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Big Data in der makroökonomischen Analyse

Author

Listed:
  • Ademmer, Martin
  • Beckmann, Joscha
  • Bode, Eckhardt
  • Boysen-Hogrefe, Jens
  • Funke, Manuel
  • Hauber, Philipp
  • Heidland, Tobias
  • Hinz, Julian
  • Jannsen, Nils
  • Kooths, Stefan
  • Söder, Mareike
  • Stamer, Vincent
  • Stolzenburg, Ulrich
Abstract
Unter dem Schlagwort Big Data werden neue und in Abgrenzung zur üblichen Wirtschaftsstatistik unkonventionelle Datenquellen zusammengefasst. Sie sind sehr umfangreich und sehr zeitnah sowie in hoher Frequenz verfügbar. Allerdings weisen diese neuen Daten eine hohe Bandbreite und Komplexität auf, weil sie nicht für die Analyse von ökonomischen Fragestellungen erhoben werden, sondern vielmehr als Nebenprodukt unterschiedlicher Anwendungen anfallen. Vor diesem Hintergrund stellen die Autoren die Anwendungsfelder und Potenziale verschiedener Datenquellen aus dem Bereich Big Data in einem vergleichbaren Rahmen vor. Sie zeigen zudem mögliche zukünftige Potenziale von Big Data auf, die derzeit noch nicht nutzbar sind, weil beispielsweise die dafür notwendigen Daten noch nicht systematisch gesammelt oder erfasst werden. Sie schlussfolgern, dass Big Data in vielen Anwendungsfeldern vor allem komplementär zu den Daten der konventionellen Wirtschaftsstatistik zum Einsatz kommen werden.

Suggested Citation

  • Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkbw:32
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    as
    1. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2019. "Using Payment System Data to Forecast Economic Activity," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 55-80, October.
    2. Andrade, Sandro C. & Bian, Jiangze & Burch, Timothy R., 2013. "Analyst Coverage, Information, and Bubbles," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1573-1605, October.
    3. Bluhm, Richard & Krause, Melanie, 2022. "Top lights: Bright cities and their contribution to economic development," Journal of Development Economics, Elsevier, vol. 157(C).
    4. Caldara, Dario & Iacoviello, Matteo & Molligo, Patrick & Prestipino, Andrea & Raffo, Andrea, 2020. "The economic effects of trade policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 109(C), pages 38-59.
    5. Ademmer, Martin & Beckmann, Joscha & Jannsen, Nils, 2019. "Zu den Auswirkungen des jüngsten Anstiegs der globalen wirtschaftspolitischen Unsicherheit," Kiel Insight 2019.7, Kiel Institute for the World Economy (IfW Kiel).
    6. Giulia Brancaccio & Myrto Kalouptsidi & Theodore Papageorgiou, 2017. "Geography, Search Frictions and Endogenous Trade Costs," NBER Working Papers 23581, National Bureau of Economic Research, Inc.
    7. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    8. Alberto Cavallo & Gita Gopinath & Brent Neiman & Jenny Tang, 2021. "Tariff Pass-Through at the Border and at the Store: Evidence from US Trade Policy," American Economic Review: Insights, American Economic Association, vol. 3(1), pages 19-34, March.
    9. Martin Ademmer & Nils Jannsen & Stefan Kooths & Saskia Mösle, 2019. "Niedrigwasser bremst Produktion [Low tide slows production down]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 99(1), pages 79-80, January.
    10. Scott R. Baker & Andrey Fradkin, 2017. "The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 756-768, December.
    11. Alberto Cavallo, 2017. "Are Online and Offline Prices Similar? Evidence from Large Multi-channel Retailers," American Economic Review, American Economic Association, vol. 107(1), pages 283-303, January.
    12. Roar Adland & Haiying Jia & Siri P. Strandenes, 2017. "Are AIS-based trade volume estimates reliable? The case of crude oil exports," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(5), pages 657-665, July.
    13. Giuntella, Osea & Lonsky, Jakub & Mazzonna, Fabrizio & Stella, Luca, 2021. "Immigration policy and immigrants’ sleep. Evidence from DACA," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 1-12.
    14. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    15. Sandra Sequeira & Nathan Nunn & Nancy Qian, 2020. "Immigrants and the Making of America," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(1), pages 382-419.
    16. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    17. Alberto Cavallo, 2018. "Scraped Data and Sticky Prices," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 105-119, March.
    18. Bond, Timothy N. & Giuntella, Osea & Lonsky, Jakub, 2023. "Immigration and work schedules: Theory and evidence," European Economic Review, Elsevier, vol. 152(C).
    19. Henning Finseraas & Marianne Røed & Pål Schøne, 2020. "Labour immigration and union strength," European Union Politics, , vol. 21(1), pages 3-23, March.
    20. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    21. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
    22. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    23. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    24. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    25. Castelnuovo, Efrem & Tran, Trung Duc, 2017. "Google It Up! A Google Trends-based Uncertainty index for the United States and Australia," Economics Letters, Elsevier, vol. 161(C), pages 149-153.
    26. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    27. Saidi, Farzad & Alfaro, Laura & Faia, Ester & Lamersdorf, Nora, 2020. "Social Interactions in Pandemics: Fear, Altruism, and Reciprocity," CEPR Discussion Papers 14716, C.E.P.R. Discussion Papers.
    28. Diane Alexander & Ezra Karger, 2023. "Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 1017-1027, July.
    29. Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020. "Economic policy uncertainty in the euro area: an unsupervised machine learning approach," Working Paper Series 2359, European Central Bank.
    30. Alberto Cavallo & W. Erwin Diewert & Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2018. "Using Online Prices for Measuring Real Consumption across Countries," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 483-487, May.
    31. Benjamin Born & Michael Ehrmann & Marcel Fratzscher, 2014. "Central Bank Communication on Financial Stability," Economic Journal, Royal Economic Society, vol. 124(577), pages 701-734, June.
    32. Arbatli Saxegaard, Elif C. & Davis, Steven J. & Ito, Arata & Miake, Naoko, 2022. "Policy uncertainty in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    33. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    34. Aditya Aladangady & Shifrah Aron-Dine & Wendy Dunn & Laura Feiveson & Paul Lengermann & Claudia Sahm, 2021. "From Transaction Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 115-145, National Bureau of Economic Research, Inc.
    35. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    36. Alberto Cavallo & Brent Neiman & Roberto Rigobon, 2014. "Currency Unions, Product Introductions, and the Real Exchange Rate," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(2), pages 529-595.
    37. 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.
    38. Martin Ademmer & Nils Jannsen, 2019. "Globale Unsicherheit und deutsche Konjunktur [Global uncertainty and the Germany economy]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 99(7), pages 519-520, July.
    39. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    40. Bilgin, Mehmet Huseyin & Demir, Ender & Gozgor, Giray & Karabulut, Gokhan & Kaya, Huseyin, 2019. "A novel index of macroeconomic uncertainty for Turkey based on Google-Trends," Economics Letters, Elsevier, vol. 184(C).
    41. Ademmer, Martin & Bickenbach, Frank & Bode, Eckhardt & Boysen-Hogrefe, Jens & Fiedler, Salomon & Gern, Klaus-Jürgen & Görg, Holger & Groll, Dominik & Hornok, Cecília & Jannsen, Nils & Kooths, Stefan &, 2017. "Produktivität in Deutschland: Messbarkeit und Entwicklung," Kieler Beiträge zur Wirtschaftspolitik 12, Kiel Institute for the World Economy (IfW Kiel).
    42. Bratu, Cristina & Dahlberg, Matz & Engdahl, Mattias & Nikolka, Till, 2020. "Spillover effects of stricter immigration policies," Journal of Public Economics, Elsevier, vol. 190(C).
    43. M. E. Bontempi & R. Golinelli & M. Squadrani, 2016. "A New Index of Uncertainty Based on Internet Searches: A Friend or Foe of Other Indicators?," Working Papers wp1062, Dipartimento Scienze Economiche, Universita' di Bologna.
    44. Bianchi, Francesco & Kind, Thilo & Kung, Howard, 2019. "Threats to Central Bank Independence: High-Frequency Identification with Twitter," CEPR Discussion Papers 14021, C.E.P.R. Discussion Papers.
    45. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    46. Theodore Papageorgiou & Myrto Kalouptsidi & Giulia Brancaccio, 2017. "Geography, Search Frictions and Trade Costs," 2017 Meeting Papers 1105, Society for Economic Dynamics.
    47. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    48. Nikolaos Askitas & Klaus F. Zimmermann, 2013. "Nowcasting Business Cycles Using Toll Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 299-306, July.
    49. Furtado, Delia & Ortega, Francesc, 2020. "Does Immigration Improve Quality of Care in Nursing Homes?," IZA Discussion Papers 13552, Institute of Labor Economics (IZA).
    50. Sumit Agarwal & Wenlan Qian, 2014. "Consumption and Debt Response to Unanticipated Income Shocks: Evidence from a Natural Experiment in Singapore," American Economic Review, American Economic Association, vol. 104(12), pages 4205-4230, December.
    51. Giulia Brancaccio & Myrto Kalouptsidi & Theodore Papageorgiou, 2020. "Geography, Transportation, and Endogenous Trade Costs," Econometrica, Econometric Society, vol. 88(2), pages 657-691, March.
    52. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    53. Jonah Busch & Kalifi Ferretti-Gallon, 2017. "What Drives Deforestation and What Stops It? A Meta-Analysis," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 11(1), pages 3-23.
    54. Addison,Douglas M. & Stewart,Benjamin P., 2015. "Nighttime lights revisited : the use of nighttime lights data as a proxy for economic variables," Policy Research Working Paper Series 7496, The World Bank.
    55. Michael Bailey & Ruiqing Cao & Theresa Kuchler & Johannes Stroebel, 2018. "The Economic Effects of Social Networks: Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2224-2276.
    56. Daas, Piet J.H. & Puts, Marco J.H., 2014. "Social media sentiment and consumer confidence," Statistics Paper Series 5, European Central Bank.
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    1. Carsten Juergens & Fabian M. Meyer-Heß & Marcus Goebel & Torsten Schmidt, 2021. "Remote Sensing for Short-Term Economic Forecasts," Sustainability, MDPI, vol. 13(17), pages 1-23, August.

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    Keywords

    Big Data; makroökonomische Analyse; Konjunktur; Konjunktur Deutschland; MachineLearning;
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