Report NEP-BIG-2022-08-22

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report:

• Zarak Jamal Khan, 2021, "Machine Learning: An Introduction for Economists," PIDE Webinar Brief, Pakistan Institute of Development Economics, number 2021:62.
• Badruddoza, Syed & Fuad, Syed M. & Amin, Modhurima, 2022, "Comparative Effectiveness of Machine Learning Methods for Causal Inference in Agricultural Economics," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322452, Aug, DOI: 10.22004/ag.econ.322452.
• Emily Aiken & Guadalupe Bedoya & Joshua Blumenstock & Aidan Coville, 2022, "Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan," Papers, arXiv.org, number 2206.11400, Jun.
• Sylvia Klosin & Max Vilgalys, 2022, "Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application," Papers, arXiv.org, number 2207.08789, Jul, revised Oct 2025.
• Jimei Shen & Zhehu Yuan & Yifan Jin, 2022, "AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment," Papers, arXiv.org, number 2206.11072, Jun, revised Dec 2022.
• Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022, "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers, arXiv.org, number 2207.04368, Jul, revised Oct 2022.
• Goller, Daniel & Diem, Andrea & Wolter, Stefan C., 2022, "Sitting Next to a Dropout: Academic Success of Students with More Educated Peers," IZA Discussion Papers, Institute of Labor Economics (IZA), number 15378, Jun.
• Emanuel Kohlscheen & Richhild Moessner, 2022, "Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix," CESifo Working Paper Series, CESifo, number 9807.
• Gafarov, Bulat & Gong, Tengda & Hilscher, Jens, 2022, "Cyclical and Trend Variation in Demand Elasticity: Big data evidence from US grocery stores," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322430, Aug, DOI: 10.22004/ag.econ.322430.
• Stich, Christoph & Tranos, Emmanouil & Nathan, Max, 2023, "Modeling clusters from the ground up: a web data approach," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 115565, Jan.
• Federico Mecchia & Marcellino Gaudenzi, 2022, "The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network," Papers, arXiv.org, number 2206.09899, Jun.
• Nicolas Herault & Stephen P. Jenkins, , "The t-statistic approach to inference for inequality indices: the issue of grouping variability," Working Papers, ECINEQ, Society for the Study of Economic Inequality, number ecineq-.
• Mathias Lindholm & Ronald Richman & Andreas Tsanakas & Mario V. Wuthrich, 2022, "A multi-task network approach for calculating discrimination-free insurance prices," Papers, arXiv.org, number 2207.02799, Jul.
• Paolo Brunori & Pedro Salas-Rojo & Paolo Verme, 2022, "Estimating Inequality with Missing Incomes," Working Papers, ECINEQ, Society for the Study of Economic Inequality, number 616, Jul.
• Man-, ZuyiKeunZuyi Wang & Takagi, Chifumi & Kim, Man-Keun & Chung, Anh, 2022, "Uncover Drivers Influencing Consumers' WTP Using Machine Learning: Case of Organic Coffee in Taiwan," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322150, Aug, DOI: 10.22004/ag.econ.322150.
• Jungyu Ahn & Sungwoo Park & Jiwoon Kim & Ju-hong Lee, 2022, "Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation," Papers, arXiv.org, number 2207.02458, Jul.
• Charl Maree & Christian W. Omlin, 2022, "Balancing Profit, Risk, and Sustainability for Portfolio Management," Papers, arXiv.org, number 2207.02134, Jun.
• Dimitrios Vamvourellis & Mate Attila Toth & Dhruv Desai & Dhagash Mehta & Stefano Pasquali, 2022, "Learning Mutual Fund Categorization using Natural Language Processing," Papers, arXiv.org, number 2207.04959, Jul.
• Carol Corrado & Jonathan Haskel & Massimiliano Iommi & Cecilia Jona-Lasinio, 2022, "The value of data in digital-based business models: Measurement and economic policy implications," OECD Economics Department Working Papers, OECD Publishing, number 1723, Aug, DOI: 10.1787/d960a10c-en.
• Merfeld, Joshua D. & Newhouse, David & Weber, Michael & Lahiri, Partha, 2022, "Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes," IZA Discussion Papers, Institute of Labor Economics (IZA), number 15390, Jun.
• Merlin Stein, 2022, "When are large female-led firms more resilient against shocks? Learnings from Indian enterprises during COVID-19 with diff-in-diff and causal forests," CSAE Working Paper Series, Centre for the Study of African Economies, University of Oxford, number 2022-01, Jan.
• Jonathan Chiu & Thorsten Koeppl, 2022, "PayTech and the D(ata) N(etwork) A(ctivities) of BigTech Platforms," Staff Working Papers, Bank of Canada, number 22-35, Aug, DOI: 10.34989/swp-2022-35.

Report NEP-BIG-2022-08-22

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: