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

• Authors: Andrzej Młodak

Abstracts

EN

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

EN

complex measure; ratio imputation; regression imputation; predictive mean matching; propensity score method

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2021
• Volume: 22
• Issue: 1
• Pages: 1-28

Contributors

author

Andrzej Młodak

• Statistical Office in Poznań, Centre for Small Area Estimation
• Poland and Calisia University – Kalisz, Poland

• https://orcid.org/0000000268539163

Identifiers

DOI

10.21307/stattrans-2021-001

Biblioteka Nauki

1047378