Author(s)

Peter Otieno Opeyo^1,2, Weihu Cheng¹, Zhao Xu¹

¹Department of Statistics, Beijing University of Technology, Beijing, China.
²Department of Statistics and Computational Mathematics, The Technical University of Kenya, Nairobi, Kenya.

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.

Panel Data, Imputation, Monte Carlo, Bias, Conditional Maximum Likelihood

Opeyo, P. , Cheng, W. and Xu, Z. (2023) Superiority of Bayesian Imputation to Mice in Logit Panel Data Models. Open Journal of Statistics, 13, 316-358. doi: 10.4236/ojs.2023.133017.