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A paradigm for assessing the scope and performance of predictive analytics

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  • Prince, Jeffrey T.

Abstract

In this paper, I outline possibilities and limitations for the scope and performance of predictive analytics within a simple paradigm. I do this by first bifurcating predictive analytics into two categories, passive and active. I contrast this categorization with current alternatives and highlight its relative merits in terms of clarity in boundaries, as well as appropriate methods for different types of prediction. I then describe the range of suitable applications, as well as the possibilities and limitations with regard to prediction accuracy, for each type of prediction. I conclude with a discussion of key ways in which an understanding of this paradigm can be valuable.

Suggested Citation

  • Prince, Jeffrey T., 2019. "A paradigm for assessing the scope and performance of predictive analytics," Information Economics and Policy, Elsevier, vol. 47(C), pages 7-13.
    • Handle: RePEc:eee:iepoli:v:47:y:2019:i:c:p:7-13
    DOI: 10.1016/j.infoecopol.2019.05.004
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    References listed on IDEAS

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    1. Jeffrey Prince & Shane Greenstein, 2014. "Does Service Bundling Reduce Churn?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 23(4), pages 839-875, December.
    2. McAleer, Michael & Pagan, Adrian R & Volker, Paul A, 1985. "What Will Take the Con out of Econometrics?," American Economic Review, American Economic Association, vol. 75(3), pages 293-307, June.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    5. Friedman, Milton & Schwartz, Anna J, 1991. "Alternative Approaches to Analyzing Economic Data," American Economic Review, American Economic Association, vol. 81(1), pages 39-49, March.
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