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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. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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).
  15. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
  16. 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.
  17. 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.
  18. 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).
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. Brunori, Paolo & Salas-Rojo, Pedro & Verme, Paolo, 2022. "Estimating Inequality with Missing Incomes," GLO Discussion Paper Series 1138, Global Labor Organization (GLO).
  24. 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.
  25. 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.).
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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).
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
  36. 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.
  37. 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.
  38. Verme, Paolo, 2020. "Which Model for Poverty Predictions?," GLO Discussion Paper Series 468, Global Labor Organization (GLO).
  39. 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.
  40. 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.
  41. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021. "Microstructure in the Machine Age [The risk of machine learning]," Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
  42. 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.
  43. 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).
  44. 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).
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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..
  52. repec:dgr:rugsom:14027-eef is not listed on IDEAS
  53. 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.
  54. 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.
  55. 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.
  56. 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.
  57. 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.
  58. 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.
  59. 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.
  60. 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.
  61. Diep Hoang Phan, 2023. "Lights and GDP relationship: What does the computer tell us?," Empirical Economics, Springer, vol. 65(3), pages 1215-1252, September.
  62. 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.
  63. 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).
  64. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
  65. 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.
  66. 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.
  67. 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.
  68. 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.
  69. 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.
  70. 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.
  71. Ben Vinod, 2016. "Big Data in the travel marketplace," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 352-359, October.
  72. 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.
  73. Jiun-Hua Su, 2021. "No-Regret Forecasting with Egalitarian Committees," Papers 2109.13801, arXiv.org.
  74. 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.
  75. 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.
  76. 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.
  77. 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).
  78. Ali Namaki & Reza Eyvazloo & Shahin Ramtinnia, 2023. "A systematic review of early warning systems in finance," Papers 2310.00490, arXiv.org.
  79. 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.
  80. 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.
  81. Zhongqi Deng & Yu Zhang & Ao Yu, 2020. "The New Economy in China: An Intercity Comparison," SAGE Open, , vol. 10(4), pages 21582440209, December.
  82. Amarda Cano, 2020. "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, January -.
  83. 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.
  84. 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.
  85. 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.
  86. 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).
  87. 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.
  88. 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.
  89. 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.
  90. Achten, Sandra & Lessmann, Christian, 2020. "Spatial inequality, geography and economic activity," World Development, Elsevier, vol. 136(C).
  91. 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).
  92. Nikita Gusarov & Amirreza Talebijamalabad & Iragaël Joly, 2020. "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers hal-03019739, HAL.
  93. Steen Nielsen, 2020. "Management accounting and the idea of machine learning," Economics Working Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
  94. 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.
  95. 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.
  96. 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.
  97. 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.
  98. Bonnet, Céline & Richards, Timothy J., 2016. "Models of Consumer Demand for Differentiated Products," TSE Working Papers 16-741, Toulouse School of Economics (TSE).
  99. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
  100. Max Nathan & Anna Rosso, 2014. "Mapping Information Economy Businesses with Big Data: Findings for the UK," CEP Occasional Papers 44, Centre for Economic Performance, LSE.
  101. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  102. Joshua Mitts, 2020. "Short and Distort," The Journal of Legal Studies, University of Chicago Press, vol. 49(2), pages 287-334.
  103. 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.
  104. 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.
  105. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.
  106. Hal Varian, 2021. "Economics at Google," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 195-199, October.
  107. Daskalova, Vessela & Vriend, Nicolaas J., 2020. "Categorization and coordination," European Economic Review, Elsevier, vol. 129(C).
  108. McKenzie, David & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.
  109. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  110. 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.
  111. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
  112. Rickard Nyman & Paul Ormerod, 2017. "Predicting Economic Recessions Using Machine Learning Algorithms," Papers 1701.01428, arXiv.org.
  113. 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.
  114. 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.
  115. 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).
  116. 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.
  117. 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).
  118. 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.
  119. 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.
  120. 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).
  121. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  122. Stefan P. Penczynski, 2019. "Using machine learning for communication classification," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 1002-1029, December.
  123. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  124. Zhong, Weifeng & Chan, Julian, 2020. "Predicting Authoritarian Crackdowns: A Machine Learning Approach," Working Papers 10464, George Mason University, Mercatus Center.
  125. Micevska, Maja, 2021. "Revisiting forced migration: A machine learning perspective," European Journal of Political Economy, Elsevier, vol. 70(C).
  126. 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.
  127. 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.
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