The use of survey weights in regression analysis
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.
|Date of creation:||Jan 2010|
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- Giuseppe Bruno & Leandro D'Aurizio & Raffaele Tartaglia Polcini, 2008. "Remote processing of firm microdata at the Bank of Italy," Questioni di Economia e Finanza (Occasional Papers) 36, Bank of Italy, Economic Research and International Relations Area.
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- Ivan Faiella & Romina Gambacorta, 2007. "The Weighting Process in the SHIW," Temi di discussione (Economic working papers) 636, Bank of Italy, Economic Research and International Relations Area.
- Jan F. Kiviet, 1986. "On the Rigour of Some Misspecification Tests for Modelling Dynamic Relationships," Review of Economic Studies, Oxford University Press, vol. 53(2), pages 241-261.
- repec:mpr:mprres:4937 is not listed on IDEAS
- Giuseppe De Luca & Franco Peracchi, 2007. "A sample selection model for unit and item nonresponse in cross-sectional surveys," CEIS Research Paper 95, Tor Vergata University, CEIS.
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