Report NEP-BIG-2020-06-15

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

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Other reports in NEP-BIG

The following items were announced in this report:

• Kalamara, Eleni & Turrell, Arthur & Redl, Chris & Kapetanios, George & Kapadia, Sujit, 2020. "Making text count: economic forecasting using newspaper text," Bank of England working papers 865, Bank of England.
• Paolo BRUNORI, & Guido NEIDHOEFER, 2020. "The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach," Working Papers - Economics wp2020_02.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
• Marcin Chlebus & Zuzanna Osika, 2020. "Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools," Working Papers 2020-15, Faculty of Economic Sciences, University of Warsaw.
• Michael Puglia & Adam Tucker, 2020. "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series 2020-038, Board of Governors of the Federal Reserve System (U.S.).
• Wang, Dandan, 2020. "Forecasting gasoline prices with mixed random forest error correction models," UC3M Working papers. Economics 30557, Universidad Carlos III de Madrid. Departamento de Economía.
• Maake, Witness & Van Zyl, Terence, 2020. "Applications of Machine Learning to Estimating the Sizes and Market Impact of Hidden Orders in the BRICS Financial Markets," MPRA Paper 99075, University Library of Munich, Germany.
• Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020. "Using Machine Learning to Forecast Future Earnings," Papers 2005.13995, arXiv.org.
• Alexandre Boumezoued & Amal Elfassihi, 2020. "Mortality data correction in the absence of monthly fertility records," Working Papers hal-02634631, HAL.
• Qiu, Yue & Xie, Tian & Yu, Jun, 2020. "Forecast combinations in machine learning," Economics and Statistics Working Papers 13-2020, Singapore Management University, School of Economics.
• Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," LIDAM Discussion Papers IRES 2020016, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
• Andrii Babii & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Time Series Regressions with an Application to Nowcasting," Papers 2005.14057, arXiv.org, revised Dec 2020.
• Nino Antulov-Fantulin & Tian Guo & Fabrizio Lillo, 2020. "Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets," Papers 2005.09356, arXiv.org, revised Dec 2020.
• Roberto Baviera & Giuseppe Messuti, 2020. "Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England," Papers 2005.13005, arXiv.org, revised Oct 2020.
• Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
• Subhadeep & Mukhopadhyay & Kaijun Wang, 2020. "Breiman's "Two Cultures" Revisited and Reconciled," Papers 2005.13596, arXiv.org.
• Andreas Gulyas & Krzysztof Pytka, 2020. "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," CRC TR 224 Discussion Paper Series crctr224_2020_131v2, University of Bonn and University of Mannheim, Germany.
• Yann Algan & Fabrice Murtin & Elizabeth Beasley & Kazuhito Higa & Claudia Senik, 2019. "Well-being through the Lens of the Internet," SciencePo Working papers Main halshs-02096551, HAL.
• Sturm, Timo & Peters, Felix, 2020. "The Impact of Artificial Intelligence on Individual Performance: Exploring the Fit between Task, Data, and Technology," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 120718, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Schilirò, Daniele, 2020. "Towards digital globalization and the covid-19 challenge," MPRA Paper 100504, University Library of Munich, Germany, revised May 2020.
• Jarmulska, Barbara, 2020. "Random forest versus logit models: which offers better early warning of fiscal stress?," Working Paper Series 2408, European Central Bank.
• Michael Creel, 2020. "Inference Using Simulated Neural Moments," Working Papers 1182, Barcelona School of Economics.
• Michael Keane & Timothy Neal, 2020. "Consumer Panic in the COVID-19 Pandemic," Discussion Papers 2020-06, School of Economics, The University of New South Wales.
• Federico Maria Ferrara & Jörg Haas & Andrew Peterson & Thomas Sattler, 2020. "Exports vs. Investment: How Public Discourse Shapes Support for External Imbalances ," Working Papers hal-02569351, HAL.
• Carlo Vercellone, 2020. "The "merchantable gratuitousness" platforms and the Free Digital Labor controversy: a new form of exploitation? [Les plateformes de la gratuité marchande et la controverse autour du Free ," Post-Print hal-02554288, HAL.
• Beria, Paolo & Lunkar, Vardhman, 2020. "Presence and mobility of the population during Covid-19 outbreak and lockdown in Italy," MPRA Paper 100896, University Library of Munich, Germany.