IDEAS home Printed from https://ideas.repec.org/h/nbr/nberch/14278.html
   My bibliography  Save this book chapter

Using Public Data to Generate Industrial Classification Codes

In: Big Data for Twenty-First-Century Economic Statistics

Author

Listed:
  • John Cuffe
  • Sudip Bhattacharjee
  • Ugochukwu Etudo
  • Justin C. Smith
  • Nevada Basdeo
  • Nathaniel Burbank
  • Shawn R. Roberts

Abstract

No abstract is available for this item.

Suggested Citation

  • John Cuffe & Sudip Bhattacharjee & Ugochukwu Etudo & Justin C. Smith & Nevada Basdeo & Nathaniel Burbank & Shawn R. Roberts, 2019. "Using Public Data to Generate Industrial Classification Codes," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 229-246, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14278
    as

    Download full text from publisher

    File URL: http://www.nber.org/chapters/c14278.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John Cuffe & Nathan Goldschlag, 2018. "Squeezing More Out of Your Data: Business Record Linkage with Python," Working Papers 18-46, Center for Economic Studies, U.S. Census Bureau.
    2. Ikudo, Akina & Lane, Julia & Staudt, Joseph & Weinberg, Bruce A., 2018. "Occupational Classifications: A Machine Learning Approach," IZA Discussion Papers 11738, Institute of Labor Economics (IZA).
    3. Muchlinski, David & Siroky, David & He, Jingrui & Kocher, Matthew, 2016. "Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data," Political Analysis, Cambridge University Press, vol. 24(1), pages 87-103, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
    2. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
    3. David Siroky & Carolyn M. Warner & Gabrielle Filip-Crawford & Anna Berlin & Steven L. Neuberg, 2020. "Grievances and rebellion: Comparing relative deprivation and horizontal inequality," Conflict Management and Peace Science, Peace Science Society (International), vol. 37(6), pages 694-715, November.
    4. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    5. Monirah Ali Aleisa & Natalia Beloff & Martin White, 2023. "Implementing AIRM: a new AI recruiting model for the Saudi Arabia labour market," Journal of Innovation and Entrepreneurship, Springer, vol. 12(1), pages 1-41, December.
    6. Joseph Staudt & Yifang Wei & Lisa Singh & Shawn Klimek & J. Bradford Jensen & Andrew Baer, 2019. "Automating Response Evaluation for Franchising Questions on the 2017 Economic Census," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 209-227, National Bureau of Economic Research, Inc.
    7. Fariha Kamal & Wei Ouyang, 2020. "Identifying U.S. Merchandise Traders: Integrating Customs Transactions with Business Administrative Data," Working Papers 20-28, Center for Economic Studies, U.S. Census Bureau.
    8. Zhaochen He & John Camobreco & Keith Perkins, 2022. "How he won: Using machine learning to understand Trump’s 2016 victory," Journal of Computational Social Science, Springer, vol. 5(1), pages 905-947, May.
    9. Phil Henrickson, 2020. "Predicting the costs of war," The Journal of Defense Modeling and Simulation, , vol. 17(3), pages 285-308, July.
    10. Marie K. Schellens & Salim Belyazid, 2020. "Revisiting the Contested Role of Natural Resources in Violent Conflict Risk through Machine Learning," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
    11. Giacomo Caterini, 2018. "Classifying Firms with Text Mining," DEM Working Papers 2018/09, Department of Economics and Management.
    12. Felix Ettensperger, 2020. "Comparing supervised learning algorithms and artificial neural networks for conflict prediction: performance and applicability of deep learning in the field," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 567-601, April.
    13. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    14. Freire, Danilo, 2021. "Democratizing Policy Analytics with AutoML," Working Papers 11015, George Mason University, Mercatus Center.
    15. Liam F. Beiser-McGrath & Robert A. Huber, 2018. "Assessing the relative importance of psychological and demographic factors for predicting climate and environmental attitudes," Climatic Change, Springer, vol. 149(3), pages 335-347, August.
    16. Güneş Murat Tezcür & Clayton Besaw, 2020. "Jihadist waves: Syria, the Islamic State, and the changing nature of foreign fighters," Conflict Management and Peace Science, Peace Science Society (International), vol. 37(2), pages 215-231, March.
    17. Glennon, Britta & Lane, Julia & Sodhi, Ridhima, 2018. "Money for Something: The Links between Research Funding and Innovation," IZA Discussion Papers 11711, Institute of Labor Economics (IZA).
    18. Antonietta di Salvatore & Mirko Moscatelli, 2024. "Improving survey information on household debt using granular credit databases," Questioni di Economia e Finanza (Occasional Papers) 839, Bank of Italy, Economic Research and International Relations Area.
    19. Vestby, Jonas & Buhaug, Halvard & von Uexkull, Nina, 2021. "Why do some poor countries see armed conflict while others do not? A dual sector approach," World Development, Elsevier, vol. 138(C).
    20. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.

    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberch:14278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.