Research Article
Comparison of MCEM and Bayesian Correction Methods of Spatially Lagged Covariates Measured with Error : Evidence from Monte Carlo Simulation
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290526, author={Mohammad Masjkur and Henk Folmer and Asep Saefuddin}, title={Comparison of MCEM and Bayesian Correction Methods of Spatially Lagged Covariates Measured with Error : Evidence from Monte Carlo Simulation}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={bayesian approach mcem measurement error spatial regression}, doi={10.4108/eai.2-8-2019.2290526} }- Mohammad Masjkur
Henk Folmer
Asep Saefuddin
Year: 2020
Comparison of MCEM and Bayesian Correction Methods of Spatially Lagged Covariates Measured with Error : Evidence from Monte Carlo Simulation
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290526
Abstract
Measurement errors in (spatially lagged) explanatory variables under the classical-errors-in variables assumption are not routinely accounted for in applied (spatial) research, in spite of their serious consequences. Particularly, the estimator of coefficients of variables measured with error but also of those not measured with error are biased and inconsistent. The purpose of this paper is to analyze and compare by way of Monte Carlo simulation two bias correction methods, i.e. Monte Carlo Expectation-Maximization (MCEM) and Bayesian approach (BA). We consider spatial lag model (SLX) with different spatial correlation of covariate of interest, different measurement error variances and sample sizes. We use relative bias (RelBias) and Root Mean Squared Error (RMSE) as valuation criteria. The main result is that the Bayesian approach and MCEM method outperform the Naive model without measurement error correction. Moreover, the Bayesian approach performs better than MCEM method.
