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The use of survey weights in regression analysis

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  • Ivan Faiella

    (Bank of Italy)

Abstract

While there is a wide consensus in using survey weights when estimating population parameters, it is not clear what to do when using survey data for analytic purposes (i.e. with the objective of making inference about model parameters). In the model-based framework (MB), under the hypothesis that the underlying model is correctly specified, using survey weights in regression analysis potentially involves a loss of efficiency. In a design-based perspective (DB), weighted estimates are both design consistent and can provide robustness to model mis-specification. In this paper, I suggest that the choice of using survey weights can be seen in a regression diagnostic set. The survey data analyst should check if the design information included in survey weights has some explanatory power in describing the model outcome. To accomplish this task a set of econometric tests is suggested, that could be supplemented by the analysis of model features under the two strategies.

Suggested Citation

  • Ivan Faiella, 2010. "The use of survey weights in regression analysis," Temi di discussione (Economic working papers) 739, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_739_10
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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2010/2010-0739/en_tema_739.pdf
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    References listed on IDEAS

    as
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    9. Magee, L. & Robb, A. L. & Burbidge, J. B., 1998. "On the use of sampling weights when estimating regression models with survey data," Journal of Econometrics, Elsevier, vol. 84(2), pages 251-271, June.
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    Cited by:

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    3. Miguel Ampudia & Michael Ehrmann, 2016. "Financial Inclusion—What’s it Worth?," Staff Working Papers 16-30, Bank of Canada.
    4. Leandro D�Aurizio & Giuseppina Papadia, 2016. "Using external sources to understand sample survey bias: the case of the Invind survey," Questioni di Economia e Finanza (Occasional Papers) 329, Bank of Italy, Economic Research and International Relations Area.
    5. Kacper Grejcz & Zbigniew Żółkiewski, 2017. "Household wealth in Poland: the results of a new survey of household finance," Bank i Kredyt, Narodowy Bank Polski, vol. 48(3), pages 295-326.
    6. Reid, Monique & Siklos, Pierre & Plessis, Stan Du, 2021. "What drives household inflation expectations in South Africa? Demographics and anchoring under inflation targeting," Economic Systems, Elsevier, vol. 45(3).
    7. Ehrmann, Michael & Ziegelmeyer, Michael, 2014. "Household Risk Management and Actual Mortgage Choice in the Euro Area," MEA discussion paper series 201406, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    8. Mathä, Thomas Y. & Porpiglia, Alessandro & Ziegelmeyer, Michael, 2017. "Household wealth in the euro area: The importance of intergenerational transfers, homeownership and house price dynamics," Journal of Housing Economics, Elsevier, vol. 35(C), pages 1-12.
    9. Giorgio Fazio & Luciano Lavecchia, 2013. "Social Capital Formation across Space," International Regional Science Review, , vol. 36(3), pages 296-321, July.
    10. Fessler, Pirmin & Rehm, Miriam & Tockner, Lukas, 2014. "The impact of housing non-cash income on the unconditional distribution of household income in Austria," Working Paper Series 1718, European Central Bank.
    11. Fazio, Giorgio & Lavecchia, Luciano, 2012. "Social capital formation across space: proximity and trust in European regions," SIRE Discussion Papers 2012-30, Scottish Institute for Research in Economics (SIRE).
    12. D’Aurizio Leandro & Papadia Giuseppina, 2019. "Using Administrative Data to Evaluate Sampling Bias in a Business Panel Survey," Journal of Official Statistics, Sciendo, vol. 35(1), pages 67-92, March.
    13. L. Arrondel & M. Roger & F. Savignac, 2014. "Wealth and Income in the Euro Area: Heterogeneity in Households Behaviours?," Working papers 497, Banque de France.
    14. Enrique Marshall, 2015. "Reflexiones sobre la Práctica del Ahorro en Chile," Economic Policy Papers Central Bank of Chile 54, Central Bank of Chile.
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    More about this item

    Keywords

    survey methods; model evaluation and testing;

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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