Analisis Regresi Kuantil Dengan Pendekatan Bootstrap Pada World Happiness Report 2024
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DOI:
https://doi.org/10.32665/statkom.v4i2.5632Keywords:
Quantile Regression, Bootstrap, World Happiness ReportAbstract
Background: Happiness is a key indicator of national well-being. The World Happiness Report measures it through economic and social factors. Linear regression (OLS) is often applied but is sensitive to outliers. Quantile regression is more robust, and bootstrapping enhances estimate stability.
Objective: This study aims to analyze the influence of these factors on happiness scores using quantile regression, which is able to provide a comprehensive picture of the influence of variables at various distribution positions, especially when there are outliers and violations of classical linear regression assumptions.
Methods: Quantile regression was applied at quantiles 0.25 to 0.75, and the best model was obtained at quantile 0.4 with a pseudo-R² value of 0.6388. To improve the reliability of parameter estimates, a bootstrap approach with 1000 resampling times was used, which provided more stable confidence intervals and standard deviation estimates.
Results: The results show that social support, healthy life expectancy and freedom of choice are variables that significantly affect the level of happiness in the 0.4 quantile. Meanwhile, GDP per capita, generosity, and perception of corruption are not statistically significant in this model.
Conclusion: This study recommends the use of quantile regression with bootstrapping as a robust approach for socioeconomic data analysis, especially in the context of distributions that are not symmetric and contain outliers. The findings also provide policy implications.
References
Anuraga, G., & Arieska, P. K. (2016). Regresi Kuantil Pendekatan Bootstrap untuk Pemodelan Kemiskinan di Pulau Jawa. Jurnal Statistika Universitas Muhammadiyah Semarang, 4(2). https://jurnal.unimus.ac.id/index.php/statistik/article/view/2231
Budi, A. D. A. S., Septiana, L., & Mahendra, B.E. P. (2024). Memahami Asumsi Klasik dalam Analisis Statistik: Sebuah Kajian Mendalam tentang Multikolinearitas, Heterokedastisitas, dan Autokorelasi dalam Penelitian. Jurnal Multidisiplin West Science, 3(1), 1-11. https://doi.org/10.58812/jmws.v3i01.878
Fitriana, N. E., & Qibthiyyah, R. M. (2021). PENGARUH KEBIJAKAN DANA DESA TERHADAP JUMLAH PENDUDUK MISKIS PEDESAAN DI INDNESIA. Jurnal Ekonomi Dan Kebijakan Publik Indonesia, 8(1), 19–44. https://www.researchgate.net/publication/367233208_PENGARUH_KEBIJAKAN_DANA_DESA_TERHADAP_JUMLAH_PENDUDUK_MISKIS_PEDESAAN_DI_INDNESIA
Fransiska, H., Rini, D. S., & Agustina, D. (2021). PENERAPAN REGRESI KUANTIL PADA DATA KEMISKINAN BENGKULU. Seminar Nasional Official Statistics, 2020(1), 1203–1208. https://doi.org/10.34123/semnasoffstat.v2020i1.650
Furno, M., & Vistocco, D. (2018). Quantile Regression. Wiley. https://doi.org/10.1002/9781118863718
Gardiner, G., Lee, D., Baranski, E., & Funder, D. (2020). Happiness around the world: A combined etic-emic approach across 63 countries. PLOS ONE, 15(12), e0242718. https://doi.org/10.1371/journal.pone.0242718
Gunawan, A. (2024). Perbandingan Metode Ordinary Least Square (OLS) dan Metode Partial Least Square (PLS) Untuk Mengatasi Multikolinearitas. Socius: Jurnal Penelitian Ilmu-Ilmu Sosial, 1(6). https://ojs.daarulhuda.or.id/index.php/Socius/article/view/147
Hao, L., & Naiman, D. (2007). Quantile Regression. SAGE Publications, Inc. https://doi.org/10.4135/9781412985550
Helliwell, J. F., Layard, R., Sachs, J. D., Helliwell, J., & Sachs, J. (2012). World Happiness Report. https://www.researchgate.net/publication/233401584
Koenker, R., & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33. https://doi.org/10.2307/1913643
Kushary, D., Davison, A. C., & Hinkley, D. V. (2000). Bootstrap Methods and Their Application. Technometrics, 42(2), 216. https://doi.org/10.2307/1271471
Maharani, I. F., Satyahadewi, N., & Kusnandar, D. (2014). Metode Ordinary Least Squares Dan Least Trimmed Squares Dalam Mengestimasi Parameter Regresi Ketika Terdapat Outlier. BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya, 3(3). https://jurnal.untan.ac.id/index.php/jbmstr/article/view/7350
Matdoan, M. Y., & Balami, A. M. (2019). Estimasi Parameter Regresi Kuantil Dengan Fungsi Spline Truncated pada Kasus Demam Berdarah Dengue di Kota Surabaya. Jurnal MSA (Matematika Dan Statistika Serta Aplikasinya), 7(1), 44. https://doi.org/10.24252/msa.v7i1.7511
Nurfauziah, F. L. (2020). Permodalan, Efisiensi, Risiko Kredit, dan Likuiditas Bank Perkreditan Rakyat di Jawa Barat: Aplikasi Regresi Kuantil Bootstrap. Jurnal Akuntansi, 14(2), 102–131. https://ejournal.atmajaya.ac.id/index.php/JARA/article/view/1614
Pinuntun, W. (2023). Penggunaan Model Regresi Kuantil Bootstrap Dalam Mengatasi Data Outlier Pada Data Angka Kemiskinan Di Provinsi Sulawesi Selatan. Repository | Universitas Hasanuddin. https://doi.org/id/eprint/39756/1/H051191037_skripsi_27-02-2024%20bab1-2.pdf
Putra G, A., Tiro, M. A., & Aidid, M. K. (2019). Metode Boostrap dan Jackknife dalam Mengestimasi Parameter Regresi Linear Ganda (Kasus: Data Kemiskinan Kota Makassar Tahun 2017). VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 1(2), 32. https://doi.org/10.35580/variansiunm12895
Ramandhani, R., Sudarno, S., & Safitri, D. (2017). Metode Bootstrap Aggregating Regresi Logistik Biner Untuk Ketepatan Klasifikasi Kesejahteraan Rumah Tangga Di Kota Pati. Jurnal Gaussian, 6(1), 121-130. https://ejournal3.undip.ac.id/index.php/gaussian/article/view/14775
Rositawati, A. F. D., & Budiantara, I. N. (2019). Pemodelan Indeks Kebahagiaan Provinsi di Indonesia Menggunakan Regresi Nonparametrik Spline Truncated. JURNAL SAINS DAN SENI ITS, 8(2). https://www.researchgate.net/publication/360679475_Pemodelan_Indeks_Kebahagiaan_Provinsi_di_Indonesia_Menggunakan_Regresi_Nonparametrik_Spline_Truncated
Saputri, O. D., Yanuar, F., & Devianto, D. (2018). Simulation Study The Implementation of Quantile Bootstrap Method on Autocorrelated Error. CAUCHY: Jurnal Matematika Murni Dan Aplikasi, 5(3), 95–101. https://doi.org/10.18860/ca.v5i3.5349
Yanuar, F., Yozza, H., Firdawati, F., Rahmi, I., & Zetra, A. (2019). Applying bootstrap quantile regression for the construction of a low birth weight model. Makara Journal of Health Research, 23(2). https://doi.org/10.7454/msk.v23i2.9886
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