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Multilevel multiprocess modeling with gsem

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  • Tamás Bartus

    (Corvinus University of Budapest)

Abstract

Multilevel multiprocess models are simultaneous equation systems that include multilevel hazard equations with correlated random effects. Demog- raphers routinely use these models to adjust estimates for endogeneity and sample selection. In this article, I demonstrate how multilevel multiprocess models can be fit with the gsem command. I distinguish between two classes of multilevel multiprocess models: nonrecursive systems of hazard equations without observed endogenous variables and recursive systems that include a hazard equation with ob- served endogenous qualitative variables. I illustrate the estimation of both classes of models using sample datasets shipped with the statistical software aML. I pay special attention to identifying structural coefficients in nonrecursive simultaneous systems.

Suggested Citation

  • Tamás Bartus, 2017. "Multilevel multiprocess modeling with gsem," Stata Journal, StataCorp LP, vol. 17(2), pages 442-461, June.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:2:p:442-461
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    Cited by:

    1. Aguilera-García, Álvaro & Gomez, Juan & Velázquez, Guillermo & Vassallo, Jose Manuel, 2022. "Ridesourcing vs. traditional taxi services: Understanding users’ choices and preferences in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 161-178.
    2. Loaba, Salamata, 2022. "The impact of mobile banking services on saving behavior in West Africa," Global Finance Journal, Elsevier, vol. 53(C).
    3. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
    4. Elijah N. Muange & Marther W. Ngigi, 2021. "Dietary quality and overnutrition among adults in Kenya: what role does ICT play?," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(4), pages 1013-1028, August.
    5. Xin, Mengwei & Shalaby, Amer, 2024. "Investigation of the interaction between urban rail ridership and network topology characteristics using temporal lagged and reciprocal effects: A case study of Chengdu, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    6. Aguilera-García, Álvaro & Gomez, Juan & Antoniou, Constantinos & Vassallo, José Manuel, 2022. "Behavioral factors impacting adoption and frequency of use of carsharing: A tale of two European cities," Transport Policy, Elsevier, vol. 123(C), pages 55-72.

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