Published on December 5, 2025 – Updated on December 5, 2025
Location
Pôle Tertiaire - Site La Rotonde - 26 avenue Léon Blum - 63000 Clermont-Ferrand
Room 212

Research seminar. Multiple imputation of missing values in longitudinal economic data: is simpler better?


Anne Viallefont
CERDI, Université Clermont Auvergne

Abstract

Multiple imputation (MI) is an efficient and simple enough way to handle missing data and get unbiased estimates, even when there are numerous missing values, and when the process that generates missingness is not 'completely at random'. However, when dealing with economic panel data, the values that are generated by most available MI routines are often unrealistic, which could largely increase the estimated variances. In this preliminary work, I generate missing values in complete panel data, under various schemes, and I use three different data-completion algorithms, including a very simple algorithm that I propose. I then compare the quality of the results in terms of bias correction and variance inflation.