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Linear Dynamic Panel-Data Estimation using Maximum Likelihood and Structural Equation Modeling


  • Richard Williams

    (University of Notre Dame, Department of Sociology)

  • Paul Allison

    (University of Pennsylvania, Sociology)

  • Enrique Moral Benito

    (Banco de Espana Madrid)


Panel data make it possible both to control for unobserved confounders and to include lagged, endogenous regressors. Trying to do both at the same time, however, leads to serious estimation difficulties. In the econometric literature, these problems have been solved by using lagged instrumental variables together with the generalized method of moments (GMM). In Stata, commands such as xtabond and xtdpdsys have been used for these models. Here we show that the same problems can be addressed via maximum likelihood estimation implemented with Stata’s structural equation modeling (sem) command. We show that the ML (sem) method is substantially more efficient than the GMM method when the normality assumption is met and suffers less from finite sample biases. We introduce a command named xtdpdml with syntax similar to other Stata commands for linear dynamic panel-data estimation. xtdpdml simplifies the SEM model specification process; makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models; and takes advantage of Stata’s ability to use full information maximum likelihood (FIML) for dealing with missing data.

Suggested Citation

  • Richard Williams & Paul Allison & Enrique Moral Benito, 2015. "Linear Dynamic Panel-Data Estimation using Maximum Likelihood and Structural Equation Modeling," 2015 Stata Conference 11, Stata Users Group.
  • Handle: RePEc:boc:scon15:11

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    Cited by:

    1. Sebastian Kripfganz, 2016. "Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models," Stata Journal, StataCorp LP, vol. 16(4), pages 1013-1038, December.
    2. Joo Hun Han & DuckJung Shin & William G. Castellano, & Alison M. Konrad & Douglas L. Kruse & Joseph R. Blasi, 2020. "Creating Mutual Gains to Leverage a Racially Diverse Workforce: The Effects of Firm-Level Racial Diversity on Financial and Workforce Outcomes Under the Use of Broad-Based Stock Options," Organization Science, INFORMS, vol. 31(6), pages 1515-1537, November.
    3. Suale Karimu, 2019. "Structural transformation, openness, and productivity growth in sub-Saharan Africa," WIDER Working Paper Series wp-2019-109, World Institute for Development Economic Research (UNU-WIDER).
    4. Dao, Nguyen Dinh, 2020. "Does the microcredit intervention change the life of the low- and middle-income households in rural Vietnam? Evidence from panel data," World Development Perspectives, Elsevier, vol. 20(C).
    5. Wu, Hania Fei, 2021. "Social determination, health selection or indirect selection? Examining the causal directions between socioeconomic status and obesity in the Chinese adult population," Social Science & Medicine, Elsevier, vol. 269(C).
    6. Matthew Thomas Clement & Nathan W. Pino & Jarrett Blaustein, 2019. "Homicide Rates and the Multiple Dimensions of Urbanization: A Longitudinal, Cross-National Analysis," Sustainability, MDPI, Open Access Journal, vol. 11(20), pages 1-16, October.

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