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The Quadratic Assignment Procedure (QAP)

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  • William Simpson

    (Harvard Business School)

Abstract

Some data sets contain observations corresponding to pairs of entities (people, companies, countries, etc.). Conceptually, each observation corresponds to a cell in a square matrix, where the rows and columns are labelled by the entities. For example, consider a square matrix where the rows and columns are the 50 U.S. states. Each observation would contain numbers such as the distance between the pair of states, exports from one state to the other, etc. The observations are not independent, so estimation procedures designed for independent observations will calculate incorrect standard errors. The quadratic assignment procedure (QAP), which is commonly used in social network analysis, is a resampling-based method, similar to the bootstrap, for calculating the correct standard errors. This talk explains the QAP algorithm and describes the -qap- command, with syntax similar to -bstrap- command, which implements the quadratic assignment procedure and allows running any estimation command using QAP samples.

Suggested Citation

  • William Simpson, 2001. "The Quadratic Assignment Procedure (QAP)," North American Stata Users' Group Meetings 2001 1.2, Stata Users Group.
    • Handle: RePEc:boc:asug01:1.2
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    Cited by:

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    2. Galik, Christopher S., 2015. "Exploring the determinants of emerging bioenergy market participation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 107-116.
    3. Mohammed Ismail Alhussam & Jifan Ren & Hongxing Yao & Omar Abu Risha, 2023. "Food Trade Network and Food Security: From the Perspective of Belt and Road Initiative," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    4. Susanna Gallani, 2015. "Through the Grapevine: Network Effects on the Design of Executive Compensation Contracts," Harvard Business School Working Papers 16-019, Harvard Business School, revised Dec 2016.
    5. Aram Min & Ji-Hyun Lee, 2019. "A Conceptual Framework for the Externalization of Ecological Wisdom: The Case of Traditional Korean Gardens," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    6. Kasarjyan, Milada, 2011. "Improving the functioning of the rural financial markets of Armenia," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 62, number 62.
    7. Minha Hwang & Bart J. Bronnenberg & Raphael Thomadsen, 2010. "An Empirical Analysis of Assortment Similarities Across U.S. Supermarkets," Marketing Science, INFORMS, vol. 29(5), pages 858-879, 09-10.
    8. Changping Zhao & Xinli Qi & Jin Wang & Fengyang Du & Xiaolan Shi, 2022. "Predicting Possible New Links to Future Global Plastic Waste Trade Networks," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
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    11. Xiaoyi Shi & Xiaoxia Huang & Huifang Liu, 2022. "Research on the Structural Features and Influence Mechanism of the Low-Carbon Technology Cooperation Network Based on Temporal Exponential Random Graph Model," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    12. Zachary Neal, 2010. "Refining the Air Traffic Approach to City Networks," Urban Studies, Urban Studies Journal Limited, vol. 47(10), pages 2195-2215, September.
    13. Elina H. Hwang & Param Vir Singh & Linda Argote, 2015. "Knowledge Sharing in Online Communities: Learning to Cross Geographic and Hierarchical Boundaries," Organization Science, INFORMS, vol. 26(6), pages 1593-1611, December.
    14. Lai, Richard, 2005. "The Geography of Retail Inventory," MPRA Paper 4755, University Library of Munich, Germany.
    15. Ziming Yan & Xiaojuan Qiu & Debin Du & Seamus Grimes, 2022. "Transboundary Water Cooperation in the Post-Cold War Era: Spatial Patterns and the Role of Proximity," IJERPH, MDPI, vol. 19(3), pages 1-19, January.
    16. Xi Chen, 2014. "Gift-Giving and Network Structure in Rural China: Utilizing Long-Term Spontaneous Gift Records," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-14, August.
    17. Ray Reagans, 2005. "Preferences, Identity, and Competition: Predicting Tie Strength from Demographic Data," Management Science, INFORMS, vol. 51(9), pages 1374-1383, September.
    18. James R. Barth & Sunghoon Joo & Kang‐Bok Lee, 2022. "Bank–client cross‐ownership of bank stocks: A network analysis," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 45(2), pages 280-312, June.

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