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Two applications of the random coefficient procedure: Correcting for misspecifications in a small area level model and resolving Simpson's paradox

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  • Swamy, P.A.V.B.
  • Mehta, J.S.
  • Tavlas, G.S.
  • Hall, S.G.

Abstract

We apply a random-coefficient framework to deal with two problems frequently encountered in applied work. First, we use a real-world relationship to derive a sub-relationship among fewer variables without introducing any specification error to correct misspecifications in a small area level model. Second, we then use this framework to resolve Simpson's paradox. We show that this paradox does not arise if a statistical relationship between a pair of variables is derived from the corresponding real-world relationship involving all relevant variables, including the original pair, without introducing a single specification error.

Suggested Citation

  • Swamy, P.A.V.B. & Mehta, J.S. & Tavlas, G.S. & Hall, S.G., 2015. "Two applications of the random coefficient procedure: Correcting for misspecifications in a small area level model and resolving Simpson's paradox," Economic Modelling, Elsevier, vol. 45(C), pages 93-98.
    • Handle: RePEc:eee:ecmode:v:45:y:2015:i:c:p:93-98
    DOI: 10.1016/j.econmod.2014.10.053
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    References listed on IDEAS

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    7. Judea Pearl, 2014. "Comment: Understanding Simpson's Paradox," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 8-13, February.
    8. Swamy P. A. V. B. & Tavlas George S & Hall Stephen G. F. & Hondroyiannis George, 2010. "Estimation of Parameters in the Presence of Model Misspecification and Measurement Error," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-35, May.
    9. Timothy W. Armistead, 2014. "Resurrecting the Third Variable: A Critique of Pearl's Causal Analysis of Simpson's Paradox," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 1-7, February.
    10. Jun Ma & Mark Wohar (ed.), 2014. "Recent Advances in Estimating Nonlinear Models," Springer Books, Springer, edition 127, number 978-1-4614-8060-0, September.
    11. P. Swamy & Stephen Hall, 2012. "Measurement of causal effects," Economic Change and Restructuring, Springer, vol. 45(1), pages 3-23, February.
    12. Keli Liu & Xiao-Li Meng, 2014. "Comment: A Fruitful Resolution to Simpson's Paradox via Multiresolution Inference," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 17-29, February.
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    1. P.A.V.B. Swamy & Jatinder S. Mehta & I-Lok Chang, 2017. "Endogeneity, Time-Varying Coefficients, and Incorrect vs. Correct Ways of Specifying the Error Terms of Econometric Models," Econometrics, MDPI, vol. 5(1), pages 1-17, February.
    2. Abonazel, Mohamed R., 2016. "Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties," MPRA Paper 72586, University Library of Munich, Germany.
    3. P.A.V.B. Swamy & Stephen G. Hall & George S. Tavlas & I-Lok Chang & Heather D. Gibson & William H. Greene & Jatinder S. Mehta, 2016. "A Method for Measuring Treatment Effects on the Treated without Randomization," Econometrics, MDPI, vol. 4(2), pages 1-23, March.
    4. Swamy Paravastu & Peter Muehlen & Jatinder Singh Mehta & I-Lok Chang, 2022. "The State Of Econometrics After John W. Pratt, Robert Schlaifer, Brian Skyrms, And Robert L. Basmann," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 627-654, November.

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