nep-big New Economics Papers
on Big Data
Issue of 2017‒07‒02
five papers chosen by
Tom Coupé
University of Canterbury

  1. Big Data and Unemployment Analysis By Simionescu, Mihaela; Zimmermann, Klaus F.
  2. Forecasting GDP growth from the outer space By Jaqueson K Galimberti
  3. Using Machine Learning To Model Interaction Effects In Education: A Graphical Approach By Fritz Schiltz; Chiara Masci; Tommaso Agasisti; Daniel Horn
  4. Forming a Representative Sample in the Analysis of Large Data By Alexandrov, Mikhail; Stefanovskiy, Dmitry
  5. Ordnungspolitik in der digitalen Welt By Haucap, Justus; Heimeshoff, Ulrich

  1. By: Simionescu, Mihaela; Zimmermann, Klaus F.
    Abstract: Internet or "big" data are increasingly measuring the relevant activities of individuals, households, firms and public agents in a timely way. The information set involves large numbers of observations and embraces flexible conceptual forms and experimental settings. Therefore, internet data are extremely useful to study a wide variety of human resource issues including forecasting, nowcasting, detecting health issues and well-being, capturing the matching process in various parts of individual life, and measuring complex processes where traditional data have known deficits. We focus here on the analysis of unemployment by means of internet activity data, a literature starting with the seminal article of Askitas and Zimmermann (2009a). The article provides insights and a brief overview of the current state of research.
    Keywords: big data,unemployment,internet,Google,internet penetration rate
    JEL: C22 C82 E17 E24 E37
    Date: 2017
  2. By: Jaqueson K Galimberti (KOF Swiss Economic Institute, ETH Zurich, Switzerland)
    Abstract: We evaluate the usefulness of satellite-based data on nighttime lights for the prediction of annual GDP growth across a global sample of countries. Going beyond traditional measures of luminosity, such as the sum of lights within a country’s borders, we propose several innovative distribution- and location-based indicators attempting to extract new predictive information from the night lights data. Whereas our ?ndings are generally favorable to the use of the night lights data to improve the accuracy of simple autoregressive model-based forecasts, we also ?nd a substantial degree of heterogeneity across countries on the estimated relationships between light emissions and economic activity: individually estimated models tend to outperform pooled speci?cations, even though the latter provide more ef?cient estimates for out-of-sample forecasting. The estimation uncertainty affecting the country-speci?c estimates tends to be more pronounced for low and lower middle income countries. We conduct bootstrapped inference in order to evaluate the statistical signi?cance of our results.
    Date: 2017–02
  3. By: Fritz Schiltz (Leuven Economics of Education Research, University of Leuven, Belgium); Chiara Masci (Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Italy); Tommaso Agasisti (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Italy); Daniel Horn (Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences)
    Abstract: Educational systems can be characterized by a complex structure: students, classes and teachers, schools and principals, and providers of education. The added value of schools is likely influenced by all these levels and, especially, by interactions between them. We illustrate the ability of Machine Learning (ML) methods (Regression Trees, Random Forests and Boosting) to model this complex ‘education production function’ using Hungarian data. We find that, in contrast to ML methods, classical regression approaches fail to identify relevant nonlinear interactions such as the role of school principals to accommodate district size policies. We visualize nonlinear interaction effects in a way that can be easily interpreted.
    Keywords: machine learning, education production function, interaction effects, non-linear effects
    JEL: C5 C18 C49 I21 H75
    Date: 2017–06
  4. By: Alexandrov, Mikhail (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Stefanovskiy, Dmitry (Russian Presidential Academy of National Economy and Public Administration (RANEPA))
    Abstract: The working paper contains the results of research of the employees of the International Laboratory of Mathematical Methods for the Study of Social Networks in 2016, according to one of the directions of the laboratory's work connected with the processing of big data.
    Date: 2017–05
  5. By: Haucap, Justus; Heimeshoff, Ulrich
    Abstract: Der vorliegende Beitrag erörtert die wichtigsten Herausforderungen, die sich durch die Digitalisierung stellen. Analysiert werden die Fragen, inwiefern die die Digitalisierung zu einer Monopolisierung von Märkten führt, wie das Kartellrecht nach der 9. GWBNovelle diese Befürchtungen adressiert und welche datengetriebene Wettbewerbsveränderungen sich durch Daten als Wettbewerbsfaktor ergibt. Weitere Themen sind Big Data und Preisdifferenzierung, Geschäftsmodelle und Regulierung der Sharing Economy, Breitbandausbau und Vectoring, die Kartellrechtsanwendung auf Online-Märkten am Beispiel von Google Shopping, Doppelpreissysteme und Bestpreisklauseln, Car Sharing und Ride Sharing, Amazon und die Buchpreisbindung für Ebooks und der Wandel der Medienlandschaft.
    Keywords: Digitalisierung,Sharing Economy,Big Data,GWB-Novelle,Vectoring,Breitband,Kartellrecht,Amazon,Ebook
    JEL: D4 K2 L1 L4 L9
    Date: 2017

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