An application of a complex measure to model–based imputation in business statistics

Młodak Andrzej ()
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Młodak Andrzej: Statistical Office in Poznań, Centre for Small Area Estimation, address: Statistical Office in Poznań, Branch in Kalisz, ul. Piwonicka 7–9, 62–800 Kalisz, Poland . and Calisia University, – Kalisz, Poland

Statistics in Transition New Series, 2021, vol. 22, issue 1, 1-28

Abstract: When faced with missing data in a statistical survey or administrative sources, imputation is frequently used in order to fill the gaps and reduce the major part of bias that can affect aggregated estimates as a consequence of these gaps. This paper presents research on the efficiency of model–based imputation in business statistics, where the explanatory variable is a complex measure constructed by taxonomic methods. The proposed approach involves selecting explanatory variables that fit best in terms of variation and correlation from a set of possible explanatory variables for imputed information, and then replacing them with a single complex measure (meta–feature) exploiting their whole informational potential. This meta–feature is constructed as a function of a median distance of given objects from the benchmark of development. A simulation study and empirical study were used to verify the efficiency of the proposed approach. The paper also presents five types of similar techniques: ratio imputation, regression imputation, regression imputation with iteration, predictive mean matching and the propensity score method. The second study presented in the paper involved a simulation of missing data using IT business data from the California State University in Los Angeles, USA. The results show that models with a strong dependence on functional form assumptions can be improved by using a complex measure to summarize the predictor variables rather than the variables themselves (raw or normalized).

Keywords: complex measure; ratio imputation; regression imputation; predictive mean matching; propensity score method. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:22:y:2021:i:1:p:1-28:n:10

DOI: 10.21307/stattrans-2021-001

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