Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE)

Budi Susetyo, Anwar Fitrianto
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Abstract

Missing data may occur in various types of research. Regression and multiple imputation by chained equations (MICE) are two methods that can be used to estimate missing data in panel data types. This study aims to compare the accuracy of the missing panel data estimation using the regression and the MICE methods. The data used in this study are 161 random samples of senior high schools and vocational schools in DKI province for the year 2016-2020. Based on the results of the Chow test, Hausman test, and Lagrange Multiplier test on panel data regression, it shows that the appropriate model for the student-teacher ratio (X5) is random, the percentage of teachers who have an educator certificate (X6) is a fixed model with the specific effect of individual school and time, while the percentage of teachers who hold a bachelor degree (X7) is a fixed model with the specific effect of individual. Based on this model, the estimation of missing data is then carried out. The accuracy of the missing data estimation was carried out by comparing the MAPE, MAE, and RMSE values. The results show that the MICE method is quite good for estimating missing data at X5, quite feasible for estimating X6, and very good for estimating missing data at X7. In general, MICE is more accurate than panel data regression
利用回归和多变量链式等式估算缺失面板数据 (MICE)
缺失数据可能出现在各种类型的研究中。回归法和连锁方程多重估算法(MICE)是可以用来估算面板数据类型中缺失数据的两种方法。本研究旨在比较使用回归法和 MICE 法估计缺失面板数据的准确性。本研究使用的数据是 2016-2020 年德基省高中和职业学校的 161 个随机样本。根据面板数据回归的Chow检验、Hausman检验和拉格朗日乘数检验结果显示,师生比(X5)的合适模型为随机模型,拥有教育家证书的教师比例(X6)为固定模型,具有个体学校和时间的特定影响,而拥有学士学位的教师比例(X7)为固定模型,具有个体的特定影响。在此模型的基础上,进行缺失数据估计。通过比较 MAPE、MAE 和 RMSE 值,对缺失数据进行准确估计。结果表明,MICE 方法对 X5 的缺失数据估计相当好,对 X6 的缺失数据估计相当可行,对 X7 的缺失数据估计非常好。总体而言,MICE 比面板数据回归更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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