Forecasting educated unemployed people in Indonesia using the Bootstrap Technique
Forecasting is an essential analytical tool used to make future predictions based on preliminary data. However, the use of small sample sizes during analysis provides inaccurate results, known as asymptotic forecasting. Therefore, this study aims to analyze the unemployment rate of educated people i...
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| Format: | Article |
| Language: | English |
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Shahid Bahonar University of Kerman
2023-01-01
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| Series: | Journal of Mahani Mathematical Research |
| Subjects: | |
| Online Access: | https://jmmrc.uk.ac.ir/article_3342_4d3e055d4205988f60f801c7468e8b40.pdf |
| _version_ | 1797799211405672448 |
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| author | Umi Mahmudah Sugiyarto Surono Puguh Wahyu Prasetyo Annisa E. Haryati |
| author_facet | Umi Mahmudah Sugiyarto Surono Puguh Wahyu Prasetyo Annisa E. Haryati |
| author_sort | Umi Mahmudah |
| collection | DOAJ |
| description | Forecasting is an essential analytical tool used to make future predictions based on preliminary data. However, the use of small sample sizes during analysis provides inaccurate results, known as asymptotic forecasting. Therefore, this study aims to analyze the unemployment rate of educated people in Indonesia using the bias-corrected forecasting bootstrap technique. Data were collected from a total of 30 time series of educated unemployed from 2015 to 2019 using the bias-corrected bootstrap technique and determined using the interval prediction method. The bootstrap replication used is at intervals of 100, 250, 500, and 1000. The results obtained using the R program showed that the bootstrap technique provides consistent forecasting results, better accuracy, and unbiased estimation. Moreover, the results also show that for the next 10 periods, the number of educated unemployed people in Indonesia is projected to decline. The bootstrap coefficient also tends to decrease with an increase in the number of replications, at an average of 0.958. The interval prediction is also known to be smooth, along with a large number of bootstrap replications. |
| first_indexed | 2024-03-13T04:16:32Z |
| format | Article |
| id | doaj.art-4b422768f495457a8039e08790f1f92e |
| institution | Directory Open Access Journal |
| issn | 2251-7952 2645-4505 |
| language | English |
| last_indexed | 2024-03-13T04:16:32Z |
| publishDate | 2023-01-01 |
| publisher | Shahid Bahonar University of Kerman |
| record_format | Article |
| series | Journal of Mahani Mathematical Research |
| spelling | doaj.art-4b422768f495457a8039e08790f1f92e2023-06-21T03:21:03ZengShahid Bahonar University of KermanJournal of Mahani Mathematical Research2251-79522645-45052023-01-0112117118210.22103/jmmr.2022.19368.12393342Forecasting educated unemployed people in Indonesia using the Bootstrap TechniqueUmi Mahmudah0Sugiyarto Surono1Puguh Wahyu Prasetyo2Annisa E. Haryati3Department of Mathematics Education, IAIN Pekalongan, Pekalongan, Central Java, IndonesiaDepartment of Mathematics, Universitas Ahmad Dahlan,Yogyakarta, IndonesiaDepartment of Mathematics Education, Universitas Ahmad Dahlan, Yogyakarta, IndonesiaDepartment of Mathematics, Universitas Ahmad Dahlan,Yogyakarta, IndonesiaForecasting is an essential analytical tool used to make future predictions based on preliminary data. However, the use of small sample sizes during analysis provides inaccurate results, known as asymptotic forecasting. Therefore, this study aims to analyze the unemployment rate of educated people in Indonesia using the bias-corrected forecasting bootstrap technique. Data were collected from a total of 30 time series of educated unemployed from 2015 to 2019 using the bias-corrected bootstrap technique and determined using the interval prediction method. The bootstrap replication used is at intervals of 100, 250, 500, and 1000. The results obtained using the R program showed that the bootstrap technique provides consistent forecasting results, better accuracy, and unbiased estimation. Moreover, the results also show that for the next 10 periods, the number of educated unemployed people in Indonesia is projected to decline. The bootstrap coefficient also tends to decrease with an increase in the number of replications, at an average of 0.958. The interval prediction is also known to be smooth, along with a large number of bootstrap replications.https://jmmrc.uk.ac.ir/article_3342_4d3e055d4205988f60f801c7468e8b40.pdfr modelbias-correctedbootstrapforecasting |
| spellingShingle | Umi Mahmudah Sugiyarto Surono Puguh Wahyu Prasetyo Annisa E. Haryati Forecasting educated unemployed people in Indonesia using the Bootstrap Technique Journal of Mahani Mathematical Research r model bias-corrected bootstrap forecasting |
| title | Forecasting educated unemployed people in Indonesia using the Bootstrap Technique |
| title_full | Forecasting educated unemployed people in Indonesia using the Bootstrap Technique |
| title_fullStr | Forecasting educated unemployed people in Indonesia using the Bootstrap Technique |
| title_full_unstemmed | Forecasting educated unemployed people in Indonesia using the Bootstrap Technique |
| title_short | Forecasting educated unemployed people in Indonesia using the Bootstrap Technique |
| title_sort | forecasting educated unemployed people in indonesia using the bootstrap technique |
| topic | r model bias-corrected bootstrap forecasting |
| url | https://jmmrc.uk.ac.ir/article_3342_4d3e055d4205988f60f801c7468e8b40.pdf |
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