Autoregressive Distributed Lag (ARDL) Method for Estimating Poverty Levels in Polewali Mandar Regency
DOI:
https://doi.org/10.24252/msa.v13i2.60197Keywords:
Autoregressive Distributed Lag(ARDL), Forecasting Accuracy, Poor Population, PovertyAbstract
Polewali Mandar Regency is the region with the highest poverty rate in West Sulawesi. According to a publication by the Central Bureau of Statistics in March 2022, the percentage of the poor population was 11.75%, an increase compared to March 2021. The forecasting method used in this study is the Autoregressive Distributed Lag (ARDL) method. This study aims to determine the Autoregressive Distributed Lag (ARDL) model, which is then used to forecast the number of poor people in Polewali Mandar Regency. The results of the study using the ARDL method yielded the best estimation model, namely ARDL (3, 3, 2, 2). The forecast results for the percentage of the poor population using the ARDL (3, 3, 2, 2) model for the following semesters are 21.79%, 10.15%, and 16.52%, respectively. The forecasting accuracy test using the Mean Absolute Percentage Error (MAPE) yielded a value of 12.18%, indicating that the ARDL model produced in this study is suitable for forecasting the percentage of the poor population in Polewali Mandar Regency.
References
K. Nurfadilah, F. R. C, and I. Kasse, “Peramalan Tingkat Suku Bunga Pasar Uang Antar Bank (Puab) Dengan Vector
Autoregressive Exogenous (VARX),” msa, vol. 6, no. 1, p. 51, Jun. 2018.
W. F. Permata, M. Rahmi, and F. I. Yusuf, “Perbandingan model arimax dan ardl untuk peramalan data (aplikasi pada
banyaknya uang beredar di indonesia),” Transformasi: Jurnal Pendidikan Matematika dan Matematika, vol. 1, no. 2, 2017.
A. Rahmasari, E. H. Sunani, M. Jannah, F. Fathulaili, L. Kurnia, and A. Satria, “ARDL Method: Forecasting Data
Kemiskinan di NTB,” JTAM, vol. 3, no. 1, p. 52, Apr. 2019.
T. Kilic, H.-A. H. Dang, C. Carletto, K. Abanokova, and K. Abanokova, Poverty Imputation in Contexts without Consumption Data: A Revisit with Further Refinements. World Bank, Washington, DC, Nov. 2021.
H. Dang, D. Jolliffe, and C. Carletto, “Data gaps, data incomparability, and data imputation: A review of poverty
measurement methods for data-scarce environments,” Journal of Economic Surveys, vol. 33, no. 3, pp. 757–797, Jul. 2019.
L. Sugiharti, R. Purwono, M. A. Esquivias, and A. D. Jayanti, “Poverty Dynamics in Indonesia: The
Prevalence and Causes of Chronic Poverty,” JPSS, vol. 30, pp. 423–447, Feb. 2022.
F. Nkurunziza, R. Kabanda, and P. McSharry, “Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda,” Cogent Economics & Finance, vol. 13, no. 1, p. 2444374, Dec. 2025.
H. H. Dang, T. Kilic, K. Abanokova, and C. Carletto, “Poverty Imputation in Contexts Without Consumption Data: A
Revisit With Further Refinements,” Review of Income and Wealth, vol. 71, no. 1, p. e12714, Feb. 2025.
M. Gualavisi and D. Newhouse, “Integrating Survey and Geospatial Data for Geographical Targeting of the Poor and
Vulnerable: Evidence from Malawi,” The World Bank Economic Review, vol. 39, no. 2, pp. 377–409, May 2025.
I. Maipita et al., Mengukur kemiskinan & distribusi pendapatan. Upp Stim Ykpn, 2014.
N. M. W. Satyawati, I. M. Candiasa, and N. M. S. Mertasari, “Prediksi Penduduk Miskin di Indonesia Menggunakan
Analisis Dekomposisi,” del.jur.il.pen.mat., vol. 9, no. 1, p. 77, Jan. 2021.
D. N. Gujarati and D. C. Porter, Dasar-Dasar Ekonometrika. Salemba Empat, 2012.
D. N. Gujarati and D. C. Porter, Basic econometrics, 5th ed. Boston: McGraw-Hill Irwin, 2009.
B. Audina, M. Fatekurohman, and A. Riski, “Peramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine,” IJAS, vol. 4, no. 1, p. 34, May 2021.
S. Kusumadewi and H. Purnomo, Aplikasi Logika Fuzzy Untuk Pendukung Keputusan Edisi 2. Graha Ilmu, 2010.
E. W. D. Dhewanty, Evy Sulistianingsih, E. Sulistianingsih, and S. Martha, “Analisis Kointegrasi dan Error Correction
Model Indeks Harga Konsumen Kota Pontianak dan Singkawang,” Bimaster, vol. 8, no. 1, Jan. 2019.
M. H. Pesaran, Y. Shin, and R. J. Smith, “Bounds testing approaches to the analysis of level relationships,” Journal of
Applied Econometrics, vol. 16, no. 3, pp. 289–326, 2001.
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