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Econometrics at scale: Spark up big data in economics

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  • Bluhm, Benjamin
  • Cutura, Jannic

Abstract

This paper provides an overview of how to use \big data" for economic research. We investigate the performance and ease of use of different Spark applications running on a distributed file system to enable the handling and analysis of data sets which were previously not usable due to their size. More specifically, we explain how to use Spark to (i) explore big data sets which exceed retail grade computers memory size and (ii) run typical econometric tasks including microeconometric, panel data and time series regression models which are prohibitively expensive to evaluate on stand-alone machines. By bridging the gap between the abstract concept of Spark and ready-to-use examples which can easily be altered to suite the researchers need, we provide economists and social scientists more generally with the theory and practice to handle the ever growing datasets available. The ease of reproducing the examples in this paper makes this guide a useful reference for researchers with a limited background in data handling and distributed computing.

Suggested Citation

  • Bluhm, Benjamin & Cutura, Jannic, 2020. "Econometrics at scale: Spark up big data in economics," SAFE Working Paper Series 266, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:266
    DOI: 10.2139/ssrn.3226976
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    1. Duchin, Ran & Sosyura, Denis, 2014. "Safer ratios, riskier portfolios: Banks׳ response to government aid," Journal of Financial Economics, Elsevier, vol. 113(1), pages 1-28.
    2. Jankowitsch, Rainer & Nagler, Florian & Subrahmanyam, Marti G., 2014. "The determinants of recovery rates in the US corporate bond market," Journal of Financial Economics, Elsevier, vol. 114(1), pages 155-177.
    3. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
    4. Daniel S. Hamermesh, 2013. "Six Decades of Top Economics Publishing: Who and How?," Journal of Economic Literature, American Economic Association, vol. 51(1), pages 162-172, March.
    5. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    6. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    7. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    8. Boneva, Lena & Böninghausen, Benjamin & Letizia, Elisa & Rousová, Linda, 2019. "Derivatives transactions data and their use in central bank analysis," Economic Bulletin Articles, European Central Bank, vol. 6.
    9. Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, vol. 141(2), pages 597-620, December.
    10. Fernández-Villaverde, Jesús & Zarruk Valencia , David, 2018. "A Practical Guide to Parallelization in Economics," CEPR Discussion Papers 12890, C.E.P.R. Discussion Papers.
    11. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    12. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    13. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Hellen Paz & Mateus Maia & Fernando Moraes & Ricardo Lustosa & Lilia Costa & Samuel Macêdo & Marcos E. Barreto & Anderson Ara, 2020. "Local Processing of Massive Databases with R: A National Analysis of a Brazilian Social Programme," Stats, MDPI, vol. 3(4), pages 1-21, October.
    2. Paz, Hellen & Maia, Mateus & Moraes, Fernando & Lustosa, Ricardo & Costa, Lilia & Macêdo, Samuel & Barreto, Marcos E. & Ara, Anderson, 2020. "Local processing of massive databases with R: a national analysis of a Brazilian social programme," LSE Research Online Documents on Economics 115770, London School of Economics and Political Science, LSE Library.
    3. Aur'elien Ouattara & Matthieu Bult'e & Wan-Ju Lin & Philipp Scholl & Benedikt Veit & Christos Ziakas & Florian Felice & Julien Virlogeux & George Dikos, 2021. "Scalable Econometrics on Big Data -- The Logistic Regression on Spark," Papers 2106.10341, arXiv.org.

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    More about this item

    Keywords

    Econometrics; Distributed Computing; Apache Spark;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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