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Inference for structural equation modelling on dependent populations


  • Savas Papadopoulos


Latent variable modelling is used widely in applications to economics, social and behavioural sciences. Since the normality-based model fitting procedures are simple and broadly available, and since such procedures are often applied to non-normal data or non-random samples, it is important to investigate the appropriateness of such practice and to suggest simple remedies. This paper addresses these issues for the analysis of multiple populations. For a very general class of latent variable models, a particular parameterisation is used for meaningful and interpretable analysis of several populations. It turns out that under this parameterisation the large sample statistical inferences based on the assumption of normal and independent populations are valid for virtually any non-normal and dependent populations. This result is also valid when some latent variables are treated as fixed instead of random, or when a group of individuals is measured over several time points longitudinally.

Suggested Citation

  • Savas Papadopoulos, 2011. "Inference for structural equation modelling on dependent populations," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 2(2), pages 123-153.
  • Handle: RePEc:ids:ijcome:v:2:y:2011:i:2:p:123-153

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    References listed on IDEAS

    1. Moawia Alghalith, 2006. "Hedging under price and output uncertainty: revisited," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 2(4), pages 243-245, July.
    2. Moawia Alghalith, 2003. "Hedging Output Price and Cost Uncertainty," Discussion Paper Series, Department of Economics 200305, Department of Economics, University of St. Andrews.
    3. Moawia Alghalith, 2005. "Estimation with price and output uncertainty," Journal of Applied Economics, Universidad del CEMA, vol. 8, pages 247-257, November.
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