TITLE:
Superiority of Bayesian Imputation to Mice in Logit Panel Data Models
AUTHORS:
Peter Otieno Opeyo, Weihu Cheng, Zhao Xu
KEYWORDS:
Panel Data, Imputation, Monte Carlo, Bias, Conditional Maximum Likelihood
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.3,
June
16,
2023
ABSTRACT: Non-responses leading to missing data are common in most studies and
causes inefficient and biased statistical inferences if ignored. When faced
with missing data, many studies choose to employ complete case analysis
approach to estimate the parameters of the model. This however compromises on
the susceptibility of the estimates to reduced bias and minimum variance as
expected. Several classical and model based techniques of imputing the missing
values have been mentioned in literature. Bayesian approach to missingness is deemed superior amongst the other techniques
through its natural self-lending to missing data settings where the
missing values are treated as unobserved random variables that have a
distribution which depends on the observed data. This paper digs up the
superiority of Bayesian imputation to Multiple Imputation with Chained
Equations (MICE) when estimating logistic panel data models with single fixed
effects. The study validates the superiority of conditional maximum likelihood
estimates for nonlinear binary choice logit panel model in the presence of
missing observations. A Monte Carlo simulation was designed to determine the
magnitude of bias and root mean square errors (RMSE) arising from MICE and Full
Bayesian imputation. The simulation results show that the conditional maximum
likelihood (ML) logit estimator presented in this paper is less biased and more
efficient when Bayesian imputation is performed to curb non-responses.