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 (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:

• Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2020, "Making text count: economic forecasting using newspaper text," Bank of England working papers, Bank of England, number 865, May.
• Paolo BRUNORI, & Guido NEIDHOEFER, 2020, "The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach," Working Papers - Economics, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa, number wp2020_02.rdf.
• Marcin Chlebus & Zuzanna Osika, 2020, "Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-15.
• Michael Puglia & Adam Tucker, 2020, "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-038, May, DOI: 10.17016/FEDS.2020.038.
• Escribano, Álvaro & Wang, Dandan, 2020, "Forecasting gasoline prices with mixed random forest error correction models," UC3M Working papers. Economics, Universidad Carlos III de Madrid. Departamento de Economía, number 30557, Jun.
• 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, University Library of Munich, Germany, number 99075, Feb.
• Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020, "Using Machine Learning to Forecast Future Earnings," Papers, arXiv.org, number 2005.13995, May.
• Alexandre Boumezoued & Amal Elfassihi, 2020, "Mortality data correction in the absence of monthly fertility records," Working Papers, HAL, number hal-02634631, May.
• Yue Qiu & Tian Xie & Jun Yu, 2020, "Forecast combinations in machine learning," Economics and Statistics Working Papers, Singapore Management University, School of Economics, number 13-2020, May.
• Bart Cockx & Michael Lechner & Joost Bollens, 2020, "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," LIDAM Discussion Papers IRES, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES), number 2020016, May.
• Andrii Babii & Eric Ghysels & Jonas Striaukas, 2020, "Machine Learning Time Series Regressions with an Application to Nowcasting," Papers, arXiv.org, number 2005.14057, May, revised Dec 2020.
• Nino Antulov-Fantulin & Tian Guo & Fabrizio Lillo, 2020, "Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets," Papers, arXiv.org, number 2005.09356, May, revised Dec 2020.
• Roberto Baviera & Giuseppe Messuti, 2020, "Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England," Papers, arXiv.org, number 2005.13005, May, revised Oct 2020.
• Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020, "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers, Federal Reserve Bank of Cleveland, number 20-13R2, May, revised 22 Sep 2020, DOI: 10.26509/frbc-wp-202013r2.
• Subhadeep & Mukhopadhyay & Kaijun Wang, 2020, "Breiman's "Two Cultures" Revisited and Reconciled," Papers, arXiv.org, number 2005.13596, May.
• Andreas Gulyas & Krzysztof Pytka, 2020, "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," CRC TR 224 Discussion Paper Series, University of Bonn and University of Mannheim, Germany, number crctr224_2020_131v2, May.
• Item repec:spo:wpmain:info:hdl:2441/63csdfkqvu9nfanvuffe3qk8r6 is not listed on IDEAS anymore

• 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), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 120718, Jun.
• Schilirò, Daniele, 2020, "Towards digital globalization and the covid-19 challenge," MPRA Paper, University Library of Munich, Germany, number 100504, Apr, revised May 2020.
• Jarmulska, Barbara, 2020, "Random forest versus logit models: which offers better early warning of fiscal stress?," Working Paper Series, European Central Bank, number 2408, May.
• Michael Creel, 2020, "Inference Using Simulated Neural Moments," Working Papers, Barcelona School of Economics, number 1182, Jun.
• Michael Keane & Timothy Neal, 2020, "Consumer Panic in the COVID-19 Pandemic," Discussion Papers, School of Economics, The University of New South Wales, number 2020-06, May.
• Item repec:hal:wpaper:hal-02569351 is not listed on IDEAS anymore

• 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 Digital Labor : une nouvelle forme d’exploitation ," Post-Print, HAL, number hal-02554288, Apr, DOI: 10.21494/ISTE.OP.2020.0502.
• Beria, Paolo & Lunkar, Vardhman, 2020, "Presence and mobility of the population during Covid-19 outbreak and lockdown in Italy," MPRA Paper, University Library of Munich, Germany, number 100896, Jun.

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 (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: