Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE)
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...
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Mathematics Department UIN Maulana Malik Ibrahim Malang
2024-05-01
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| Series: | Cauchy: Jurnal Matematika Murni dan Aplikasi |
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| Online Access: | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/24824 |
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| author | Budi Susetyo Anwar Fitrianto |
| author_facet | Budi Susetyo Anwar Fitrianto |
| author_sort | Budi Susetyo |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-d84d5c0e4bf94a34a5607dba32c6f393 |
| institution | OA Journals |
| issn | 2086-0382 2477-3344 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Mathematics Department UIN Maulana Malik Ibrahim Malang |
| record_format | Article |
| series | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| spelling | doaj-art-d84d5c0e4bf94a34a5607dba32c6f3932025-08-20T02:13:45ZengMathematics Department UIN Maulana Malik Ibrahim MalangCauchy: Jurnal Matematika Murni dan Aplikasi2086-03822477-33442024-05-01919410510.18860/ca.v9i1.248247812Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE)Budi Susetyo0Anwar Fitrianto1IPB University Bogor IndonesiaDepartment of Statistics IPB University, Indonesia, Institute of Engineering Mathematics, Universiti Malaysia PerlisMissing 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 regressionhttps://ejournal.uin-malang.ac.id/index.php/Math/article/view/24824missing data, panel data, imputation, regression, multiple imputation by chained equations |
| spellingShingle | Budi Susetyo Anwar Fitrianto Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) Cauchy: Jurnal Matematika Murni dan Aplikasi missing data, panel data, imputation, regression, multiple imputation by chained equations |
| title | Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) |
| title_full | Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) |
| title_fullStr | Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) |
| title_full_unstemmed | Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) |
| title_short | Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) |
| title_sort | estimating missing panel data with regression and multivariate imputation by chained equations mice |
| topic | missing data, panel data, imputation, regression, multiple imputation by chained equations |
| url | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/24824 |
| work_keys_str_mv | AT budisusetyo estimatingmissingpaneldatawithregressionandmultivariateimputationbychainedequationsmice AT anwarfitrianto estimatingmissingpaneldatawithregressionandmultivariateimputationbychainedequationsmice |