Kernel density estimation for heaped data

Marcus Groß and Ulrich Rendtel

No 2015/27, Discussion Papers from Free University Berlin, School of Business & Economics

Abstract: In self-reported data usually a phenomenon called 'heaping' occurs, i.e. survey participants round the values of their income, weight or height to some degree. Additionally, respondents may be more prone to round off or up due to social desirability. By ignoring the heaping process a severe bias in terms of spikes and bumps is introduced when applying kernel density methods naively to the rounded data. A generalized Stochastic Expectation Maximization (SEM) approach accounting for heaping with potentially asymmetric rounding behaviour in univariate kernel density estimation is presented in this work. The introduced methods are applied to survey data of the German Socio-Economic Panel and exhibit very good performance simulations.

Keywords: Heaping; Survey Data; Measurement error; Self-reported data; Kernel density estimation; Rounded data (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm
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