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Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data

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  • Alt, James E.
  • King, Gary
  • Signorino, Curtis S.

Abstract

Binary, count, and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for which known statistical methods can estimate the same parameters—and it is generally used only for count and duration data. The result is that seemingly trivial decisions about which level of data to use can have important consequences for substantive interpretations. We describe the theoretical event process for which results exist, based on time independence. We also derive a set of models for a time-dependent process and compare their predictions to those of a commonly used model. Any hope of understanding and avoiding the more serious problems of aggregation bias in events data is contingent on first deriving a much wider arsenal of statistical models and theoretical processes that are not constrained by the particular forms of data that happen to be available. We discuss these issues and suggest an agenda for political methodologists interested in this very large class of aggregation problems.

Suggested Citation

  • Alt, James E. & King, Gary & Signorino, Curtis S., 2001. "Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data," Political Analysis, Cambridge University Press, vol. 9(1), pages 21-44, January.
  • Handle: RePEc:cup:polals:v:9:y:2001:i:01:p:21-44_00
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    Cited by:

    1. Alandejani, Maha & Kutan, Ali M. & Samargandi, Nahla, 2017. "Do Islamic banks fail more than conventional banks?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 135-155.
    2. Sarah E. Croco & Tze Kwang Teo, 2005. "Assessing the Dyadic Approach to Interstate Conflict Processes: A.k.a. “Dangerous†Dyad-Years," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(1), pages 5-18, February.
    3. Elizabeth A. Stanley & John P. Sawyer, 2009. "The Equifinality of War Termination," Journal of Conflict Resolution, Peace Science Society (International), vol. 53(5), pages 651-676, October.
    4. Bhati, Avinash, 2007. "Learning from multiple analogies: an Information Theoretic framework for predicting criminal recidivism," MPRA Paper 11850, University Library of Munich, Germany.
    5. Jong Hee Park, 2010. "Structural Change in U.S. Presidents' Use of Force," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 766-782, July.
    6. Halvard Buhaug, 2005. "Dangerous Dyads Revisited: Democracies May Not Be That Peaceful After All," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(2), pages 95-111, April.
    7. Sam R Bell & Amanda Murdie, 2018. "The apparatus for violence: Repression, violent protest, and civil war in a cross-national framework," Conflict Management and Peace Science, Peace Science Society (International), vol. 35(4), pages 336-354, July.
    8. Vito D'Orazio & James E Yonamine, 2015. "Kickoff to Conflict: A Sequence Analysis of Intra-State Conflict-Preceding Event Structures," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-21, May.
    9. Raynee Gutting & Martin C. Steinwand, 2017. "Donor Fragmentation, Aid Shocks, and Violent Political Conflict," Journal of Conflict Resolution, Peace Science Society (International), vol. 61(3), pages 643-670, March.

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