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Big Data: New Tricks for Econometrics

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As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Software for Research
    by Anton Tarasenko in Economics and Development on 2016-01-15 01:06:24

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography for Economics:
  1. > Econometrics > Big Data

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Cited by:

  1. Green, Gareth & Richards, Timothy, 2016. "Interpreting Results of Demand Estimation from Machine Learning Models," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236147, Agricultural and Applied Economics Association.
  2. Monge, Manuel & Poza, Carlos & Borgia, Sofía, 2022. "A proposal of a suspicion of tax fraud indicator based on Google trends to foresee Spanish tax revenues," International Economics, Elsevier, vol. 169(C), pages 1-12.
  3. Matthew A. Cole & Robert J R Elliott & Bowen Liu, 2020. "The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 553-580, August.
  4. Mashabela, Juliet & Raputsoane, Leroi, 2018. "The behaviour of disaggregated transitory and potential output over the economic cycle," MPRA Paper 84422, University Library of Munich, Germany.
  5. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
  6. Mr. Andrew J Tiffin, 2016. "Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon," IMF Working Papers 2016/056, International Monetary Fund.
  7. Ning Xu & Jian Hong & Timothy C. G. Fisher, 2016. "Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression," Papers 1610.05448, arXiv.org.
  8. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
  9. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
  10. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
  11. Haskamp, Ulrich, 2017. "Improving the forecasts of European regional banks' profitability with machine learning algorithms," Ruhr Economic Papers 705, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  12. Leroi RAPUTSOANE, 2016. "Real Effective Exchange Rates Comovements and the South African Currency," Journal of Economics Library, KSP Journals, vol. 3(1), pages 57-68, March.
  13. Lane, Julia I. & Owen-Smith, Jason & Rosen, Rebecca F. & Weinberg, Bruce A., 2015. "New linked data on research investments: Scientific workforce, productivity, and public value," Research Policy, Elsevier, vol. 44(9), pages 1659-1671.
  14. Thomas Pave Sohnesen & Niels Stender, 2017. "Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment," Poverty & Public Policy, John Wiley & Sons, vol. 9(1), pages 118-133, March.
  15. Carstensen, Kai & Bachmann, Rüdiger & Schneider, Martin & Lautenbacher, Stefan, 2018. "Uncertainty is Change," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181572, Verein für Socialpolitik / German Economic Association.
  16. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
  17. Massimiliano Caporin & Mikhail Stolbov & Maria Shchepeleva, 2022. "What drives the expansion of research on banking crises? Cross-country evidence," Applied Economics, Taylor & Francis Journals, vol. 54(52), pages 6054-6064, November.
  18. 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.
  19. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
  20. Daniele Guariso, 2018. "Terrorist Attacks and Immigration Rhetoric: A Natural Experiment on British MPs," Working Paper Series 1218, Department of Economics, University of Sussex Business School.
  21. Shin Oblander & Daniel Minh McCarthy, 2023. "Frontiers: Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19," Marketing Science, INFORMS, vol. 42(5), pages 839-852, September.
  22. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
  23. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  24. René Böheim & Philipp Stöllinger, 2021. "Decomposition of the gender wage gap using the LASSO estimator," Applied Economics Letters, Taylor & Francis Journals, vol. 28(10), pages 817-828, June.
  25. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
  26. Abigail N. Devereaux, 2019. "The nudge wars: A modern socialist calculation debate," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 32(2), pages 139-158, June.
  27. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
  28. Hand, Michael S. & Thompson, Matthew P. & Calkin, David E., 2016. "Examining heterogeneity and wildfire management expenditures using spatially and temporally descriptive data," Journal of Forest Economics, Elsevier, vol. 22(C), pages 80-102.
  29. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
  30. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
  31. Ivan Ajdukovic & Sylvain Max & Rodolphe Perchot & Eli Spiegelman, 2018. "The Economic Psychology of Gabriel Tarde: Something new for behavioral economics?," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 2(1), pages 5-11, March.
  32. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
  33. Chengyan Gu, 2023. "Market segmentation and dynamic price discrimination in the U.S. airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 338-361, October.
  34. Ben Vinod, 2016. "Big Data in the travel marketplace," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 352-359, October.
  35. Jorge Mejia & Shawn Mankad & Anandasivam Gopal, 2019. "A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections," Information Systems Research, INFORMS, vol. 30(4), pages 1363-1386, December.
  36. Tejas Ramdas & Martin T. Wells, 2024. "Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets," Papers 2409.05192, arXiv.org.
  37. Guillaume Belly & Lukas Boeckelmann & Carlos Mateo Caicedo Graciano & Alberto Di Iorio & Klodiana Istrefi & Vasileios Siakoulis & Arthur Stalla‐Bourdillon, 2023. "Forecasting sovereign risk in the Euro area via machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 657-684, April.
  38. Gavoille, Nicolas & Zasova, Anna, 2023. "What we pay in the shadows: Labor tax evasion, minimum wage hike and employment," Journal of Public Economics, Elsevier, vol. 228(C).
  39. Ali Namaki & Reza Eyvazloo & Shahin Ramtinnia, 2023. "A systematic review of early warning systems in finance," Papers 2310.00490, arXiv.org.
  40. Amarda Cano, 2021. "Evolution of Public Debt in Albania during 1990-2017 and its impact on the Economic Growth," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 4, ejme_v4_i.
  41. 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.
  42. Leroi RAPUTSOANE, 2015. "Alternative Measures of Credit Extension for Countercyclical Buffer Decisions in South Africa," Turkish Economic Review, KSP Journals, vol. 2(4), pages 210-221, December.
  43. León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020. "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
  44. Edward McFowland III & Sriram Somanchi & Daniel B. Neill, 2018. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection," Papers 1803.09159, arXiv.org, revised May 2023.
  45. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
  46. Achten, Sandra & Lessmann, Christian, 2020. "Spatial inequality, geography and economic activity," World Development, Elsevier, vol. 136(C).
  47. Steen Nielsen, 2020. "Management accounting and the idea of machine learning," Economics Working Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
  48. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
  49. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
  50. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2021. "Local mortality estimates during the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1189-1217, October.
  51. Wenbo Wu & Jiaqi Chen & Liang Xu & Qingyun He & Michael L. Tindall, 2019. "A statistical learning approach for stock selection in the Chinese stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-18, December.
  52. Bonnet, Céline & Richards, Timothy J., 2016. "Models of Consumer Demand for Differentiated Products," TSE Working Papers 16-741, Toulouse School of Economics (TSE).
  53. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
  54. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
  55. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
  56. Rickard Nyman & Paul Ormerod, 2017. "Predicting Economic Recessions Using Machine Learning Algorithms," Papers 1701.01428, arXiv.org.
  57. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
  58. Isaiah Hull & Anna Grodecka-Messi, 2022. "Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach," Papers 2203.14751, arXiv.org.
  59. Clarke, Damian & Torres, Nicolás Paris & Villena-Roldan, Benjamin, 2023. "(Frisch-Waugh-Lovell)' On the Estimation of Regression Models by Row," IZA Discussion Papers 16630, Institute of Labor Economics (IZA).
  60. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  61. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  62. Micevska, Maja, 2021. "Revisiting forced migration: A machine learning perspective," European Journal of Political Economy, Elsevier, vol. 70(C).
  63. Jiaqi Chen & Michael Tindall & Wenbo Wu, 2016. "Hedge Fund Return Prediction and Fund Selection: A Machine-Learning Approach," Occasional Papers 16-4, Federal Reserve Bank of Dallas.
  64. Emrich Eike & Pierdzioch Christian, 2016. "Public Goods, Private Consumption, and Human Capital: Using Boosted Regression Trees to Model Volunteer Labour Supply," Review of Economics, De Gruyter, vol. 67(3), pages 263-283, December.
  65. Prüfer, Jens & Prüfer, Patricia, 2019. "Data Science for Entrepreneurship Research : Studying Demand Dynamics for Entrepreneurial Skills in the Netherlands," Other publications TiSEM 83a4ca9e-c0cd-4786-ac8c-9, Tilburg University, School of Economics and Management.
  66. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
  67. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
  68. Oz Shy, 2020. "Alternative Methods for Studying Consumer Payment Choice," FRB Atlanta Working Paper 2020-8, Federal Reserve Bank of Atlanta.
  69. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
  70. Khudri, Md Mohsan & Hussey, Andrew, 2024. "Breastfeeding and Child Development Outcomes across Early Childhood and Adolescence: Doubly Robust Estimation with Machine Learning," IZA Discussion Papers 17080, Institute of Labor Economics (IZA).
  71. Stelios Michalopoulos & Elias Papaioannou, 2018. "Spatial Patterns of Development: A Meso Approach," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 383-410, August.
  72. Grazia Cecere & Thierry Pénard, 2020. "Introduction to the Special Issue: “From The digital economy to the digitalization of the economy”," Revue d'économie industrielle, De Boeck Université, vol. 0(4), pages 11-17.
  73. Brunori, Paolo & Salas-Rojo, Pedro & Verme, Paolo, 2022. "Estimating Inequality with Missing Incomes," GLO Discussion Paper Series 1138, Global Labor Organization (GLO).
  74. Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
  75. Hughes, Neal & Soh, Wei Ying & Lawson, Kenton & Lu, Michael, 2022. "Improving the performance of micro-simulation models with machine learning: The case of Australian farms," Economic Modelling, Elsevier, vol. 115(C).
  76. Paolo Brunori & Vito Peragine & Laura Serlenga, 2019. "Upward and downward bias when measuring inequality of opportunity," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(4), pages 635-661, April.
  77. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
  78. Omid Zamani & Thomas Bittmann & Jens‐Peter Loy, 2024. "Does the internet bring food prices closer together? Exploring search engine query data in Iran," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(2), pages 688-715, June.
  79. Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.
  80. Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2022. "Artificial intelligence in the field of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2055-2084, April.
  81. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
  82. Rohan Arora & Chen Fan & Guillaume Ouellet Leblanc, 2019. "Liquidity Management of Canadian Corporate Bond Mutual Funds: A Machine Learning Approach," Staff Analytical Notes 2019-7, Bank of Canada.
  83. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
  84. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
  85. 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).
  86. Panayotis Giannakouros & Lihua Chen, 2018. "A problem-solving approach to data analysis for economics," Forum for Social Economics, Taylor & Francis Journals, vol. 47(1), pages 87-114, January.
  87. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
  88. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
  89. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
  90. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
  91. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
  92. Vittoria La Serra & Emiliano Svezia, 2024. "A supervised record linkage approach for anomaly detection in insurance assets granular data," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4181-4205, October.
  93. Masci, Chiara & Johnes, Geraint & Agasisti, Tommaso, 2018. "Student and school performance across countries: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1072-1085.
  94. Pablo Picardo, 2019. "Predicción de precios de vivienda: Aprendizaje estadístico con datos de oferta y transacciones para la ciudad de Montevideo," Documentos de trabajo 2019002, Banco Central del Uruguay.
  95. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
  96. Evgeniy M. Ozhegov & Daria Teterina, 2018. "The Ensemble Method For Censored Demand Prediction," HSE Working papers WP BRP 200/EC/2018, National Research University Higher School of Economics.
  97. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
  98. Uwe Deichmann & Aparajita Goyal & Deepak Mishra, 2016. "Will digital technologies transform agriculture in developing countries?," Agricultural Economics, International Association of Agricultural Economists, vol. 47(S1), pages 21-33, November.
  99. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  100. Bui, Dien Giau & Kong, De-Rong & Lin, Chih-Yung & Lin, Tse-Chun, 2023. "Momentum in machine learning: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
  101. Nicholas Berente & Stefan Seidel & Hani Safadi, 2019. "Research Commentary—Data-Driven Computationally Intensive Theory Development," Service Science, INFORMS, vol. 30(1), pages 50-64, March.
  102. Nathan, Max & Rosso, Anna, 2015. "Mapping digital businesses with big data: Some early findings from the UK," Research Policy, Elsevier, vol. 44(9), pages 1714-1733.
  103. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
  104. Newell, Richard G. & Prest, Brian C. & Sexton, Steven E., 2021. "The GDP-Temperature relationship: Implications for climate change damages," Journal of Environmental Economics and Management, Elsevier, vol. 108(C).
  105. Jing Liang & Yueming Qiu & Bo Xing, 2021. "Social Versus Private Benefits of Energy Efficiency Under Time-of-Use and Increasing Block Pricing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(1), pages 43-75, January.
  106. Lennon H. T. Choy & Winky K. O. Ho, 2023. "The Use of Machine Learning in Real Estate Research," Land, MDPI, vol. 12(4), pages 1-15, March.
  107. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
  108. 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.
  109. Bo Yan & Mengru Liang & Yinxin Zhao, 2024. "Market sentiment and price dynamics in weak markets: A comprehensive empirical analysis of the soybean meal option market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 744-766, May.
  110. Feldman, Maryann & Kenney, Martin & Lissoni, Francesco, 2015. "The New Data Frontier," Research Policy, Elsevier, vol. 44(9), pages 1629-1632.
  111. Wendy C.Y. Li & Makoto Nirei & Kazufumi Yamana, 2018. "Value of Data: There’s No Such Thing as a Free Lunch in the Digital Economy," BEA Working Papers 0164, Bureau of Economic Analysis.
  112. Ron Tidhar & Kathleen M. Eisenhardt, 2020. "Get rich or die trying… finding revenue model fit using machine learning and multiple cases," Strategic Management Journal, Wiley Blackwell, vol. 41(7), pages 1245-1273, July.
  113. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
  114. Benjamin David, 2014. "On the information and communication technologies - productivity nexus: a long-lasting adjustment period," Working Papers hal-04141282, HAL.
  115. Erik Nelson & John Fitzgerald & Nathan Tefft, 2019. "The distributional impact of a green payment policy for organic fruit," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-25, February.
  116. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
  117. Castellani, Marco, 2019. "Does culture matter for the economic performance of countries? An overview of the literature," Journal of Policy Modeling, Elsevier, vol. 41(4), pages 700-717.
  118. David, Benjamin, 2017. "Computer technology and probable job destructions in Japan: An evaluation," Journal of the Japanese and International Economies, Elsevier, vol. 43(C), pages 77-87.
  119. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
  120. Dylan Brewer & Alyssa Carlson, 2024. "Addressing sample selection bias for machine learning methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 383-400, April.
  121. Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2022. "How effective is carbon pricing?—A machine learning approach to policy evaluation," Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
  122. Alessandro Sontuoso & Sudeep Bhatia, 2021. "A notion of prominence for games with natural‐language labels," Quantitative Economics, Econometric Society, vol. 12(1), pages 283-312, January.
  123. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "Are precious metals a hedge against exchange-rate movements? An empirical exploration using bayesian additive regression trees," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 27-38.
  124. Chen, Shunqin & Guo, Zhengfeng & Zhao, Xinlei, 2021. "Predicting mortgage early delinquency with machine learning methods," European Journal of Operational Research, Elsevier, vol. 290(1), pages 358-372.
  125. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
  126. Marina Johnson & Rashmi Jain & Peggy Brennan-Tonetta & Ethne Swartz & Deborah Silver & Jessica Paolini & Stanislav Mamonov & Chelsey Hill, 2021. "Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(3), pages 197-217, September.
  127. Akachi, Yoko & Canning, David, 2015. "Inferring the economic standard of living and health from cohort height: Evidence from modern populations in developing countries," Economics & Human Biology, Elsevier, vol. 19(C), pages 114-128.
  128. Daskalova, Vessela & Vriend, Nicolaas J., 2020. "Categorization and coordination," European Economic Review, Elsevier, vol. 129(C).
  129. Creel, Michael, 2017. "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, vol. 2(C), pages 36-49.
  130. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
  131. Khan, Muhammad Asif & Segovia, Juan E.Trinidad & Bhatti, M.Ishaq & Kabir, Asif, 2023. "Corporate vulnerability in the US and China during COVID-19: A machine learning approach," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
  132. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2018. "Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 89-110, National Bureau of Economic Research, Inc.
  133. Rickard Nyman & Paul Ormerod, 2020. "Understanding the Great Recession Using Machine Learning Algorithms," Papers 2001.02115, arXiv.org.
  134. Aaron Chalfin & Benjamin Hansen & Jason Lerner & Lucie Parker, 2019. "Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City," NBER Working Papers 25798, National Bureau of Economic Research, Inc.
  135. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.
  136. Griffin, John M. & Kruger, Samuel & Maturana, Gonzalo, 2021. "What drove the 2003–2006 house price boom and subsequent collapse? Disentangling competing explanations," Journal of Financial Economics, Elsevier, vol. 141(3), pages 1007-1035.
  137. Koffi Dumor & Li Yao, 2019. "Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
  138. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
  139. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
  140. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  141. Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
  142. Sunčica Rogić & Ljiljana Kašćelan & Vladimir Kašćelan & Vladimir Đurišić, 2022. "Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution," Information Technology and Management, Springer, vol. 23(4), pages 315-333, December.
  143. Muhammed Sehid Gorus & Erdal Tanas Karagol, 2023. "Factors affecting per capita ecological footprint in OECD countries: Evidence from machine learning techniquesa," Energy & Environment, , vol. 34(7), pages 2601-2618, November.
  144. Stolbov, Mikhail & Shchepeleva, Maria, 2020. "What predicts the legal status of cryptocurrencies?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 273-291.
  145. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo & Rocha, Ana Paula, 2019. "Deep learning in exchange markets," Information Economics and Policy, Elsevier, vol. 47(C), pages 38-51.
  146. Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
  147. Lulin Xu & Zhongwu Li, 2021. "A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 617-637, February.
  148. Eakin, Hallie & Appendini, Kirsten & Sweeney, Stuart & Perales, Hugo, 2015. "Correlates of Maize Land and Livelihood Change Among Maize Farming Households in Mexico," World Development, Elsevier, vol. 70(C), pages 78-91.
  149. Ignaciuk, Ada & Ilicic, Joanna & Asprooth, Lauren & Sitko, Nicholas J. & Bernard, Angela & Maggio, Giuseppe & Tubiello, Francesco N. & Mueller, Marc, 2021. "Progress towards sustainable agriculture – Drivers of change," FAO Agricultural Development Economics Technical Study 319833, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
  150. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 467-515, May.
  151. Paolo Brunori & Paul Hufe & Daniel Gerszon Mahler, 2017. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees," Working Papers - Economics wp2017_18.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
  152. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
  153. Benjamin David, 2014. "On the information and communication technologies - productivity nexus: a long-lasting adjustment period," EconomiX Working Papers 2014-60, University of Paris Nanterre, EconomiX.
  154. Marian Socoliuc & Cristina-Gabriela Cosmulese & Marius-Sorin Ciubotariu & Svetlana Mihaila & Iulia-Diana Arion & Veronica Grosu, 2020. "Sustainability Reporting as a Mixture of CSR and Sustainable Development. A Model for Micro-Enterprises within the Romanian Forestry Sector," Sustainability, MDPI, vol. 12(2), pages 1-34, January.
  155. 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.
  156. Wolfram Höpken & Tobias Eberle & Matthias Fuchs & Maria Lexhagen, 2019. "Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden," Information Technology & Tourism, Springer, vol. 21(1), pages 45-62, March.
  157. Joyce P Jacobsen & Laurence M Levin & Zachary Tausanovitch, 2016. "Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists’ Predictions," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 42(3), pages 387-398, June.
  158. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
  159. Damien Échevin & Qing Li & Marc-André Morin, 2017. "Hospital Readmission is Highly Predictable from Deep Learning," Cahiers de recherche 1705, Chaire de recherche Industrielle Alliance sur les enjeux économiques des changements démographiques.
  160. Sebastian Fossati & Joseph Marchand, 2024. "First to $15: Alberta’s Minimum Wage Policy on Employment by Wages, Ages, and Places," ILR Review, Cornell University, ILR School, vol. 77(1), pages 119-142, January.
  161. David Easley & Eleonora Patacchini & Christopher Rojas, 2020. "Multidimensional diffusion processes in dynamic online networks," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
  162. Kässi, Otto & Lehdonvirta, Vili, 2018. "Online labour index: Measuring the online gig economy for policy and research," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 241-248.
  163. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
  164. Levi Boxell, 2015. "K-fold Cross-Validation and the Gravity Model of Bilateral Trade," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 43(2), pages 289-300, June.
  165. Pasidis, Ilias, 2019. "Congestion by accident? A two-way relationship for highways in England," Journal of Transport Geography, Elsevier, vol. 76(C), pages 301-314.
  166. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
  167. Márton Gosztonyi & Csákné Filep Judit, 2022. "Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
  168. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
  169. Xu, Xiaofeng & Liu, Zhiting & Liu, Wenzhi & Pei, Chuantao & Wu, Xiangfan & Nie, Zhengya, 2024. "A sustainable development benchmarking framework for energy companies based on topic mining and knowledge graph: The case of oil and gas industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
  170. Bolin Lei & Yuping Song, 2024. "Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1706-1730, August.
  171. Moramarco, Domenico & Brunori, Paolo & Salas Rojo, Pedro, 2024. "Biases in inequality of opportunity estimates: measures and solutions," LSE Research Online Documents on Economics 125442, London School of Economics and Political Science, LSE Library.
  172. Achim Ahrens, 2015. "Civil conflicts in Africa: Climate, economic shocks, nighttime lights and spill-over effects," SEEC Discussion Papers 1501, Spatial Economics and Econometrics Centre, Heriot Watt University.
  173. Fink, Carsten & Helmers, Christian & Ponce, Carlos J., 2018. "Trademark squatters: Theory and evidence from Chile," International Journal of Industrial Organization, Elsevier, vol. 59(C), pages 340-371.
  174. Prince, Jeffrey T., 2019. "A paradigm for assessing the scope and performance of predictive analytics," Information Economics and Policy, Elsevier, vol. 47(C), pages 7-13.
  175. Steve J. Bickley & Benno Torgler, 2021. "Behavioural Economics, What Have we Missed? Exploring “Classical” Behavioural Economics Roots in AI, Cognitive Psychology, and Complexity Theory," CREMA Working Paper Series 2021-21, Center for Research in Economics, Management and the Arts (CREMA).
  176. Madio, Leonardo & Principe, Francesco, 2023. "Who supports liberal policies? A tale of two referendums in Italy," Economics Letters, Elsevier, vol. 232(C).
  177. Krüger, Jens J. & Rhiel, Mathias, 2016. "Determinants of ICT infrastructure: A cross-country statistical analysis," Darmstadt Discussion Papers in Economics 228, Darmstadt University of Technology, Department of Law and Economics.
  178. Josh Siepel & Marcus Dejardin, 2020. "How do we measure firm performance? A review of issues facing entrepreneurship researchers," Working Papers halshs-02571478, HAL.
  179. Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.
  180. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
  181. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
  182. Jeffrey C. Chen & Abe Dunn & Kyle Hood & Alexander Driessen & Andrea Batch, 2019. "Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 373-402, National Bureau of Economic Research, Inc.
  183. James T. Bang & Atin Basuchoudhary & Aniruddha Mitra, 2021. "Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning," Games, MDPI, vol. 12(3), pages 1-20, June.
  184. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
  185. Selod,Harris & Soumahoro,Souleymane, 2020. "Big Data in Transportation : An Economics Perspective," Policy Research Working Paper Series 9308, The World Bank.
  186. Takayuki Morimoto & Yoshinori Kawasaki, 2017. "Forecasting Financial Market Volatility Using a Dynamic Topic Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(3), pages 149-167, September.
  187. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
  188. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
  189. Xie, Wen-Jie & Wei, Na & Zhou, Wei-Xing, 2023. "An interpretable machine-learned model for international oil trade network," Resources Policy, Elsevier, vol. 82(C).
  190. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
  191. Mendolia, Silvia & Siminski, Peter, 2017. "Is education the mechanism through which family background affects economic outcomes? A generalised approach to mediation analysis," Economics of Education Review, Elsevier, vol. 59(C), pages 1-12.
  192. Jens Prüfer & Patricia Prüfer, 2018. "Data science for institutional and organizational economics," Chapters, in: Claude Ménard & Mary M. Shirley (ed.), A Research Agenda for New Institutional Economics, chapter 28, pages 248-259, Edward Elgar Publishing.
  193. Smith, Gary, 2019. "The Paradox of Big Data," Economics Department, Working Paper Series 1003, Economics Department, Pomona College, revised 04 Jun 2019.
  194. Chong, Terence Tai Leung & Wu, Zhang & Liu, Yuchen, 2019. "Market Reaction to iPhone Rumors," MPRA Paper 92014, University Library of Munich, Germany.
  195. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
  196. Daisuke Miyakawa & Kohei Shintani, 2020. "Disagreement between Human and Machine Predictions," IMES Discussion Paper Series 20-E-11, Institute for Monetary and Economic Studies, Bank of Japan.
  197. Wen Lin, 2023. "The effect of product quantity on willingness to pay: A meta‐regression analysis of beef valuation studies," Agribusiness, John Wiley & Sons, Ltd., vol. 39(3), pages 646-663, July.
  198. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
  199. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
  200. Heiko Kirchhain & Jan Mutl & Joachim Zietz, 2020. "The Impact of Exogenous Shocks on House Prices: the Case of the Volkswagen Emissions Scandal," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 587-610, May.
  201. Task Force Members Include: Lilli Japec & Frauke Kreuter & Marcus Berg & Paul Biemer & Paul Decker & Cliff Lampe & Julia Lane & Cathy O'Neil & Abe Usher, "undated". "AAPOR Report on Big Data," Mathematica Policy Research Reports 4eb9b798fd5b42a8b53a9249c, Mathematica Policy Research.
  202. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
  203. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
  204. Poza, Carlos & Monge, Manuel, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, Elsevier, vol. 163(C), pages 163-175.
  205. Pelin Demirel & Qian Cher Li & Francesco Rentocchini & J. Pawan Tamvada, 2019. "Born to be green: new insights into the economics and management of green entrepreneurship," Small Business Economics, Springer, vol. 52(4), pages 759-771, April.
  206. Nicodemo, Catia & Satorra, Albert, 2020. "Exploratory Data Analysis on Large Data Sets: The Example of Salary Variation in Spanish Social Security Data," IZA Discussion Papers 13459, Institute of Labor Economics (IZA).
  207. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
  208. Olga Takács & János Vincze, 2020. "The gender-dependent structure of wages in Hungary: results using machine learning techniques," CERS-IE WORKING PAPERS 2044, Institute of Economics, Centre for Economic and Regional Studies.
  209. van Dijk Herman K., 2024. "Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 155-176, April.
  210. Ryan Engstrom & David Newhouse & Vidhya Soundararajan, 2020. "Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
  211. 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).
  212. Francesco Bloise & Paolo Brunori & Patrizio Piraino, 2021. "Estimating intergenerational income mobility on sub-optimal data: a machine learning approach," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 643-665, December.
  213. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
  214. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
  215. Cassetta, Ernesto & Meleo, Linda & Monarca, Umberto & Nava, Consuelo R., 2017. "Is the answer blowin' in the wind (auctions)? An assessment of Italian auction procedures to promote onshore wind energy," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201709, University of Turin.
  216. Vincze, János & Takács, Olga, 2018. "Bérelőrejelzések - prediktorok és tanulságok [Wage forecasts predictors and lessons]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 592-618.
  217. Schiltz, Fritz & Sestito, Paolo & Agasisti, Tommaso & De Witte, Kristof, 2018. "The added value of more accurate predictions for school rankings," Economics of Education Review, Elsevier, vol. 67(C), pages 207-215.
  218. Miao Liu, 2022. "Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 607-651, May.
  219. Jurgen A. Doornik & David F. Hendry & Steve Cook, 2015. "Statistical model selection with “Big Data”," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1045216-104, December.
  220. Gao, Yanyan & Zhang, Lin & Nan, Yongqing, 2023. "Travel to breathe the fresh air? Big data evidence on the short-term migration effect of air pollution from China," China Economic Review, Elsevier, vol. 82(C).
  221. Kézdi, Gábor & Mátyás, László & Balázsi, László & Divényi, János Károly, 2014. "A közgazdasági adatforradalom és a panelökonometria [The revolution in economic data and panel econometrics]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1319-1340.
  222. Marco Quatrosi, 2024. "Emission trading in a high dimensional context: to what extent are carbon markets integrated with the broader system?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 41(3), pages 793-814, October.
  223. Xiong Xiong & Zhang Jin & Jin Xi & Feng Xu, 2016. "Review on Financial Innovations in Big Data Era," Journal of Systems Science and Information, De Gruyter, vol. 4(6), pages 489-504, December.
  224. Agarwal, Sumit & Wang, Long & Yang, Yang, 2021. "Impact of transboundary air pollution on service quality and consumer satisfaction," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 357-380.
  225. Andrew Harvey & Stephen Thiele, 2021. "Cointegration and control: Assessing the impact of events using time series data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 71-85, January.
  226. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
  227. Brunori, Paolo & Hufe, Paul & Mahler, Daniel Gerszon, 2021. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees and Forests," IZA Discussion Papers 14689, Institute of Labor Economics (IZA).
  228. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.
  229. Pietro Battiston & Simona Gamba & Alessandro Santoro, 2020. "Optimizing Tax Administration Policies with Machine Learning," Working Papers 436, University of Milano-Bicocca, Department of Economics, revised Mar 2020.
  230. Mendolia, Silvia & Siminski, Peter, 2016. "Does Family Background Affect Earnings through Education? A Generalised Approach to Mediation Analysis," IZA Discussion Papers 9917, Institute of Labor Economics (IZA).
  231. Nicolas Pröllochs & Stefan Feuerriegel & Dirk Neumann, 2018. "Statistical inferences for polarity identification in natural language," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
  232. Kawamura, Kohei & Kobashi, Yohei & Shizume, Masato & Ueda, Kozo, 2019. "Strategic central bank communication: Discourse analysis of the Bank of Japan’s Monthly Report," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 230-250.
  233. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
  234. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
  235. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
  236. Hallman, Nicholas J. & Kartapanis, Antonis & Schmidt, Jaime J., 2022. "How do auditors respond to competition? Evidence from the bidding process," Journal of Accounting and Economics, Elsevier, vol. 73(2).
  237. Nicholas Charron & Paola Annoni, 2021. "What is the Influence of News Media on People’s Perception of Corruption? Parametric and Non-Parametric Approaches," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(3), pages 1139-1165, February.
  238. 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).
  239. Opoku-Agyemang, Kweku A., 2017. "Priming human-computer interactions: Experimental evidence from economic development mobile surveys," SocArXiv 6bwxv, Center for Open Science.
  240. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
  241. Potnuru Kishen Suraj & Ankesh Gupta & Makkunda Sharma & Sourabh Bikas Paul & Subhashis Banerjee, 2017. "On monitoring development indicators using high resolution satellite images," Papers 1712.02282, arXiv.org, revised Jun 2018.
  242. Dyevre, Arthur & Lampach, Nicolas, 2018. "The origins of regional integration: Untangling the effect of trade on judicial cooperation," International Review of Law and Economics, Elsevier, vol. 56(C), pages 122-133.
  243. Nicolas Gavoille & Anna Zasova, 2021. "What we pay in the shadow: Labor tax evasion, minimum wage hike and employment," Working Papers CEB 21-017, ULB -- Universite Libre de Bruxelles.
  244. Luca Coraggio & Marco Pagano & Annalisa Scognamiglio & Joacim Tåg, 2022. "JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality," EIEF Working Papers Series 2205, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2022.
  245. Alpino, Matteo & Hauge, Karen Evelyn & Kotsadam, Andreas & Markussen, Simen, 2022. "Effects of dialogue meetings on sickness absence—Evidence from a large field experiment," Journal of Health Economics, Elsevier, vol. 83(C).
  246. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
  247. Michael T. Kiley, 2020. "Financial Conditions and Economic Activity: Insights from Machine Learning," Finance and Economics Discussion Series 2020-095, Board of Governors of the Federal Reserve System (U.S.).
  248. Samuel Shamiri & Leanne Ngai & Peter Lake & Yin Shan & Amee McMillan & Therese Smith & Kishor Sharma, 2022. "Nowcasting the Australian Labour Market at Disaggregated Levels," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 55(3), pages 389-404, September.
  249. Joey Blumberg & Gary Thompson, 2022. "Nonparametric segmentation methods: Applications of unsupervised machine learning and revealed preference," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 976-998, May.
  250. Wang, Huamao & Yao, Yumei & Salhi, Said, 2020. "Tension in big data using machine learning: Analysis and applications," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  251. Ning Xu & Jian Hong & Timothy C. G. Fisher, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," Papers 1606.00142, arXiv.org.
  252. 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.
  253. Mehmet Güney Celbiş & Pui‐hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 559-584, April.
  254. Verme, Paolo, 2020. "Which Model for Poverty Predictions?," GLO Discussion Paper Series 468, Global Labor Organization (GLO).
  255. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021. "Microstructure in the Machine Age [The risk of machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
  256. Onorante, Luca & Raftery, Adrian E., 2016. "Dynamic model averaging in large model spaces using dynamic Occam׳s window," European Economic Review, Elsevier, vol. 81(C), pages 2-14.
  257. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
  258. Meena Madhavan & Mohammed Ali Sharafuddin & Pairach Piboonrungroj & Ching-Chiao Yang, 2023. "Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo," Global Business Review, International Management Institute, vol. 24(6), pages 1145-1179, December.
  259. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Econometrics and Machine Learning," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 147-169.
  260. Jens Prüfer & Patricia Prüfer, 2020. "Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands," Small Business Economics, Springer, vol. 55(3), pages 651-672, October.
  261. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  262. Felipe D. Calainho & Alex M. Minne & Marc K. Francke, 2024. "A Machine Learning Approach to Price Indices: Applications in Commercial Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 68(4), pages 624-653, May.
  263. repec:dgr:rugsom:14027-eef is not listed on IDEAS
  264. Georg von Graevenitz & Christian Helmers & Valentine Millot & Oliver Turnbull, 2016. "Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2016-07, Centre for Competition Policy, University of East Anglia, Norwich, UK..
  265. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
  266. Fossati, Sebastian & Marchand, Joseph, 2020. "First to $15: Alberta's Minimum Wage Policy on Employment by Wages, Ages, and Places," Working Papers 2020-15, University of Alberta, Department of Economics, revised 27 Jul 2023.
  267. Mashabela, Juliet & Raputsoane, Leroi, 2018. "The behaviour of disaggregated output over the economic cycle," MPRA Paper 121912, University Library of Munich, Germany.
  268. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
  269. Diep Hoang Phan, 2023. "Lights and GDP relationship: What does the computer tell us?," Empirical Economics, Springer, vol. 65(3), pages 1215-1252, September.
  270. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
  271. Gordeev, Stepan & Steinbach, Sandro, 2024. "Determinants of PTA design: Insights from machine learning," International Economics, Elsevier, vol. 178(C).
  272. Houcine Senoussi, 2021. "Inflation and Inflation Uncertainty in Growth Model of Barro: An Application of Random Forest Method," International Econometric Review (IER), Econometric Research Association, vol. 13(1), pages 4-23, March.
  273. Olga Takacs & Janos Vincze, 2019. "The gender pay gap in Hungary: new results with a new methodology," CERS-IE WORKING PAPERS 1924, Institute of Economics, Centre for Economic and Regional Studies.
  274. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Demand Estimation with Machine Learning and Model Combination," NBER Working Papers 20955, National Bureau of Economic Research, Inc.
  275. Jiun-Hua Su, 2021. "No-Regret Forecasting with Egalitarian Committees," Papers 2109.13801, arXiv.org.
  276. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
  277. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
  278. Braaksma, Barteld & Zeelenberg, Kees, 2015. "“Re-make/Re-model”: Should big data change the modelling paradigm in official statistics?," MPRA Paper 87741, University Library of Munich, Germany.
  279. Zhongqi Deng & Yu Zhang & Ao Yu, 2020. "The New Economy in China: An Intercity Comparison," SAGE Open, , vol. 10(4), pages 21582440209, December.
  280. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
  281. Brorsen, B. Wade, 2017. "2016 WAEA Presidential Address: Comments on Agricultural Economics Research," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 42(1), pages 1-9, January.
  282. Roger Alejandro Banegas Rivero & Marco Alberto Nu ez Ramirez & Jorge Salas Vargas & Luis Fernando Escobar Caba & Sacnict Valdez del R o, 2019. "Landlocked Countries, Natural Resources and Growth: The Double Economic Curse Hypothesis," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 113-124.
  283. Chen, Cathy W.S. & Dong, Manh Cuong & Liu, Nathan & Sriboonchitta, Songsak, 2019. "Inferences of default risk and borrower characteristics on P2P lending," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
  284. Gusarov, N. & Talebijmalabad, A. & Joly, I., 2020. "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers 2020-12, Grenoble Applied Economics Laboratory (GAEL).
  285. Erokhin, Dmitry & Zagler, Martin, 2024. "Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms," Economic Modelling, Elsevier, vol. 139(C).
  286. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
  287. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  288. Joshua Mitts, 2020. "Short and Distort," The Journal of Legal Studies, University of Chicago Press, vol. 49(2), pages 287-334.
  289. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.
  290. Vincze, János, 2017. "Információ és tudás. A big data egyes hatásai a közgazdaságtanra [Information and knowledge: some effects of big data on economics]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1148-1159.
  291. Hal Varian, 2021. "Economics at Google," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 195-199, October.
  292. Akachi, Yoko & Canning, David, 2015. "Inferring the economic standard of living and health from cohort height: Evidence from modern populations in developing countries," Economics & Human Biology, Elsevier, vol. 19(C), pages 114-128.
  293. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
  294. Bruzikas, Tadas & Soetevent, Adriaan, 2014. "Detailed data and changes in market structure," Research Report 14027-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  295. Phyllis Asorh Oteng & Victor Curtis Lartey & Amos Kwasi Amofa, 2023. "Modeling the Macroeconomic and Demographic Determinants of Life Insurance Demand in Ghana Using the Elastic Net Algorithm," SAGE Open, , vol. 13(3), pages 21582440231, September.
  296. Roland-Holst, David & Karymshakov, Kamalbek & Sulaimanova, Burulcha & Sultakeev, Kadyrbek, 2022. "ICT, Online Search Behavior, and Remittances: Evidence from the Kyrgyz Republic," ADBI Working Papers 1348, Asian Development Bank Institute.
  297. Stefan P. Penczynski, 2019. "Using machine learning for communication classification," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 1002-1029, December.
  298. Zhong, Weifeng & Chan, Julian, 2020. "Predicting Authoritarian Crackdowns: A Machine Learning Approach," Working Papers 10464, George Mason University, Mercatus Center.
  299. Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
  300. Dave Donaldson & Adam Storeygard, 2016. "The View from Above: Applications of Satellite Data in Economics," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 171-198, Fall.
  301. Christian Handke & Lucie Guibault & Joan‐Josep Vallbé, 2021. "Copyright's impact on data mining in academic research," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(8), pages 1999-2016, December.
  302. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
  303. Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024. "Biases in inequality of opportunity estimates: measures and solutions," Working Papers 675, ECINEQ, Society for the Study of Economic Inequality.
  304. Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022. "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, vol. 108(C).
  305. 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.
  306. Fink Simonsen, Nicolai & Kjær, Trine, 2021. "New Evidence of Health State Dependent Utility of Consumption: A combined survey and register study," DaCHE discussion papers 2021:2, University of Southern Denmark, Dache - Danish Centre for Health Economics.
  307. Lessmann, Christian & Steinkraus, Arne, 2019. "The geography of natural resources, ethnic inequality and civil conflicts," European Journal of Political Economy, Elsevier, vol. 59(C), pages 33-51.
  308. Amin, Modhurima Dey & Badruddoza, Syed & McCluskey, Jill J., 2021. "Predicting access to healthful food retailers with machine learning," Food Policy, Elsevier, vol. 99(C).
  309. Schilling, Chris & Knight, Josh & Mortimer, Duncan & Petrie, Dennis & Clarke, Philip & Chalmers, John & Kerr, Andrew & Jackson, Rod, 2017. "Australian general practitioners initiate statin therapy primarily on the basis of lipid levels; New Zealand general practitioners use absolute risk," Health Policy, Elsevier, vol. 121(12), pages 1233-1239.
  310. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
  311. He, Yinan & Wu, Chao & Fan, Yuanyuan, 2024. "Exploring the drivers of local government budget coordination: A random forest regression analysis," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1104-1113.
  312. Duo Qin, 2022. "Redirect the Probability Approach in Econometrics Towards PAC Learning," Working Papers 249, Department of Economics, SOAS University of London, UK.
  313. Yan Chen & Joseph Konstan, 2015. "Online field experiments: a selective survey of methods," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 29-42, July.
  314. 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.
  315. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Économétrie & Machine Learning," Working Papers hal-01568851, HAL.
  316. Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021. "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, vol. 14(2), pages 111-128, August.
  317. Marco Guerzoni & Consuelo R. Nava & Massimiliano Nuccio, 2019. "The survival of start-ups in time of crisis. A machine learning approach to measure innovation," Papers 1911.01073, arXiv.org.
  318. Kohei Kawamura & Yohei Kobashi & Masato Shizume & Kozo Ueda, 2016. "Strategic central bank communication: discourse and game-theoretic analyses of the Bank of Japan's Monthly Report," CAMA Working Papers 2016-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  319. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
  320. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
  321. Robertas Damasevicius, 2023. "Progress, Evolving Paradigms and Recent Trends in Economic Analysis," Financial Economics Letters, Anser Press, vol. 2(2), pages 35-47, October.
  322. Thiago Christiano Silva & Benjamin Miranda Tabak & Idamar Magalhães Ferreira, 2019. "Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies," Complexity, Hindawi, vol. 2019, pages 1-14, December.
  323. Christian M. Dahl & Torben S. D. Johansen & Emil N. S{o}rensen & Christian E. Westermann & Simon F. Wittrock, 2021. "Applications of Machine Learning in Document Digitisation," Papers 2102.03239, arXiv.org.
  324. Brunke, Henrich & Lapsley, James T. & Mueller, Rolf A.E. & Tauscher, Ludwig, 2016. ""Tested Quality In The Glass": Wine Quality Certification In Germany," Working Papers 234641, American Association of Wine Economists.
  325. Pave Sohnesen,Thomas & Stender,Niels, 2016. "Is random forest a superior methodology for predicting poverty ? an empirical assessment," Policy Research Working Paper Series 7612, The World Bank.
  326. Yael Grushka-Cockayne & Victor Richmond R. Jose & Kenneth C. Lichtendahl Jr., 2017. "Ensembles of Overfit and Overconfident Forecasts," Management Science, INFORMS, vol. 63(4), pages 1110-1130, April.
  327. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
  328. Ermolova, Maria D. & Penikas, Henry I., 2016. "QAIDS Model Based on Russian Pseudo - Panel Data: Impact of 1998 and 2008 Crises," MPRA Paper 82876, University Library of Munich, Germany.
  329. Jenny W Sun & Jessica M Franklin & Kathryn Rough & Rishi J Desai & Sonia Hernández-Díaz & Krista F Huybrechts & Brian T Bateman, 2020. "Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
  330. 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.
  331. Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
  332. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
  333. Fabio Pammolli & Paolo Bonaretti & Massimo Riccaboni & Valentina Tortolini, 2019. "Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte," Working Papers CERM 01-2019, Competitività, Regole, Mercati (CERM).
  334. Donia Kamel & Laura Pollacci, 2023. "Academic Migration and Academic Networks: Evidence from Scholarly Big Data and the Iron Curtain," CESifo Working Paper Series 10377, CESifo.
  335. Matthias Duschl & Shi-Shu Peng, 2013. "Chinese firm dynamics and the role of ownership type A conditional estimation approach of the Asymmetric Exponential Power (AEP) density," Papers on Economics and Evolution 2014-01, Philipps University Marburg, Department of Geography.
  336. Nathan, Max, 2014. "Top Team Diversity and Business Performance: Latent Class Analysis for Firms and Cities," IZA Discussion Papers 8462, Institute of Labor Economics (IZA).
  337. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
  338. Li, Jinsong & Takeuchi, Kenji, 2023. "Do municipal mergers reduce the cost of waste management? Evidence from Japan," Regional Science and Urban Economics, Elsevier, vol. 103(C).
  339. Cerulli, Giovanni, 2020. "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper 99111, University Library of Munich, Germany.
  340. David Autor, 2014. "Polanyi's Paradox and the Shape of Employment Growth," NBER Working Papers 20485, National Bureau of Economic Research, Inc.
  341. Neil Kellard & Denise Osborn & Jerry Coakley & Imanol Arrieta-ibarra & Ignacio N. Lobato, 2015. "Testing for Predictability in Financial Returns Using Statistical Learning Procedures," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 672-686, September.
  342. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
  343. Schintler, Laurie A. & Fischer, Manfred M., 2018. "Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research," Working Papers in Regional Science 2018/02, WU Vienna University of Economics and Business.
  344. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
  345. Mimoun Benali & Lahboub Karima, 2024. "Modelling Stock Prices of Energy Sector using Supervised Machine Learning Techniques," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 594-602, March.
  346. Yi Ren & Tong Xia & Yong Li & Xiang Chen, 2019. "Predicting socio-economic levels of urban regions via offline and online indicators," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-15, July.
  347. David Zenz, 2020. "Die Vernetzung Wiens mit den Städten Europas," wiiw Statistical Reports 9, The Vienna Institute for International Economic Studies, wiiw.
  348. Rama K. Malladi, 2024. "Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1021-1045, March.
  349. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
  350. Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
  351. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
  352. Embaye, Weldensie T. & Zereyesus, Yacob A., 2017. "Measuring the value of housing services in household surveys: an application of machine learning approach," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252851, Southern Agricultural Economics Association.
  353. Evgeny A. Antipov & Elena B. Pokryshevskaya, 2020. "Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 355-364, October.
  354. Knudsen, Eirik Sjåholm & Lien, Lasse B. & Timmermans, Bram & Belik, Ivan & Pandey, Sujit, 2021. "Stability in turbulent times? The effect of digitalization on the sustainability of competitive advantage," Journal of Business Research, Elsevier, vol. 128(C), pages 360-369.
  355. Koffi Dumor & Komlan Gbongli, 2021. "Trade impacts of the New Silk Road in Africa: Insight from Neural Networks Analysis," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 17(02), pages 13-26.
  356. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
  357. 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.
  358. Yang Yi & Le Wen & Shan He, 2022. "Partitioning for “Common but Differentiated” Precise Air Pollution Governance: A Combined Machine Learning and Spatial Econometric Approach," Energies, MDPI, vol. 15(9), pages 1-23, May.
  359. Paul Felix Reiter, 2023. "Measuring the Relevance of Factors on Cross-Sectional Returns with Decision Trees," Applied Economics and Finance, Redfame publishing, vol. 10(4), pages 14-25, November.
  360. Martin Magris & Mostafa Shabani & Alexandros Iosifidis, 2022. "Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets," Papers 2203.03613, arXiv.org, revised Jan 2023.
  361. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
  362. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
  363. Raputsoane, Leroi, 2018. "Quantifying economic recovery from the recent global financial crisis," MPRA Paper 87410, University Library of Munich, Germany.
  364. Nakamura, Nobuyuki & Suzuki, Aya, 2021. "COVID-19 and the intentions to migrate from developing countries: Evidence from online search activities in Southeast Asia," Journal of Asian Economics, Elsevier, vol. 76(C).
  365. Andre Boik, 2018. "Prediction and Identification in Two-Sided Markets," CESifo Working Paper Series 6857, CESifo.
  366. Tadas Bruzikas & Adriaan R. Soetevent, 2014. "Detailed Data and Changes in Market Structure: The Move to Unmanned Gasoline Service Stations," Tinbergen Institute Discussion Papers 14-123/VII, Tinbergen Institute.
  367. Askitas, Nikos, 2015. "Trend-Spotting in the Housing Market," IZA Discussion Papers 9427, Institute of Labor Economics (IZA).
  368. Allison Baker & Timothy Brennan & Jack Erb & Omar Nayeem & Aleksandr Yankelevich, 2014. "Economics at the FCC, 2013–2014," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 45(4), pages 345-378, December.
  369. Peysakhovich, Alexander & Naecker, Jeffrey, 2017. "Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 373-384.
  370. Kakatkar, Chinmay & Bilgram, Volker & Füller, Johann, 2020. "Innovation analytics: Leveraging artificial intelligence in the innovation process," Business Horizons, Elsevier, vol. 63(2), pages 171-181.
  371. Eli P Fenichel & Yukiko Hashida, 2019. "Choices and the value of natural capital," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 35(1), pages 120-137.
  372. Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024. "Biases in inequality of opportunity estimates: measures and solutions," SERIES 02-2024, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Aug 2024.
  373. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
  374. Michael Creel, 2016. "Neural Nets for Indirect Inference," UFAE and IAE Working Papers 960.16, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), revised 18 Jul 2016.
  375. Carlianne Patrick & Amanda Ross & Heather Stephens, 2016. "Designing Policies to Spur Economic Growth: How Regional Scientists Can Contribute to Future Policy Development and Evaluation," Working Papers 16-04, Department of Economics, West Virginia University.
  376. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
  377. Lang, Jan Hannes & Izzo, Cosimo & Fahr, Stephan & Ruzicka, Josef, 2019. "Anticipating the bust: a new cyclical systemic risk indicator to assess the likelihood and severity of financial crises," Occasional Paper Series 219, European Central Bank.
  378. Hyoung Sun Yoo & Chul Lee & Seung-Pyo Jun, 2018. "The Characteristics of SMEs Preferring Cooperative Research and Development Support from the Government: The Case of Korea," Sustainability, MDPI, vol. 10(9), pages 1-18, August.
  379. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
  380. Emrich, Eike & Pierdzioch, Christian, 2015. "Public goods, private consumption, and human-capital formation: On the economics of volunteer labour supply," Working Papers of the European Institute for Socioeconomics 14, European Institute for Socioeconomics (EIS), Saarbrücken.
  381. Alex Coad & Dominik Janzing & Paul Nightingale, 2018. "Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 37(75), pages 779-808, March.
  382. Brunori, Paolo & Hufe, Paul & Mahler, Daniel, 2023. "The roots of inequality: estimating inequality of opportunity from regression trees and forests," LSE Research Online Documents on Economics 118220, London School of Economics and Political Science, LSE Library.
  383. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.
  384. Michele Rabasco & Pietro Battiston, 2023. "Predicting the deterrence effect of tax audits. A machine learning approach," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 531-556, July.
  385. Olga Takacs & Janos Vincze, 2019. "Blinder-Oaxaca decomposition with recursive tree-based methods: a technical note," CERS-IE WORKING PAPERS 1923, Institute of Economics, Centre for Economic and Regional Studies.
  386. Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
  387. Robert J. Hill & Miriam Steurer, 2020. "Commercial Property Price Indices and Indicators: Review and Discussion of Issues Raised in the CPPI Statistical Report of Eurostat (2017)," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 736-751, September.
  388. Ibarrarán, Pablo & Medellín, Nadin & Regalia, Ferdinando & Stampini, Marco & Parodi, Sandro & Tejerina, Luis & Cueva, Pedro & Vásquez, Madiery, 2017. "How Conditional Cash Transfers Work," IDB Publications (Books), Inter-American Development Bank, number 8159, December.
  389. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
  390. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
  391. Arthur Blouin & Julian Dyer, 2021. "How Cultures Converge: An Empirical Investigation of Trade and Linguistic Exchange," Working Papers tecipa-691, University of Toronto, Department of Economics.
  392. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
  393. Vincenzo Butticè & Carlotta Orsenigo & Mike Wright, 2018. "The effect of information asymmetries on serial crowdfunding and campaign success," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(2), pages 143-173, June.
  394. Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  395. Jian Gao & Tao Zhou, 2017. "Quantifying China's Regional Economic Complexity," Papers 1703.01292, arXiv.org, revised Nov 2017.
  396. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
  397. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
  398. Brown, Ross & Rocha, Augusto, 2020. "Entrepreneurial uncertainty during the Covid-19 crisis: Mapping the temporal dynamics of entrepreneurial finance," Journal of Business Venturing Insights, Elsevier, vol. 14(C).
  399. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
  400. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
  401. Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
  402. Arthur Acolin & Ari Decter-Frain & Matt Hall, 2022. "Small-area estimates from consumer trace data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(27), pages 843-882.
  403. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
  404. David, Guy & Saynisch, Philip A. & Smith-McLallen, Aaron, 2018. "The economics of patient-centered care," Journal of Health Economics, Elsevier, vol. 59(C), pages 60-77.
  405. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
  406. Francisco Corona & Graciela Gonz'alez-Far'ias & Jes'us L'opez-P'erez, 2021. "A nowcasting approach to generate timely estimates of Mexican economic activity: An application to the period of COVID-19," Papers 2101.10383, arXiv.org.
  407. Lopez Cordova,Jose Ernesto, 2020. "Digital Platforms and the Demand for International Tourism Services," Policy Research Working Paper Series 9147, The World Bank.
  408. Tobias Cagala, 2017. "Improving data quality and closing data gaps with machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46, Bank for International Settlements.
  409. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  410. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
  411. Mochen Yang & Gediminas Adomavicius & Gordon Burtch & Yuqing Rena, 2018. "Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining," Information Systems Research, INFORMS, vol. 29(1), pages 4-24, March.
  412. Cui, Hailong & Rajagopalan, Sampath & Ward, Amy R., 2020. "Predicting product return volume using machine learning methods," European Journal of Operational Research, Elsevier, vol. 281(3), pages 612-627.
  413. O’Connell, Martin & Smith, Howard & Thomassen, Øyvind, 2023. "A two sample size estimator for large data sets," Discussion Papers 2023/1, Norwegian School of Economics, Department of Business and Management Science.
  414. Shengying Zhai & Qihui Chen & Wenxin Wang, 2019. "What Drives Green Fodder Supply in China?—A Nerlovian Analysis with LASSO Variable Selection," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
  415. Koffi Dumor & Li Yao & Jean-Paul Ainam & Edem Koffi Amouzou & Williams Ayivi, 2021. "Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks," SAGE Open, , vol. 11(3), pages 21582440211, July.
  416. Mr. Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 2019/228, International Monetary Fund.
  417. Ron Adner & Phanish Puranam & Feng Zhu, 2019. "What Is Different About Digital Strategy? From Quantitative to Qualitative Change," Strategy Science, INFORMS, vol. 4(4), pages 253-261, December.
  418. Greg Distelhorst & Jens Hainmueller & Richard M. Locke, 2017. "Does Lean Improve Labor Standards? Management and Social Performance in the Nike Supply Chain," Management Science, INFORMS, vol. 63(3), pages 707-728, March.
  419. 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.
  420. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
  421. Bholat, David, 2015. "Big data and central banks," Bank of England Quarterly Bulletin, Bank of England, vol. 55(1), pages 86-93.
  422. Gabriel Loumeau & Christian Stettler, 2021. "Fiscal Autonomy and Self-Determination," CESifo Working Paper Series 9445, CESifo.
  423. William G. Bostic Jr. & Ron S. Jarmin & Brian Moyer, 2016. "Modernizing Federal Economic Statistics," American Economic Review, American Economic Association, vol. 106(5), pages 161-164, May.
  424. Straubhaar, Thomas, 2021. "Datenwirtschaft: Was ist neu und anders?," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 9-25, Hamburg Institute of International Economics (HWWI).
  425. 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).
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