, vol. 5, pages 1-17, January.
  • Andreou, Andreas S. & Zombanakis, George A. & Georgopoulos, E. F. & Likothanassis, S. D., 2000. "In Search of a Warning Strategy Against Exchange-rate Attacks: Forecasting Tactics Using Artificial Neural Networks," MPRA Paper 18197, University Library of Munich, Germany.
• Fischer, Manfred M. & Reismann, Martin & Hlavackova-Schindler, Katerina, 2000. "Evaluating Neural Spatial Interaction. Modelling By Bootstrapping," ERSA conference papers ersa00p370, European Regional Science Association.
  • Fischer, Manfred M. & Reismann, Martin, 2000. "Evaluating Neural Spatial Interaction Modelling by Bootstrapping," MPRA Paper 77790, University Library of Munich, Germany.
• Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
  • Pereira, Robert, 2000. "Genetic Algorithm Optimisation for Finance and Investments," MPRA Paper 8610, University Library of Munich, Germany.
  • Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
• Goyal, Sanjeev & Joshi, Sumit, 2003. "Networks of collaboration in oligopoly," Games and Economic Behavior, Elsevier, vol. 43(1), pages 57-85, April.
  • Goyal, S. & Joshi, S., 2000. "Networks of Collaboration in Oligopoly," Econometric Institute Research Papers EI 9952-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Sanjeev Goyal & Sumit Joshi, 2000. "Networks of Collaboration in Oligopoly," Tinbergen Institute Discussion Papers 00-092/1, Tinbergen Institute.
• Pereira, Robert, 2000. "Genetic Algorithm Optimisation for Finance and Investments," MPRA Paper 8610, University Library of Munich, Germany.
  • Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
  • Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
• Gerardo A. Colmenares L., 2000. "Stratified/Pca: A data and variable processing method for the construction of neural network models," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 25(16), pages 45-71, January-D.
• Fischer, Manfred M. & Reismann, Martin, 2000. "Evaluating Neural Spatial Interaction Modelling by Bootstrapping," MPRA Paper 77790, University Library of Munich, Germany.
  • Fischer, Manfred M. & Reismann, Martin & Hlavackova-Schindler, Katerina, 2000. "Evaluating Neural Spatial Interaction. Modelling By Bootstrapping," ERSA conference papers ersa00p370, European Regional Science Association.
• J. F. Gu, 2000. "Wuli-Shili-Renli System Approach And Its Practice In China," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 1, pages 3-16, World Scientific Publishing Co. Pte. Ltd..
• Stanley Zionts, 2000. "Some Thoughts About Multiple Criteria Decision Making For Ordinary Decisions," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 2, pages 17-28, World Scientific Publishing Co. Pte. Ltd..
• Han-Lin Li & Jia-Jane Shuai, 2000. "A Decision Support System For Incorporating Competencies Of A Software Company," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 3, pages 31-47, World Scientific Publishing Co. Pte. Ltd..
• Minghe Sun & Ralph E. Steuer, 2000. "Quad Tree Data Structures For Use In Large–Scale Discrete Alternative Multiple Criteria Problems," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 4, pages 48-71, World Scientific Publishing Co. Pte. Ltd..
• Tamaki Tanaka, 2000. "Vector-Valued Minimax Theorems In Multicriteria Games," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 5, pages 75-99, World Scientific Publishing Co. Pte. Ltd..
• H. P. Benson & G. M. Boger, 2000. "Analysis Of An Outcome Space Formulation Of The Multiplicative Programming Problem," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 6, pages 100-122, World Scientific Publishing Co. Pte. Ltd..
• Pekka Korhonen & Guang Yuan Yu, 2000. "Quadratic Pareto Race," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 7, pages 123-142, World Scientific Publishing Co. Pte. Ltd..
  • P. Korhonen & G.Y. Yu, 1997. "Quadratic Pareto Race," Working Papers ir97058, International Institute for Applied Systems Analysis.
• G. Fandel, 2000. "Managerial Disposition Efficiency And Performance Efficiency In Large-Scale Production Projects," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 8, pages 145-156, World Scientific Publishing Co. Pte. Ltd..
• Yacov Y. Haimes, 2000. "The Art And Science Of Risk Assessment And Management," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 9, pages 157-170, World Scientific Publishing Co. Pte. Ltd..
• Milan Zeleny, 2000. "The Elimination Of Tradeoffs In Modern Business And Economics," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 10, pages 173-195, World Scientific Publishing Co. Pte. Ltd..
• Duan Li, 2000. "Time-Varying Trade-Offs In Multiobjective Dynamic Programming," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 11, pages 196-206, World Scientific Publishing Co. Pte. Ltd..
• O. I. Larichev, 2000. "Qualitative Comparison Of Multicriteria Alternatives," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 12, pages 207-224, World Scientific Publishing Co. Pte. Ltd..
• Hirotaka NAKAYAMA & Tetsuzo TANINO & Yeboon YUN, 2000. "Generalized Data Envelopment Analysis And Its Application," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 13, pages 227-248, World Scientific Publishing Co. Pte. Ltd..
• Thierry Post & Jaap Spronk, 2000. "Evaluating Productive Performance Under Uncertainty: Combining Data Envelopment Analysis, Mean-Variance Analysis, And Multi-Factor Risk Models," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 14, pages 249-269, World Scientific Publishing Co. Pte. Ltd..
• C. I. Chiang & G. H. Tzeng, 2000. "A Multiple Objective Programming Approach To Data Envelopment Analysis," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 15, pages 270-285, World Scientific Publishing Co. Pte. Ltd..
• Kyung Sam Park & Kwang-Jae Kim & Herbert Moskowitz, 2000. "Multi-Response Surface Optimization And Multi-Objective Optimization: Relationships And Directions," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 16, pages 289-303, World Scientific Publishing Co. Pte. Ltd..
• Yong Shi & Xiaowo Tang, 2000. "A State-Of-The-Art Of Mc2 Linear Programming," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 17, pages 304-330, World Scientific Publishing Co. Pte. Ltd..
• Antonie Stam & A. Pedro Duarte Silva, 2000. "Multiplicative Ratings For The Ahp," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 18, pages 331-345, World Scientific Publishing Co. Pte. Ltd..
• S. L. Liu & S. Y. Wang & K. K. Lai, 2000. "Multiple Criteria Decision Making Models For Competitive Bidding," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 19, pages 349-372, World Scientific Publishing Co. Pte. Ltd..
• Chien-Hsiung Lin & Khalid J. Siddiqui & Yi-Hsin Liu, 2000. "Multiple Objective Linear Programming To Analyze And Classify Business Information," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 20, pages 373-388, World Scientific Publishing Co. Pte. Ltd..
• Heeseok Lee & Jongwon Lee & Myung Ho Sohn & Yong Shi, 2000. "From Enterprise Network To Network Enterprise: Another Perspective Of Multiple Criteria Decision Making For Building Corporate Information System," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 21, pages 389-402, World Scientific Publishing Co. Pte. Ltd..
• Simon Cummings, 2000. "On the Use of Neural Networks for Analysing Travel Preference Data," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 1, pages 1-12, World Scientific Publishing Co. Pte. Ltd..
• Rudy Setiono & James Y. L. Thong & Chee-Sing Yap, 2000. "Extracting Rules Concerning Market Segmentation from Artificial Neural Networks," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 2, pages 13-28, World Scientific Publishing Co. Pte. Ltd..
• A. Vellido & P. J. G. Lisboa & K. Meehan, 2000. "Characterising and Segmenting the Business-to-Consumer E-Commerce Market Using Neural Networks," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 3, pages 29-54, World Scientific Publishing Co. Pte. Ltd..
• A. H. Boussabaine & M. Wanous, 2000. "A Neurofuzzy Model for Predicting Business Bankruptcy," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 4, pages 55-72, World Scientific Publishing Co. Pte. Ltd..
• Kimmo Kiviluoto & Pentti Bergius & Jyrki Maaranen, 2000. "Neural Networks for Analysis of Financial Statements," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 5, pages 73-84, World Scientific Publishing Co. Pte. Ltd..
• Mark Somers & Greg Piper, 2000. "Developments in Accurate Consumer Risk Assessment Technology," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 6, pages 85-98, World Scientific Publishing Co. Pte. Ltd..
• Inderjit Sandhu, 2000. "Strategies for Exploiting Neural Networks in Retail Finance," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 7, pages 99-111, World Scientific Publishing Co. Pte. Ltd..
• John Shawe-Taylor & Keith Howker & Phil Gosset & Mark Hyland & Herman Verrelst & Yves Moreau & Christof Stoermann & Peter Burge, 2000. "Novel Techniques for Profiling and Fraud Detection in Mobile Telecommunications," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 8, pages 113-139, World Scientific Publishing Co. Pte. Ltd..
• Khosrow Hassibi, 2000. "Detecting Payment Card Fraud with Neural Networks," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 9, pages 141-157, World Scientific Publishing Co. Pte. Ltd..
• Bernard Chartier & Thomas Spillane, 2000. "Money Laundering Detection with a Neural-Network," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 10, pages 159-172, World Scientific Publishing Co. Pte. Ltd..
• Bill Edisbury & Roger England & Stewart Hanson, 2000. "Utilising Fuzzy Logic and Neurofuzzy for Business Advantage," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 11, pages 173-194, World Scientific Publishing Co. Pte. Ltd..

1999

• Greg Tkacz & Sarah Hu, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Staff Working Papers 99-3, Bank of Canada.
• Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 424-443, May.
  • PERRON, Benoît, 1999. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," Cahiers de recherche 9901, Universite de Montreal, Departement de sciences economiques.
  • Benoit Perron, 2002. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," CIRANO Working Papers 2002s-88, CIRANO.
  • Benoit Perron, 2000. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-return Trade-off," Econometric Society World Congress 2000 Contributed Papers 1576, Econometric Society.
• Bolgot, S. & Terraza, M., 1999. "Prevision des prix a terme du cacao et modeles ARMA non-lineaires," G.R.E.Q.A.M. 99b02, Universite Aix-Marseille III.
• Ghattas, B., 1999. "Previsions par arbres de classification," G.R.E.Q.A.M. 99b03, Universite Aix-Marseille III.
• Ghattas, B., 1999. "Previsions des pics d'ozone par arbres de regression, simples et agreges par bootstrap," G.R.E.Q.A.M. 99b04, Universite Aix-Marseille III.
• Ghattas, B., 1999. "Agregation d'arbres de classification," G.R.E.Q.A.M. 99b05, Universite Aix-Marseille III.
• Lakhal, S. & Poulin, D. & Martel, A., 1999. "Vers un cadre theorique de l'entreprise reseau," Papers 99-004, Laval - Faculte des sciences de administration.
  • Lakhal, S. & Martel, A. & Poulin, D., 1999. "Vers un cadre theorique de l'entreprise reseau," Papers 1999-4, Laval - Faculte des sciences de administration.
• Oral, M. & Poulin, D. & Kettani, O., 1999. "Mondialisation, Competition, Reseautage et Decision Collective," Papers 1999-6, Laval - Faculte des sciences de administration.
• Cottrell, M. & Turova, T.S., 1999. "Use of an Hourglass Model in Neuronal Coding," Papiers d'Economie Mathématique et Applications 1999-24, Université Panthéon-Sorbonne (Paris 1).
• Robert Pereira, 1999. "Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules," Working Papers 1999.06, School of Economics, La Trobe University.
  • Robert Pereira, 1999. "Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules," Working Papers 1999.06, School of Economics, La Trobe University.
  • Pereira, Robert, 1999. "Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules," MPRA Paper 9055, University Library of Munich, Germany.
• Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 424-443, May.
  • PERRON, Benoît, 1999. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," Cahiers de recherche 9901, Universite de Montreal, Departement de sciences economiques.
  • Benoit Perron, 2002. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off," CIRANO Working Papers 2002s-88, CIRANO.
  • Benoit Perron, 2000. "Semi-Parametric Weak Instrument Regressions with an Application to the Risk-return Trade-off," Econometric Society World Congress 2000 Contributed Papers 1576, Econometric Society.
• Robert Pereira, 1999. "Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules," Working Papers 1999.06, School of Economics, La Trobe University.
  • Pereira, Robert, 1999. "Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules," MPRA Paper 9055, University Library of Munich, Germany.
  • Robert Pereira, 1999. "Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules," Working Papers 1999.06, School of Economics, La Trobe University.
• André Monteiro D'Almeida Monteiro & Dionísio Dias Carneiro & Carlos Eduardo Pedreira, 1999. "The application of clustering analysis to international private indebtedness," Textos para discussão 412, Department of Economics PUC-Rio (Brazil).
  • Monteiro Andre & Carneiro Dionisio & Pedreira Carlos, 2005. "An Application of Clustering Analysis to International Private Indebtedness," Computational Economics 0505001, University Library of Munich, Germany.
• Manfred M. Fischer & Katerina Hlavácková-Schindler & Martin Reismann, 1999. "articles: A global search procedure for parameter estimation in neural spatial interaction modelling," Papers in Regional Science, Springer;Regional Science Association International, vol. 78(2), pages 119-134.
• Pereira, Robert, 1999. "Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules," MPRA Paper 9055, University Library of Munich, Germany.
  • Robert Pereira, 1999. "Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules," Working Papers 1999.06, School of Economics, La Trobe University.
  • Robert Pereira, 1999. "Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules," Working Papers 1999.06, School of Economics, La Trobe University.
• Gottschling, Andreas & Haefke, Christian & White, Halbert, 1999. "Closed form integration of artificial neural networks with some applications," Research Notes 99-9, Deutsche Bank Research.

1998

• Täppinen, Jan, 1998. "Interest Rate Forecasting with Neural Networks," Discussion Papers 170, VATT Institute for Economic Research.
• Bolgot, S. & Meyfredi, J.-C., 1998. "Reseaux de neurones, lissage de la fonction d'actualisation et prevision des OAT demembrees: une etude empirique," G.R.E.Q.A.M. 98b04, Universite Aix-Marseille III.
• Ching‐To Albert Ma & Thomas G. Mcguire, 2002. "Network Incentives in Managed Health Care," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(1), pages 1-35, March.
  • Ching-to Albert Ma & Thomas G. McGuire, 1998. "Network Incentives in Managed Health Care," Papers 0094, Boston University - Industry Studies Programme.
  • Ma, C.-t.A. & McGuirem T.G., 1998. "Network Incentives in Managed Health Care," Papers 94, Boston University - Department of Economics.
• Ching‐To Albert Ma & Thomas G. Mcguire, 2002. "Network Incentives in Managed Health Care," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(1), pages 1-35, March.
  • Ma, C.-t.A. & McGuirem T.G., 1998. "Network Incentives in Managed Health Care," Papers 94, Boston University - Department of Economics.
  • Ching-to Albert Ma & Thomas G. McGuire, 1998. "Network Incentives in Managed Health Care," Papers 0094, Boston University - Industry Studies Programme.
• Andreou, Andreas S. & Zombanakis, George A. & Georgopoulos, E. F. & Likothanassis, S. D., 1998. "Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks," MPRA Paper 74534, University Library of Munich, Germany, revised 01 Dec 1998.
  • Andreou, Andreas S. & Zombanakis, George A. & Georgopoulos, E. F. & Likothanassis, S. D., 1998. "Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks," MPRA Paper 17764, University Library of Munich, Germany.
• Fischer, Manfred M. & Staufer, Petra, 1998. "Optimization in an Error Backpropagation Neural Network Environment with a Performance Test on a Pattern Classification Problem," MPRA Paper 77810, University Library of Munich, Germany.
• Thomas de Graaff & Raymond J.G.M. Florax & Peter Nijkamp & Aura Reggiani, 1998. "Diagnostic Tools for Nonlinearity in Spatial Models," Tinbergen Institute Discussion Papers 98-072/3, Tinbergen Institute.

1997

• William A. Barnett & A. Ronald Gallant & Melvin J. Hinich & Jochen A. Jungeilges & Daniel T. Kaplan, 2004. "A Single-Blind Controlled Competition Among Tests for Nonlinearity and Chaos," Contributions to Economic Analysis, in: Functional Structure and Approximation in Econometrics, pages 581-615, Emerald Group Publishing Limited.
  • Barnett, William A. & Gallant, A. Ronald & Hinich, Melvin J. & Jungeilges, Jochen A. & Kaplan, Daniel T. & Jensen, Mark J., 1997. "A single-blind controlled competition among tests for nonlinearity and chaos," Journal of Econometrics, Elsevier, vol. 82(1), pages 157-192.
  • William A. Barnett & A. Ronald Gallant & Melvin J. Hinich & Jochen A. Jungeilges & Daniel T. Kaplan & Mark J. Jensen, 1996. "A Single-Blind Controlled Competition among Tests for Nonlinearity and Chaos," Econometrics 9602005, University Library of Munich, Germany, revised 29 Jan 1997.
  • William Barnett & A. Ronald Gallant & Melvin J. Hinich & Jochen A. Jungeilges & Daniel T. Kaplan & Mark J. Jensen, 2012. "A Single-Blind Controlled Competition Among Tests For Nonlinearity And Chaos," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201219, University of Kansas, Department of Economics, revised Sep 2012.
• Fourneau, J.-M. & Pekergin, N. & Verchere, D., 1997. "Reseaux generalises multiclasses avec synchronisations cycliques," Papiers d'Economie Mathématique et Applications 97.77, Université Panthéon-Sorbonne (Paris 1).
• Heinemann, Maik & Lange, Carsten, 1997. "Modellierung von Preiserwartungen durch neuronale Netze," Hannover Economic Papers (HEP) dp-203, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
• Heinemann, Maik, 1997. "Convergence of Adaptive Learning and the Concept of Expectational Stability in Linear Rational Expectations Models with Multiple Equilibria," Hannover Economic Papers (HEP) dp-207, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
• Jackwerth, Jens Carsten, 1997. "Artificial Stupidity: A Reply," MPRA Paper 11636, University Library of Munich, Germany.
• Pekka Korhonen & Guang Yuan Yu, 2000. "Quadratic Pareto Race," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 7, pages 123-142, World Scientific Publishing Co. Pte. Ltd..
  • P. Korhonen & G.Y. Yu, 1997. "Quadratic Pareto Race," Working Papers ir97058, International Institute for Applied Systems Analysis.

1996

• Hill, R.J., 1996. "Measuring Inflation Using Spanning Trees," Papers 96/26, New South Wales - School of Economics.

1995

• Haefke, Christian & Helmenstein, Christian, 1995. "Forecasting Austrian IPOs: An Application of Linear and Neural Network Error-Correction Models," Economics Series 18, Institute for Advanced Studies.
• Haefke, Christian & Helmenstein, Christian, 1995. "Prediction Risk and the Forecasting of Stock Market Indexes," Economics Series 20, Institute for Advanced Studies.
• Christian Haefke & Christian Helmenstein, "undated". "Forecasting Stock Market Averages to Enhance Profitable Trading Strategies," Computing in Economics and Finance 1996 _023, Society for Computational Economics.
  • Haefke, Christian & Helmenstein, Christian, 1995. "Forecasting Stock Market Averages to Enhance Profitable Trading Strategies," Economics Series 21, Institute for Advanced Studies.
• Fischer, Manfred M. & Gopal, Sucharita & Staufer, Petra & Steinnocher, Klaus, 1995. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application," MPRA Paper 77811, University Library of Munich, Germany.
• Wojtek Kowalczyk & Rafal Weron, 1995. "Analysis of ROBECO data by neural networks," HSC Research Reports HSC/95/02, Hugo Steinhaus Center, Wroclaw University of Technology.

1994

• Fischer, Manfred M. & Gopal, Sucharita, 1994. "Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows," MPRA Paper 77822, University Library of Munich, Germany.

1993

• Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1993. "The Carnegie Conjecture: Some Empirical Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(2), pages 413-435.
  • Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1992. "The Carnegie Conjecture: Some Empirical Evidence," Working Papers 682, Princeton University, Department of Economics, Industrial Relations Section..
  • Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1992. "The Carnegie Conjecture: Some Empirical Evidence," NBER Working Papers 4118, National Bureau of Economic Research, Inc.

1992

• Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1993. "The Carnegie Conjecture: Some Empirical Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(2), pages 413-435.
  • Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1992. "The Carnegie Conjecture: Some Empirical Evidence," Working Papers 682, Princeton University, Department of Economics, Industrial Relations Section..
  • Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1992. "The Carnegie Conjecture: Some Empirical Evidence," NBER Working Papers 4118, National Bureau of Economic Research, Inc.
• Fischer, Manfred M., 1992. "Expert Systems and Artificial Neural Networks for Spatial Analysis and Modelling. Essential Components for Knowledge-Based Geographical Information Systems," MPRA Paper 77818, University Library of Munich, Germany.
• Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1993. "The Carnegie Conjecture: Some Empirical Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(2), pages 413-435.
  • Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1992. "The Carnegie Conjecture: Some Empirical Evidence," NBER Working Papers 4118, National Bureau of Economic Research, Inc.
  • Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1992. "The Carnegie Conjecture: Some Empirical Evidence," Working Papers 682, Princeton University, Department of Economics, Industrial Relations Section..
• Douglas Holtz-Eakin & David Joulfaian & Harvey Rosen, 1992. "The Carnegie Conjecture: Some Empirical Evidence," Working Papers 682, Princeton University, Department of Economics, Industrial Relations Section..

0

• Gautier M Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2014. "Trade Integration and Trade Imbalances in the European Union: A Network Perspective," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-14, January.
  • KRINGS, Gautier M. & CARPANTIER, Jean-François & dELVENNE, Jean-Charles & ,, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Discussion Papers CORE 2013056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Gautier M. Krings & Jean-Franc{c}ois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Papers 1309.4156, arXiv.org.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821137, HAL.
  • Gautier M. Krings & Jean-François Carpantier, & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," DEM Discussion Paper Series 13-22, Department of Economics at the University of Luxembourg.
  • Gautier M. Krings & Jean-Franccois Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network perspective," Working Papers hal-01821141, HAL.
  • Gautier M. Krings & Jean-François Carpantier & Jean-Charles Delvenne, 2013. "Trade integration and trade imbalances in the European Union: a network pespective," Working Papers hal-01821136, HAL.
  • KRINGS, Gautier M & CARPANTIER, Jean-François & DELVENNE, Jean-Charles, 2014. "Trade integration and trade imbalances in the European Union: a network perspective," LIDAM Reprints CORE 2619, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Money Affairs, CEMLA, vol. 0(1), pages 1-24, January-J.
  • Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 80-115, June.
  • Martha López P., 2004. "Efficient policy rule for inflation targeting in Colombia," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 80-115, June.
  • Martha López P., 2003. "Efficient Policy Rule For Inflation Targeting In Colombia," Borradores de Economia 2437, Banco de la Republica.
  • Martha López P., 2003. "Efficient Policy Rule for Inflation Targeting in Colombia," Borradores de Economia 240, Banco de la Republica de Colombia.
• José Mauricio Salazar Sáenz, 2009. "Evaluación de pronóstico de una red neuronal sobre el PIB en Colombia," Borradores de Economia 575, Banco de la Republica de Colombia.
• Camilo Serrano & Martin Hoesli, 2007. "Forecasting EREIT Returns," Swiss Finance Institute Research Paper Series 07-35, Swiss Finance Institute.
• J.B. Satinover & D. Sornette, 2008. "Anomalous Returns in a Neural Network Equity-Ranking Predictor," Swiss Finance Institute Research Paper Series 08-15, Swiss Finance Institute.
• Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Diagnosis and Prediction of Market Rebounds in Financial Markets," Papers 1003.5926, arXiv.org, revised Mar 2011.
  • Wanfeng YAN & Ryan WOODARD & Didier SORNETTE, 2010. "Diagnosis and Prediction of Market Rebounds in Financial Markets," Swiss Finance Institute Research Paper Series 10-15, Swiss Finance Institute.
• Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
  • PREMINGER, Arie & FRANCK, Raphael, 2005. "Forecasting exchange rates: a robust regression approach," LIDAM Discussion Papers CORE 2005025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • PREMINGER, Arie & FRANCK, Raphael, 2007. "Forecasting exchange rates: a robust regression approach," LIDAM Reprints CORE 1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Hoogerheide, Lennart F. & Kaashoek, Johan F. & van Dijk, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," Journal of Econometrics, Elsevier, vol. 139(1), pages 154-180, July.
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & VAN DIJK, Herman K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," LIDAM Discussion Papers CORE 2005029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & van DIJK, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," LIDAM Reprints CORE 1922, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Hoogerheide, L.F. & Kaashoek, J.F. & van Dijk, H.K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks," Econometric Institute Research Papers EI 2005-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Jermain C. Kaminski & Christian Hopp, 0. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 0, pages 1-23.
• Weidong Lin & Abderrahim Taamouti, 2023. "Machine Learning Based Portfolio Selection Under Systemic Risk," Working Papers 202311, University of Liverpool, Department of Economics.
• Tobias Götze & Marc Gürtler & Eileen Witowski, 0. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 0, pages 1-19.
• Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
• Franz Fuerst & Gianluca Marcato, "undated". "Re-thinking Commercial Real Estate Market Segmentation," Real Estate & Planning Working Papers rep-wp2010-12, Henley Business School, University of Reading.
• Fioretti, Guido, 2006. "Recognising investment opportunities at the onset of recoveries," Research in Economics, Elsevier, vol. 60(2), pages 69-84, June.
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