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

2018

• Przemysław Ryś & Robert Ślepaczuk, 2018, "Machine learning in algorithmic trading strategy optimization - implementation and efficiency," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2018-25, Dec.
• Katarzyna Hubicka & Grzegorz Marcjasz & Rafal Weron, 2018, "A note on averaging day-ahead electricity price forecasts across calibration windows," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/18/03, Jul.
• Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2018, "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/18/05, Jul.
• Rafal Weron & Florian Ziel, 2018, "Electricity price forecasting," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/18/08, Sep.
• Martinho, Vítor João Pereira Domingues, 2018, "Ranking the socioeconomic and environmental framework of European Union farms: A network analysis," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 173285.
• Steinkraus, Arne, 2018, "Rethinking Policy Evaluation – Do Simple Neural Nets Bear Comparison with Synthetic Control Method?," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 177390.
• Jahn, Malte, 2018, "Artificial neural network regression models: Predicting GDP growth," HWWI Research Papers, Hamburg Institute of International Economics (HWWI), number 185.
• Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018, "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2018-016.
• 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, Verein für Socialpolitik / German Economic Association, number 181544.

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, volume 5, issue 0, pages 218-223, September, DOI: 10.12955/cbup.v5.928.
• 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, volume 5, issue 1, pages 1-14, June, DOI: http://dx.doi.org/10.17093/alphanum.
• Ufuk Çelik & Çağatay Başarır, 2017, "The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 5, issue 1, pages 45-54, June, DOI: http://dx.doi.org/10.17093/alphanum.
• Yusuf Kuvvetli, 2017, "Returned Product Acquisition Pricing by Adaptive Neuro Fuzzy Inference System," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 5, issue 2, pages 207-214, October, DOI: http://dx.doi.org/10.17093/alphanum.
• Engin Taş, 2017, "Classification of Gene Samples Using Pair-Wise Support Vector Machines," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 5, issue 2, pages 283-292, November, DOI: http://dx.doi.org/10.17093/alphanum.
• Raymond Brummelhuis & Zhongmin Luo, 2017, "CDS Rate Construction Methods by Machine Learning Techniques," Papers, arXiv.org, number 1705.06899, May.
• Inna Strelchenko, 2017, "Modelling Of Scenarios Of The Crisis Phenomena Transfer Among Financial Markets," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", volume 3, issue 2, DOI: 10.30525/2256-0742/2017-3-2-136-140.
• Aleksey Mints, 2017, "Classification Of Tasks Of Data Mining And Data Processing In The Economy," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", volume 3, issue 3, DOI: 10.30525/2256-0742/2017-3-3-47-52.
• Mehmet OZCALICI, 2017, "Market Segmentation with Self-Organizing Maps in Banking Industry," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 11, issue 2, pages 9-30.
• Danilo Leiva-Leon, 2017, "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers, Banco de España, number 1726, Jul.
• 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, Banco de la Republica de Colombia, number 993, May, DOI: 10.32468/be.993.
• 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, volume 79, issue 4, pages 513-545, August.
• Chiranjit Chakraborty & Andreas Joseph, 2017, "Machine learning at central banks," Bank of England Staff Working Paper series, Bank of England, number 674, Sep.
• 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, volume 68, issue HS1, pages 87-106.
• Alvaro J. Riascos & Mauricio Romero & Natalia Serna, 2017, "Risk Adjustment Revisited using Machine Learning Techniques," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 15601, Mar.
• Andrés González, 2017, "Evaluación de pronósticos de modelos lineales y no lineales de la tasa de cambio de Colombia," Vniversitas Económica, Universidad Javeriana - Bogotá, volume 0, issue 0, pages 1-45.
• 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, volume 21, issue 45, pages 5-25, DOI: 10.17230/ecos.2017.45.1.
• 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, Association for Cultural Economics International, number AWP-08-2017, Sep, 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, volume 51, issue 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, volume 51, issue 3, pages 263-280.
• Charlie Joyez, 2017, "Network Structure of French Multinational Firms," Working Papers, DIAL (Développement, Institutions et Mondialisation), number DT/2017/08, Oct.
• 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, University of Paris Nanterre, EconomiX, number 2017-34.
• 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, International Journal of Economics and Financial Issues, volume 7, issue 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, volume 67, issue C, pages 355-367, DOI: 10.1016/j.econmod.2017.02.014.
• Creel, Michael, 2017, "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, volume 2, issue C, pages 36-49, DOI: 10.1016/j.ecosta.2016.11.008.
• Zhao, Yang & Li, Jianping & Yu, Lean, 2017, "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, volume 66, issue C, pages 9-16, DOI: 10.1016/j.eneco.2017.05.023.
• 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, volume 50, issue C, pages 67-80, DOI: 10.1016/j.irfa.2017.02.004.
• 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, volume 486, issue C, pages 883-894, DOI: 10.1016/j.physa.2017.05.060.
• 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, volume 0, issue 1, pages 137-147.
• Thomas R. Cook & Aaron Smalter Hall, 2017, "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 17-11, Sep, DOI: 10.18651/RWP2017-11.
• Jason Brown & Maeve Maloney & Jordan Rappaport & Aaron Smalter Hall, 2017, "How Centralized is U.S. Metropolitan Employment?," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 17-16, Nov, DOI: 10.18651/RWP2017-16.
• 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, volume 62, issue 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, volume 62, issue 3, pages 761-774, Julio-Sep.
• Karol Szafranek, 2017, "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers, Narodowy Bank Polski, number 262.
• Evgenia Vasileva, 2017, "Creating of Something from Nothing. Methodic. (Applying the Principles of Chaos and Complex Systems in a Learning Environment)," Research Papers of UNWE, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 169-186, August, DOI: 10.37075/RP.2017.2.06.
• Fajar, Muhammad & Hartini, Sri, 2017, "Inflation forecasting by hybrid singular spectrum analysis – multilayer perceptrons neural network method, case of Indonesia," MPRA Paper, University Library of Munich, Germany, number 105100, Oct, 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, University Library of Munich, Germany, number 78623, Mar.
• Brummelhuis, Raymond & Luo, Zhongmin, 2017, "CDS Rate Construction Methods by Machine Learning Techniques," MPRA Paper, University Library of Munich, Germany, number 79194, May.
• Milan Fičura, 2017, "Forecasting Stock Market Realized Variance with Echo State Neural Networks," European Financial and Accounting Journal, Prague University of Economics and Business, volume 2017, issue 3, pages 145-155, DOI: 10.18267/j.efaj.193.
• 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, volume 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, volume 67, issue 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, volume 111, issue 2, pages 743-772, May, DOI: 10.1007/s11192-017-2268-3.
• Anthony Mouraud, 2017, "Innovative time series forecasting: auto regressive moving average vs deep networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 4, issue 3, pages 282-293, March, DOI: 10.9770/jesi.2017.4.3S(4).
• León, C. & Moreno, José Fernando & Cely, Jorge, 2017, "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Discussion Paper, Tilburg University, Center for Economic Research, number 2017-009.
• León, C. & Moreno, José Fernando & Cely, Jorge, 2017, "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Other publications TiSEM, Tilburg University, School of Economics and Management, number 75d8648e-9855-4c5c-9aa9-0.
• 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, volume 42, issue 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., volume 36, issue 2, pages 109-121, March.
• 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, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/17/03, Jul.
• 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), volume 23, issue 4, pages 75-78.
• Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2017, "Tail event driven networks of SIFIs," SFB 649 Discussion Papers, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk, number 2017-004.

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, DOI: 10.15609/annaeconstat2009.123-124.0.
• Borghesi, Simone & Flori, Andrea, undated, "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," MITP: Mitigation, Innovation and Transformation Pathways, Fondazione Eni Enrico Mattei (FEEM), number 232214, DOI: 10.22004/ag.econ.232214.
• Jozwiak, Akos & Milkovics, Matyas & Lakner, Zoltan, undated, "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, volume 19, issue A, pages 1-26, DOI: 10.22004/ag.econ.240694.
• 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, volume 63, issue 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, volume 63, issue 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, volume 4, issue 2, pages 95-114, September, DOI: http://dx.doi.org/10.17093/aj.2016..
• 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, volume 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, Banco de la Republica de Colombia, number 959, Sep, DOI: 10.32468/be.959.
• Michael Creel, 2016, "Neural Nets for Indirect Inference," Working Papers, Barcelona School of Economics, number 942, Nov.
• 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, volume 24, issue 1, pages 115-145, June.
• 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, Boston College Department of Economics, number 914, Jun.
• 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- Spanish Securities Markets Commission - Research and Statistics Department, number CNMV Working Papers no. 6.
• 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, Association for Cultural Economics International, number AWP-09-2016, Sep, 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, volume 50, issue 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, volume 50, issue 3, pages 63-82.
• Vergote, Olivier, 2016, "Credit risk spillover between financials and sovereigns in the euro area during 2007-2015," Working Paper Series, European Central Bank, number 1898, Apr.
• Kok, Christoffer & Montagna, Mattia, 2016, "Multi-layered interbank model for assessing systemic risk," Working Paper Series, European Central Bank, number 1944, Aug.
• Endrész, Marianna & Skudelny, Frauke, 2016, "Crisis severity and the international trade network," Working Paper Series, European Central Bank, number 1971, Oct.
• Baruník, Jozef & Malinská, Barbora, 2016, "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, volume 164, issue C, pages 366-379, DOI: 10.1016/j.apenergy.2015.11.051.
• Kiani, Khurshid M., 2016, "On business cycle fluctuations in USA macroeconomic time series," Economic Modelling, Elsevier, volume 53, issue C, pages 179-186, DOI: 10.1016/j.econmod.2015.11.022.
• Ductor, Lorenzo & Leiva-Leon, Danilo, 2016, "Dynamics of global business cycle interdependence," Journal of International Economics, Elsevier, volume 102, issue C, pages 110-127, DOI: 10.1016/j.jinteco.2016.07.003.
• 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, volume 146, issue C, pages 40-58, DOI: 10.1016/j.inteco.2015.11.003.
• Simone Borghesi & Andrea Flori, 2016, "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," Working Papers, Fondazione Eni Enrico Mattei, number 2016.08, Jan.
• Ben R. Craig & Martin Saldias Zambrana, 2016, "Spatial Dependence and Data-Driven Networks of International Banks," Working Papers (Old Series), Federal Reserve Bank of Cleveland, number 1627, Dec.
• 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, volume 4, issue 1, pages 1-14, March.
• Leoni Eleni Oikonomikou, 2016, "Forecasting the Market Risk Premium with Artificial Neural Networks," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers, Courant Research Centre PEG, number 202, Apr.
• 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, Courant Research Centre PEG, number 203, Apr.
• Anne Peguin-Feissolle & Bilel Sanhaji, 2016, "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Post-Print, HAL, number hal-04218472, DOI: 10.15609/annaeconstat2009.123-124.0.
• 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, volume 6, issue 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, volume 6, issue 1, pages 244-253, January.
• 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, volume 10, issue 2, pages 27-32.
• Mr. Alexei P Kireyev & Andrei Leonidov, 2016, "A Network Model of Multilaterally Equilibrium Exchange Rates," IMF Working Papers, International Monetary Fund, number 2016/130, Jul.
• Ben Craig & Martín Saldías, 2016, "Spatial Dependence and Data-Driven Networks of International Banks," IMF Working Papers, International Monetary Fund, number 2016/184, Sep.
• Michel Philipp & Achim Zeileis & Carolin Strobl, 2016, "A Toolkit for Stability Assessment of Tree-Based Learners," Working Papers, Faculty of Economics and Statistics, Universität Innsbruck, number 2016-11, May.
• Florian Wickelmaier & Achim Zeileis, 2016, "Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models," Working Papers, Faculty of Economics and Statistics, Universität Innsbruck, number 2016-26, Sep.
• 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, IZA Network @ LISER, number 10008, Jun.
• Julian Hagenauer, 2016, "Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks," Journal of Geographical Systems, Springer, volume 18, issue 1, pages 1-15, January, DOI: 10.1007/s10109-015-0220-8.
• Julian Hagenauer, 2016, "Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks," Journal of Geographical Systems, Springer, volume 18, issue 1, pages 1-15, January, DOI: 10.1007/s10109-015-0220-8.
• Salehi, Mehdi & Hamidehpour, Kiana & Khadem, Hamid, 2016, "Comparison of Forecasting the Index Price Movement in Financial Institutions using Artificial Intelligence," Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی), Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, volume 9, issue 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), volume 15, issue 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, volume 61, issue 4, pages 603-628, Octubre-D.
• Daniel Goetz, 2016, "Broadband Mergers and Dynamic Bargaining: An Application to Netflix," Working Papers, NET Institute, number 16-07, Sep.
• 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, NET Institute, number 16-09, Sep.
• Monika Hadas-Dyduch & Adam P. Balcerzak & Michal Bernard Pietrzak, 2016, "Wavelet Analisis of Unemployment Rate in Visegrad Countries," Chapters, Institute of Economic Research, in: Tomas Kliestik, "16th International Scientific Conference Globalization and Its Socio-Economic Consequences. University of Zilina, The Faculty of Operation and Economi".
• Monika Hadas-Dyduch & Michal Bernard Pietrzak & Adam P. Balcerzak, 2016, "Wavelet Analysis of Unemployment Rate in Visegrad Countries," Working Papers, Institute of Economic Research, number 37/2016, Sep, revised Sep 2016.
• Gawlik, Remigiusz, 2016, "Methodological Aspects of Qualitative-Quantitative Analysis of Decision-Making Processes," MPRA Paper, University Library of Munich, Germany, number 72362, Jun.
• Basihos, Seda, 2016, "Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey," MPRA Paper, University Library of Munich, Germany, number 75553, May, revised 09 Sep 2016.
• Aviral Kumar Tiwari & Rangan Gupta & Stelios Bekiros, 2016, "Chaos in G7 Stock Markets using Over One Century of Data: A Note," Working Papers, University of Pretoria, Department of Economics, number 201678, Oct.
• 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 Эффективности В Нефтегазовой И Других Сырьевых Отраслях]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, volume 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', Department of Economics - University Roma Tre, number 0209, Oct.
• 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, International Institute of Social and Economic Sciences, number 3205849, Mar.
• 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, volume 51, issue 4, pages 1541-1556, December, DOI: 10.1007/s00181-015-1049-1.
• 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, volume 7, issue 3, pages 341-357, August, DOI: 10.1007/s13209-016-0144-7.
• Anders Bredahl Kock & Timo Teräsvirta, 2016, "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, volume 35, issue 8-10, pages 1753-1779, December, DOI: 10.1080/07474938.2015.1035163.
• 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, DOI: 10.26531/vnbu2016.238.013.
• 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, volume 41, issue 42, pages 105-132, july-dece.
• Francis Bismans & Igor N. Litvine, 2016, "Forecasting with Neural Networks Models," Working Papers of BETA, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg, number 2016-28.
• 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", volume 20, issue 1, pages 54-68.
• Muczyński Andrzej & Walacik Marek, 2016, "Neural Networks Modelling of Municipal Real Estate Market Rent Rates," Folia Oeconomica Stetinensia, Paradigm, volume 16, issue 2, pages 17-28, December, DOI: 10.1515/foli-2016-0022.
• Kaczmarczyk Paweł, 2016, "Integrated Model of Demand for Telephone Services in Terms of Microeconometrics," Folia Oeconomica Stetinensia, Paradigm, volume 16, issue 2, pages 72-83, December, DOI: 10.1515/foli-2016-0026.
• 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, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb, "Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016".
• 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, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb, "Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016".
• 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, volume 14, issue 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)," AMSE Working Papers, Aix-Marseille School of Economics, France, number 1516, Mar.
• Sihem Khemakhem & Younes Boujelbene, 2015, "Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, volume 14, issue 1, pages 60-78, March.
• Aida Krichene Abdelmoula, 2015, "Bank Credit Risk Analysis with K-Nearest-Neighbor Classifier: Case of Tunisian Banks," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, volume 14, issue 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, volume 3, issue 2, pages 65-72, December, DOI: http://dx.doi.org/10.17093/aj.2015..
• Oscar Claveria & Enric Monte & Salvador Torra, 2015, "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 201502, Jan, 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, University of Barcelona, Regional Quantitative Analysis Group, number 201503, Jan, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015, "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 201506, Jan, 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, University of Barcelona, Regional Quantitative Analysis Group, number 201508, Mar, revised Mar 2015.
• Jozef Barunik & Barbora Malinska, 2015, "Forecasting the term structure of crude oil futures prices with neural networks," Papers, arXiv.org, number 1504.04819, Apr.
• 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, volume 9, issue 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, volume 60, issue 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, Bank for International Settlements, in: Bank for International Settlements, "Indicators to support monetary and financial stability analysis: data sources and statistical methodologies".
• Zekić-Sušac Marijana & Has Adela, 2015, "Data Mining as Support to Knowledge Management in Marketing," Business Systems Research, Paradigm, volume 6, issue 2, pages 18-30, September, DOI: 10.1515/bsrj-2015-0008.
• Margherita Comola & Mariapia Mendola, 2015, "Formation of Migrant Networks," Scandinavian Journal of Economics, Wiley Blackwell, volume 117, issue 2, pages 592-618, April.
• 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, Banco Central de Bolivia, number 2015/01, Oct.
• 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.
• Lorenzo Ductor & Danilo Leiva-Leon, 2015, "Dynamics of Global Business Cycles Interdependence," Working Papers Central Bank of Chile, Central Bank of Chile, number 763, Jul.
• 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- Spanish Securities Markets Commission - Research and Statistics Department, number CNMV Working Papers no 59.
• Åžebnem KOLTAN YILMAZ & M. Mustafa YÃœCEL, 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, volume 2, issue 2, pages 1-15, February, DOI: 10.17740/eas.stat.2015-V2-01.
• Astebro , Thomas & Akdemir , Canan & Elhedhli , Samir, 2015, "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," HEC Research Papers Series, HEC Paris, number 1097, Oct.
• Bekiros, Stelios D., 2015, "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, volume 30, issue C, pages 34-49, DOI: 10.1016/j.jempfin.2014.11.002.
• 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, volume 47, issue C, pages 37-41, DOI: 10.1016/j.eneco.2014.10.009.
• 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, volume 45, issue C, pages 37-43, DOI: 10.1016/j.resourpol.2015.03.004.
• 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, volume 34, issue C, pages 273-292, DOI: 10.1016/j.pacfin.2015.03.002.
• 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, volume 33, issue C, pages 75-98, DOI: 10.1016/j.ribaf.2014.07.002.
• 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, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2015-33, Nov.
• 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, volume 65, issue 2, pages 144-166, April.
• Jozef Barunik & Barbora Malinska, 2015, "Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2015/25, Nov, 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), volume 0, issue 1, pages 71-89, May.
• Margherita Comola & M. Mendola, 2015, "The Formation of Migrant Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers), HAL, number hal-00977544, Apr, DOI: 10.1111/sjoe.12093.
• Margherita Comola & M. Mendola, 2015, "The Formation of Migrant Networks," Post-Print, HAL, number hal-00977544, Apr, DOI: 10.1111/sjoe.12093.
• Margherita Comola & M. Mendola, 2015, "The Formation of Migrant Networks," PSE-Ecole d'économie de Paris (Postprint), HAL, number hal-00977544, Apr, DOI: 10.1111/sjoe.12093.
• Samir Elhedhli & Canan Akdemir & Thomas Astebro, 2015, "Classification Models Via Tabu Search: An Application to Early Stage Venture Classification," Working Papers, HAL, number hal-02002758, Jul.
• Anne Péguin-Feissolle & Bilel Sanhaji, 2015, "Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)," Working Papers, HAL, number halshs-01133751, Mar.
• Green, Rikard, 2015, "A Power Market Forward Curve with Hydrology Dependence An Approach based on Artificial Neural Networks," Knut Wicksell Working Paper Series, Lund University, Knut Wicksell Centre for Financial Studies, number 2015/1, Jan.
• 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), volume 21, issue 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, Indira Gandhi Institute of Development Research, Mumbai, India, number 2015-020, Jul.
• 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, Faculty of Economics and Statistics, Universität Innsbruck, number 2015-10, Sep.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015, "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 201502, Jan, 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, University of Barcelona, Research Institute of Applied Economics, number 201503, Jan, revised Jan 2015.
• Oscar Claveria & Enric Monte & Salvador Torra, 2015, "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 201507, Jan, 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, University of Barcelona, Research Institute of Applied Economics, number 201511, Mar, revised Mar 2015.
• 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, volume 17, issue 2, pages 107-135, April, DOI: 10.1007/s10109-015-0210-x.
• 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), volume 14, issue 2, pages 28-157.
• Wenyun Tang & Lin Cheng, 2015, "Analyzing Multiday Route Choice Behavior using GPS Data," Working Papers, University of Minnesota: Nexus Research Group, number 000135, DOI: 10.1177/1687814016633030.
• Goldstone, Robert L., 2015, "Homo Economicus and Homo Sapiens," Review of Behavioral Economics, now publishers, volume 2, issue 1-2, pages 77-87, July, DOI: 10.1561/105.00000019.
• Tomasz Jasinski & Agnieszka Scianowska, 2015, "Security Assessment And Optimization Of Energy Supply (Neural Networks Approach)," Oeconomia Copernicana, Institute of Economic Research, volume 6, issue 2, pages 129-141, June, DOI: 10.12775/OeC.2015.016.
• Kulaksizoglu, Tamer, 2015, "Measuring the Core Inflation in Turkey with the SM-AR Model," MPRA Paper, University Library of Munich, Germany, number 62653, Mar.
• Matkovskyy, Roman & Bouraoui, Taoufik & Hammami, Helmi, 2015, "Estimation and prediction of an Index of Financial Safety of Tunisia," MPRA Paper, University Library of Munich, Germany, number 74573, revised 2016.
• Marcos Álvarez-Díaz & Rangan Gupta, 2015, "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers, University of Pretoria, Department of Economics, number 201512, Mar.
• 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, University of Pretoria, Department of Economics, number 201548, Jun.
• 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, volume 7, issue 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, volume 0, issue 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, volume 55, issue 1, pages 187-220, October, DOI: 10.1007/s00168-015-0679-8.
• 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, volume 30, issue 3, pages 821-843, September, DOI: 10.1007/s00180-014-0543-9.
• Harun Özkan & M. Yazgan, 2015, "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, volume 48, issue 2, pages 609-626, March, DOI: 10.1007/s00181-013-0793-3.
• 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, Tinbergen Institute, number 15-125/III, Nov.
• Kim Ristolainen, 2015, "Were the Scandinavian Banking Crises Predictable? A Neural Network Approach," Discussion Papers, Aboa Centre for Economics, number 99, Jan.
• 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, DOI: 10.26531/vnbu2015.234.052.
• Bilal Zorić, Alisa, 2015, "Case Study in Banking Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb, "Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015".