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Prediction Policy Problems
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Cited by:
- Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
- 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.
- Bansak, Kirk & Martén, Linna, 2024. "Algorithmic Decision-Making, Fairness, and the Distribution of Impact: Application to Refugee Matching," SOFI Working Papers in Labour Economics 6/2024, Stockholm University, Swedish Institute for Social Research.
- Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
- Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
- Kimia Keshanian & Narayan Ramasubbu & Kaushik Dutta, 2023. "Mobile advertisement campaigns for boosting in‐store visits: A design framework and case study," Production and Operations Management, Production and Operations Management Society, vol. 32(8), pages 2438-2454, August.
- Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020.
"Optimal data collection for randomized control trials,"
The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
- Carneiro, Pedro & Lee, Sokbae & Wilhelm, Daniel, 2016. "Optimal Data Collection for Randomized Control Trials," IZA Discussion Papers 9908, Institute of Labor Economics (IZA).
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers CWP15/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers 15/17, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2019. "Optimal Data Collection for Randomized Control Trials," CeMMAP working papers CWP21/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers 45/17, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2016. "Optimal Data Collection for Randomized Control Trials," Papers 1603.03675, arXiv.org, revised Aug 2016.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers CWP45/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2016. "Optimal data collection for randomized control trials," CeMMAP working papers 15/16, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2016. "Optimal data collection for randomized control trials," CeMMAP working papers CWP15/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Anja Lambrecht & Catherine Tucker, 2024. "Apparent algorithmic discrimination and real-time algorithmic learning in digital search advertising," Quantitative Marketing and Economics (QME), Springer, vol. 22(4), pages 357-387, December.
- Hidalgo, César A., 2023.
"The policy implications of economic complexity,"
Research Policy, Elsevier, vol. 52(9).
- Cesar A. Hidalgo, 2022. "The Policy Implications of Economic Complexity," Papers in Evolutionary Economic Geography (PEEG) 2230, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Nov 2022.
- César A. Hidalgo, 2023. "The policy implications of economic complexity," Post-Print hal-04361080, HAL.
- Michael Allan Ribers & Hannes Ullrich, 2020.
"Machine Predictions and Human Decisions with Variation in Payoffs and Skill,"
Papers
2011.11017, arXiv.org.
- Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skills," Discussion Papers of DIW Berlin 1911, DIW Berlin, German Institute for Economic Research.
- Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," CESifo Working Paper Series 8702, CESifo.
- 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).
- Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2023. "Taste of home: Birth town bias in Geographical Indications," Economics & Statistics Discussion Papers esdp23089, University of Molise, Department of Economics.
- van der Heijden, Hans, 2022. "Predicting industry sectors from financial statements: An illustration of machine learning in accounting research," The British Accounting Review, Elsevier, vol. 54(5).
- Chen, S. & Doerr, S. & Frost, J. & Gambacorta, L. & Shin, H.S., 2023.
"The fintech gender gap,"
Journal of Financial Intermediation, Elsevier, vol. 54(C).
- Gambacorta, Leonardo & Chen, Sharon & Doerr, Sebastian & Frost, Jon & Shin, Hyun Song, 2021. "The fintech gender gap," CEPR Discussion Papers 16270, C.E.P.R. Discussion Papers.
- Sharon Chen & Sebastian Doerr & Jon Frost & Leonardo Gambacorta & Hyun Song Shin, 2021. "The fintech gender gap," BIS Working Papers 931, Bank for International Settlements.
- Leuz, Christian, 2022.
"Towards a design-based approach to accounting research,"
Journal of Accounting and Economics, Elsevier, vol. 74(2).
- Leuz, Christian, 2023. "Towards a design-based approach to accounting research," CFS Working Paper Series 703, Center for Financial Studies (CFS).
- Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
- 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).
- Newell, Richard G. & Prest, Brian C. & Sexton, Steven, 2020. "The GDP Temperature Relationship: Implications for Climate Change Damages," RFF Working Paper Series 18-17, Resources for the Future.
- Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
- Emanuel Kohlscheen, 2022.
"Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices,"
Papers
2208.14254, arXiv.org, revised Oct 2022.
- Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
- Aiello, Francesco & Albanese, Giuseppe & Piselli, Paolo, 2019. "Good value for public money? The case of R&D policy," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1057-1076.
- Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
- Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
- Müller, Stephan & Rau, Holger A., 2021. "Economic preferences and compliance in the social stress test of the COVID-19 crisis," Journal of Public Economics, Elsevier, vol. 194(C).
- Keyon Vafa & Susan Athey & David M. Blei, 2024.
"Estimating Wage Disparities Using Foundation Models,"
Papers
2409.09894, arXiv.org.
- Vafa, Keyon & Athey, Susan & Blei, David M., 2024. "Estimating Wage Disparities Using Foundation Models," Research Papers 4206, Stanford University, Graduate School of Business.
- Emanuel Kohlscheen & Richhild Moessner, 2022.
"Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix,"
Papers
2208.14650, arXiv.org.
- Emanuel Kohlscheen & Richhild Moessner, 2022. "Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix," CESifo Working Paper Series 9807, CESifo.
- Jens Ludwig & Sendhil Mullainathan & Jann Spiess, 2017. "Machine-Learning Tests for Effects on Multiple Outcomes," Papers 1707.01473, arXiv.org, revised May 2019.
- Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
- Resce, Giuliano, 2022. "The impact of political and non-political officials on the financial management of local governments," Journal of Policy Modeling, Elsevier, vol. 44(5), pages 943-962.
- Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022.
"Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels,"
Energy Economics, Elsevier, vol. 105(C).
- Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021. "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series 8835, CESifo.
- Hannes Mueller & Christopher Rauh, 2022.
"The Hard Problem of Prediction for Conflict Prevention,"
Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
- Mueller, Hannes & Rauh, Christopher, 2019. "The Hard Problem of Prediction for Conflict Prevention," CEPR Discussion Papers 13748, C.E.P.R. Discussion Papers.
- Hannes Mueller & Christopher Rauh, 2019. "The hard problem of prediction for conflict prevention," Cahiers de recherche 2019-02, Universite de Montreal, Departement de sciences economiques.
- Hannes Mueller, 2021. "The Hard Problem of Prediction for Conflict Prevention," Working Papers 1244, Barcelona School of Economics.
- Mueller, H. & Rauh, C., 2021. "The Hard Problem of Prediction for Conflict Prevention," Cambridge Working Papers in Economics 2103, Faculty of Economics, University of Cambridge.
- Mueller, H. & Rauh, C., 2020. "The Hard Problem of Prediction for Conflict Prevention," Cambridge Working Papers in Economics 2015, Faculty of Economics, University of Cambridge.
- Hannes Mueller & Christopher Rauh, 2019. "The Hard Problem of Prediction for Conflict Prevention," Cahiers de recherche 02-2019, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Songul Tolan, 2018. "Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges," JRC Working Papers on Digital Economy 2018-10, Joint Research Centre.
- C'esar A. Hidalgo, 2022. "The Policy Implications of Economic Complexity," Papers 2205.02164, arXiv.org, revised Aug 2023.
- Oliver Lock & Michael Bain & Christopher Pettit, 2021. "Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example," Environment and Planning B, , vol. 48(3), pages 484-502, March.
- Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
- Emanuel Kohlscheen, 2021.
"What does machine learning say about the drivers of inflation?,"
BIS Working Papers
980, Bank for International Settlements.
- Emanuel Kohlscheen, 2022. "What does machine learning say about the drivers of inflation?," Papers 2208.14653, arXiv.org, revised Jan 2023.
- 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.
- Christian Posso & Jorge Tamayo & Arlen Guarin & Estefania Saravia, 2024. "Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes," Borradores de Economia 1269, Banco de la Republica de Colombia.
- Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
- Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
- Vitezslav Titl & Deni Mazrekaj & Fritz Schiltz, 2024.
"Identifying Politically Connected Firms: A Machine Learning Approach,"
Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 137-155, February.
- Vitezslav Titl & Fritz Schiltz, 2021. "Identifying Politically Connected Firms: A Machine Learning Approach," Working Papers 2110, Utrecht School of Economics.
- Ginevra Buratti & Alessio D'Ignazio, 2024. "Improving the effectiveness of financial education programs. A targeting approach," Journal of Consumer Affairs, Wiley Blackwell, vol. 58(2), pages 451-485, June.
- Momin M. Malik, 2020. "A Hierarchy of Limitations in Machine Learning," Papers 2002.05193, arXiv.org, revised Feb 2020.
- 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.
- Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
- Isabel Hovdahl, 2019. "On the use of machine learning for causal inference in climate economics," Working Papers No 05/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Hannes Wallimann & David Imhof & Martin Huber, 2023.
"A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels,"
Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
- Wallimann, Hannes & Imhof, David & Huber, Martin, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers 513, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
- 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.
- Alexei Alexandrov & Russell Pittman & Olga Ukhaneva, 2018.
"Pricing of Complements in the U.S. Freight Railroads: Cournot Versus Coase,"
EAG Discussions Papers
201801, Department of Justice, Antitrust Division.
- Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2018. "Pricing of Complements in the U.S. freight railroads: Cournot versus Coase," MPRA Paper 86279, University Library of Munich, Germany.
- Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021.
"Preventing rather than punishing: An early warning model of malfeasance in public procurement,"
International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
- Gallego, J & Rivero, G & Martínez, J.D., 2018. "Preventing rather than Punishing: An Early Warning Model of Malfeasance in Public Procurement," Documentos de Trabajo 16724, Universidad del Rosario.
- Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
- Emile Cammeraat & Brinn Hekkelman & Pim Kastelein & Suzanne Vissers, 2023. "Predictability and (co-)incidence of labor and health shocks," CPB Discussion Paper 453, CPB Netherlands Bureau for Economic Policy Analysis.
- Pauline Affeldt, 2019. "EU Merger Policy Predictability Using Random Forests," Discussion Papers of DIW Berlin 1800, DIW Berlin, German Institute for Economic Research.
- 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).
- 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.
- 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.
- Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2024. "Political favouritism and inefficient management: Policy-makers’ birth town bias in EU quality certifications," Journal of Policy Modeling, Elsevier, vol. 46(3), pages 683-702.
- Ashesh Rambachan & Jon Kleinberg & Sendhil Mullainathan & Jens Ludwig, 2020. "An Economic Approach to Regulating Algorithms," NBER Working Papers 27111, National Bureau of Economic Research, Inc.
- 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.
- Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
- Nikolaos Askitas, 2016.
"Big Data is a big deal but how much data do we need? [Big Data gut und schön. Aber wie viel Data brauchen wir?],"
AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 113-125, October.
- Askitas, Nikos, 2016. "Big Data Is a Big Deal But How Much Data Do We Need?," IZA Discussion Papers 9988, Institute of Labor Economics (IZA).
- Steinkraus, Arne, 2018. "Rethinking Policy Evaluation – Do Simple Neural Nets Bear Comparison with Synthetic Control Method?," EconStor Preprints 177390, ZBW - Leibniz Information Centre for Economics.
- 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).
- Michael Allan Ribers & Hannes Ullrich, 2024. "Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing," Quantitative Marketing and Economics (QME), Springer, vol. 22(4), pages 445-483, December.
- Michael Allan Ribers & Hannes Ullrich, 2019.
"Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?,"
Papers
1906.03044, arXiv.org.
- Michael A. Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Discussion Papers of DIW Berlin 1803, DIW Berlin, German Institute for Economic Research.
- Michael Allan Ribers & Hannes Ullrich, 2019. "Battling antibiotic resistance: can machine learning improve prescribing?," CESifo Working Paper Series 7654, CESifo.
- Zhou, Jinwei & Luo, Qi, 2024. "Influence factor studies based on ensemble learning on the innovation performance of technology mergers and acquisitions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 67-89.
- Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
- Konstantin Boss & Finja Krueger & Conghan Zheng & Tobias Heidland & Andre Groeger, 2023. "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers 1387, Barcelona School of Economics.
- Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2017. "Royalty stacking in the U.S. freight railroads: Cournot vs. Coase," MPRA Paper 78249, University Library of Munich, Germany.
- DIODATO Dario, 2024. "Handbook of Economic Complexity for Policy," JRC Research Reports JRC138666, Joint Research Centre.
- Heidland, Tobias & Jannsen, Nils & Groll, Dominik & Kalweit, René & Boockmann, Bernhard, 2021. "Analyse und Prognose von Migrationsbewegungen," Kieler Beiträge zur Wirtschaftspolitik 34, Kiel Institute for the World Economy (IfW Kiel).
- Susan Athey & Herman Brunborg & Tianyu Du & Ayush Kanodia & Keyon Vafa, 2024.
"LABOR-LLM: Language-Based Occupational Representations with Large Language Models,"
Papers
2406.17972, arXiv.org, revised Dec 2024.
- Du, Tianyu & Kanodia, Ayush & Brunborg, Herman & Vafa, Keyon & Athey, Susan, 2024. "Labor-LLM: Language-Based Occupational Representations with Large Language Models," Research Papers 4188, Stanford University, Graduate School of Business.
- Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
- Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
- Keser, Claudia & Rau, Holger A., 2022. "Policy Incentives and Determinants of Citizens' COVID-19 Vaccination Motives," VfS Annual Conference 2022 (Basel): Big Data in Economics 264040, Verein für Socialpolitik / German Economic Association.
- 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.
- 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.
- Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
- Keser, Claudia & Rau, Holger A., 2022. "Policy incentives and determinants of citizens' COVID-19 vaccination motives," University of Göttingen Working Papers in Economics 434, University of Goettingen, Department of Economics.
- 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.
- Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022.
"Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications,"
Food Policy, Elsevier, vol. 112(C).
- Resce, Giuliano & Vaquero-Pineiro, Cristina, 2022. "Predicting Agri-food Quality across Space: A Machine Learning Model for the Acknowledgment of Geographical Indications," Economics & Statistics Discussion Papers esdp22082, University of Molise, Department of Economics.
- Tsun Se Cheong & Guanghua Wan & David Kam Hung Chui, 2022. "Unveiling the Relationship between Economic Growth and Equality for Developing Countries," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 30(5), pages 1-28, September.
- Bas Bosma & Arjen Witteloostuijn, 2024. "Machine learning in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 55(6), pages 676-702, August.
- 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).
- Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024.
"Predicting dropout from higher education: Evidence from Italy,"
Economic Modelling, Elsevier, vol. 130(C).
- Marco Delogu & Raffaelle Lagravinese & Dimitri Paolini & Giuliano Resce, 2020. "Predicting dropout from higher education: Evidence from Italy," DEM Discussion Paper Series 22-06, Department of Economics at the University of Luxembourg.
- 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).
- Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018.
"Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life,"
Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
- Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2015. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life," Harvard Business School Working Papers 16-065, Harvard Business School.
- Glaeser, Edward L. & Kominers, Scott Duke & Luca, Michael & Naik, Nikhil, 2015. "Big Data and Big Cities: The Promises and Limitations of Improved Measures for Urban Life," Working Paper Series 15-075, Harvard University, John F. Kennedy School of Government.
- Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2015. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life," NBER Working Papers 21778, National Bureau of Economic Research, Inc.
- 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.
- 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).
- Giri, Prashant & Sharma, Tarun, 2024. "Market instrument for the first fuel and its role in decarbonizing Indian industrial production," Energy Policy, Elsevier, vol. 190(C).
- 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.
- Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
- 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.
- Zhu, Jingjing & Huang, Tianyuan, 2024. "Public debt and welfare with machine learning," Finance Research Letters, Elsevier, vol. 69(PA).
- 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.
- Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Discussion Paper series in Regional Science & Economic Geography 2020-06, Gran Sasso Science Institute, Social Sciences, revised Oct 2020.
- Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Working Papers 14/20, Sapienza University of Rome, DISS.
- Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
- Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.
- Brown, David P. & Cajueiro, Daniel O. & Eckert, Andrew & Silveira, Douglas, 2024. "Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets," Working Papers 2024-2, University of Alberta, Department of Economics.
- Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023.
"Towards data-driven project design: Providing optimal treatment rules for development projects,"
Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
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