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Efektivitas ECM - MIDAS berbasis Principal Component Analysis (PCA) dalam Memprediksi PDB di Indonesia

The Effectiveness of ECM - MIDAS Based on Principal Component Analysis (PCA) in Predicting GDP in Indonesia

Fajar Fithra Ramadhan  -  Politeknik Statistika STIS, Indonesia
Dea Malaika  -  Politeknik Statistika STIS, Indonesia
Ni Kadek Dwi Utami  -  Politeknik Statistika STIS, Indonesia
*Fitri Kartiasih  -  Politeknik Statistika STIS, Indonesia
Open Access Copyright 2025 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

GDP is closely related to monetary policy, because changes in GDP often affect decisions taken by the central bank in formulating policies to maintain economic stability. This study aims to predict the value of Gross Domestic Product (GDP) by developing a more accurate and efficient model. The variables analyzed include primary money, net domestic assets, net foreign position, and foreign exchange reserves as independent variables, and gross domestic product (GDP) as the dependent variable. The method used combines the Error Correction Model (ECM) into the Mixed Data Sampling (MIDAS) and Principal Component Analysis (PCA) models, this approach provides a more comprehensive analytical framework to capture complex interactions between variables with different frequencies, while taking into account long-term and short-term dynamics that influence each other. The results of the study indicate that the combination approach of PCA and MIDAS with the Almon distribution is more effective in capturing data patterns than other approaches that only use PCA with the average or median of economic indicators. The ECM-MIDAS-PCA model with the Almon weight function showed the best results, marked by an Adjusted R-Square value of 22.33% and low prediction error. The Error Correction Term (ECT) coefficient of -0.1579 indicates a correction towards long-term equilibrium of 15.79% per quarter, so that the process towards equilibrium can be achieved in 6.33 quarters.

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  1. Language: Market Responses to Central Bank Speeches. SSRN Electronic Journal, May 2023, 105921. https://doi.org/10.2139/ssrn.4471242
  2. Akhyar, R. R., Khoiriawati, N., Hidayah, L., Malikah, B. I., & Nur Rohmah, I. L. (2024). Pengaruh Suku Bunga, Jumlah Uang Beredar, Kurs, Dan Pengeluaran Pemerintah Terhadap Inflasi Di Indonesia. WORLDVIEW ( Jurnal Ekonomi Bisnis dan Sosial Sains ), 3(1), 01–10. https://doi.org/10.38156/worldview.v3i1.414
  3. Alhayria, & Azaluddin. (2023). Pengaruh inflasi dan suku bunga terhadap investasi dan pertumbuhan ekonomi. Jurnal Ilmu Ekonomi Mulawarman (JIEM), 13(1), 259–267. http://journal.feb.unmul.ac.id/index.php/JIEM/article/view/1381
  4. Anggraeni, D., & Dwiputri, I. N. (2022). Variabel-variabel yang Mempengaruhi Inflasi di Indonesia. Jurnal Ekonomi Pembangunan, 11(2), 119–128. https://doi.org/10.23960/jep.v11i2.490
  5. Apriliani, D. (2022). Analisis Pengaruh Variabel Makroekonomi Terhadap Inflasi Di Indonesia. Ekopem: Jurnal Ekonomi Pembangunan, 4(4), 106–119. https://doi.org/10.32938/jep.v4i4.3113
  6. Bacchiocchi, E., Bastianin, A., Missale, A., & Rossi, E. (2020). Structural analysis with mixed-frequency data: A model of US capital flows. Economic Modelling, 89, 427–443. https://doi.org/10.1016/j.econmod.2019.11.010
  7. Bank Indonesia. (2024). Kebijakan moneter. Jakarta: Bank Indonesia
  8. Bharadiya, J. P. (2023). A Tutorial on Principal Component Analysis for Dimensionality Reduction in Machine Learning. International Journal of Innovative Research in Science Engineering and Technology, 8(5), 2028–2032. https://doi.org/10.5281/zenodo.8002436
  9. Budi Laksono, Vara Afrindasari, Zulfanah Diana, & Muhammad Kurniawan. (2024). Analisis Pengaruh Jumlah Uang Beredar (JUB) Dan Tingkat Suku Bunga (RATE) Terhadap Inflasi Di Indonesia Tahun 2014-2023. Jurnal Bisnis, Ekonomi Syariah, dan Pajak, 1(2), 54–68. https://doi.org/10.61132/jbep.v1i2.153
  10. Damanik, E. O. P., Napitu, R., & Dina Valentina Pratiwi. (2023). Pengaruh Inflasi Dan Suku Bunga Terhadap Pertumbuhan Ekonomi Di Provinsi Sumatera Utara Tahun 2013 – 2021. Jurnal Ilmiah Accusi, 5(`1), 14–24. https://doi.org/10.36985/a86hy427
  11. Dana, D. A. N., Terhadap, P., Nugroho, S. P., Terhadap, P., & Supply, M. (2018). ANALISIS PENGARUH NET DOMESTIC ASSET ,. 5(2), 76–83
  12. Dewi, E. R., & Ibnu Hadi. (2019). Peramalan Produk Domestik Bruto (PDB) Industri Pengolahan Non Migas di Indonesia dengan menggunakan Metode Fuzzy Time Series. Jurnal Statistika dan Aplikasinya, 3(2), 16–24. https://doi.org/10.21009/jsa.03203
  13. Ekonomi, F., & Malikussaleh, U. (2024). Pengaruh Utang Luar Negeri , Inflasi dan Net Ekspor terhadap Produk Domestik Bruto di Indonesia. 25(1), 84–96
  14. Engle, R. F., & Granger, W. J. (1987). EngleGranger1987.pdf. In Econometrica (Vol. 55, Nomor 2, hal. 251–276)
  15. Eniayewu, P. E., Samuel, G. T., Joshua, J. D., Samuel, B. T., Dogo, B. S., Yusuf, U., Ihekuna, R. O., & Mevweroso, C. R. (2024). Forecasting exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach. Scientific African, 23(November 2023), e02101. https://doi.org/10.1016/j.sciaf.2024.e02101
  16. EViews. (n.d.). MIDAS and the MIDAS procedure in EViews 9. EViews. Retrieved January 18, 2025, from https://www.eviews.com/EViews9/ev95midas.html
  17. Fatih, M. (2022). Forecasting German Gross Domestic Product Using Dax Index Values : MIDAS Analysis Forecasting German Gross Domestic Product Using Dax Index Values : MIDAS Analysis. October
  18. Fatmasari, D., Harjadi, D., & Hamzah, A. (2022). Error Correction Model Approach As a Determinant of Stock Prices. Trikonomika, 21(2), 84–91. https://doi.org/10.23969/trikonomika.v21i2.6968
  19. Ghysels, E. (2004). The MIDAS Touch : Mixed Data Sampling Regression Models ∗ Pedro Santa-Clara Rossen Valkanov. Ucla
  20. Ghysels, E., & Marcellino, M. (2016). The econometric analysis of mixed frequency data sampling. Journal of Econometrics, 193(2), 291–293. https://doi.org/10.1016/j.jeconom.2016.04.007
  21. Hafidz Meiditambua Saefulloh, M., Rizah Fahlevi, M., & Alfa Centauri, S. (2023). Pengaruh Inflasi Terhadap Pertumbuhan Ekonomi: Perspektif Indonesia. Jurnal Keuangan Negara dan Kebijakan Publik, 3(1), 17–26
  22. Harefa, A. O., Zega, Y., & Mendrofa, R. N. (2023). The Application of the Least Squares Method to Multicollinear Data. International Journal of Mathematics and Statistics Studies, 11(1), 30–39. https://doi.org/10.37745/ijmss.13/vol11n13039
  23. Hauzenberger, N., Marcellino, M., Pfarrhofer, M., & Stelzer, A. (2024). Nowcasting with mixed frequency data using Gaussian processes. http://arxiv.org/abs/2402.10574
  24. Hecq, A. W., Götz, T. B., & Urbain, J. R. Y. J. (2012).. Forecasting Mixed Frequency Time Series with ECM- MIDAS Models. 2012. https://doi.org/10.26481/umamet.2012012
  25. Jansen, A., Wang, R., Behrens, P., & Hoekstra, R. (2024). Beyond GDP: a review and conceptual framework for measuring sustainable and inclusive wellbeing. The Lancet Planetary Health, 8(9), e695–e705. https://doi.org/10.1016/S2542-5196(24)00147-5
  26. Jennifer Patricia. (2021). PERAMALAN LAJU PRODUK DOMESTIK BRUTO INDONESIA DENGAN DATA GOOGLE TRENDS MENGGUNAKAN METODE NEURAL NETWORK DAN EXTREME GRADIENT BOOSTING
  27. Jollife, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065). https://doi.org/10.1098/rsta.2015.0202
  28. Kartiasih, F., & Setiawan, A. (2020). Aplikasi Error Correction Mechanism Dalam Analisis Dampak Pertumbuhan Ekonomi, Konsumsi Energi Dan Perdagangan Internasional Terhadap Emisi Co2 Di Indonesia. Media Statistika, 13(1), 104–115. https://doi.org/10.14710/medstat.13.1.104-115
  29. Kim, J. M., Cho, C., & Jun, C. (2022). Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model. Journal of Risk and Financial Management, 15(2). https://doi.org/10.3390/jrfm15020074
  30. Kurniawan, M. A., & Falentina, A. T. (2022). Analisis Big Data dan Official Statistics dalam Melakukan Nowcasting Pertumbuhan Ekonomi Indonesia Sebelum dan Selama Pandemi COVID-19. Seminar Nasional Official Statistics, 2022(1), 521–532. https://doi.org/10.34123/semnasoffstat.v2022i1.1146
  31. Kurniawan, T.A. (2024). Nowcasting Produk Domestik Bruto Atas Dasar Harga Konstan Triwulanan Indonesia. 691–700
  32. Liu, T., Choo, W., Tunde, M. B., Wan, C., & Liang, Y. (2024). Enhancing stock volatility prediction with the AO-GARCH-MIDAS model. PLoS ONE, 19(6 June), 1–20. https://doi.org/10.1371/journal.pone.0305420
  33. Marsus, B., Indriani, N. K., Darmawan, V., & Fisu, A. A. (2020). Pengaruh Panjang Infrastruktur Jalan Terhadap PDRB dan Pertumbuhan Ekonomi Kota Palopo. Jurnal Pembangunan Ekonomi Dan Keuangan Daerah, 1(2016), 1–5
  34. Mishra, P., Alakkari, K., Abotaleb, M., Singh, P. K., Singh, S., Ray, M., Das, S. S., Rahman, U. H., Othman, A. J., Ibragimova, N. A., Ahmed, G. F., Homa, F., Tiwari, P., & Balloo, R. (2021). Now casting India economic growth using a mixed-data sampling (MIDAS) model (empirical study with economic policy uncertainty–consumer prices index). Data, 6(11). https://doi.org/10.3390/data6110113
  35. Ningrum, D., Hutagaol, R. M. A., & Muhtar, A. (2024). Penerapan Time Series Forecasting untuk Memprediksi Pertumbuhan Ekonomi Indonesia 2024. Data Sciences Indonesia (DSI), 3(2), 79–89. https://doi.org/10.47709/dsi.v3i2.3263
  36. Polyzos, E., & Siriopoulos, C. (2024). Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research. Computational Economics, 64(1), 225–262. https://doi.org/10.1007/s10614-023-10429-9
  37. Rachmawaty, R., Oktrima, B., & Waluyo Jati. (2024). Impact Analysis Of Monetary And Fiscal Policies On Indonesia’s Economic Growth. Jurnal Manajemen, 28(1), 88–106. https://doi.org/10.24912/jm.v28i1.1518
  38. Ramadani, G., Petrovska, M., & Bucevska, V. (2021). Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia. South East European Journal of Economics and Business, 16(2), 43–52. https://doi.org/10.2478/jeb-2021-0013
  39. Rasyidin, M., Saleh, M., Muttaqim, H., Nova, N., & Khairani, C. (2022). Pengaruh Kebijakan Moneter Terhadap Inflasi di Indonesia. Journal of Business and Economics Research (JBE), 3(2), 225–231. https://doi.org/10.47065/jbe.v3i2.1761
  40. Safitri, I. (2020). Analisis Pengaruh Inflasi, Suku Bunga, Nilai Tukar dan Cadangan Devisa Terhadap Produk Domestik Bruto di Indonesia Tahun 1995-2019. Jurnal Ekonomi dan Bisnis UMS Surakarta, 5(7), 1–17
  41. Saufi, M. S. (2021). Jurnal Ilmu Ekonomi dan Pembangunan. Journal of Chemical Information and Modeling, 4(2), 2021. https://doi.org/10.1080/09638288.2019.1595750%0Ahttps://doi.org/10.1080/17518423.2017.1368728%0Ahttp://dx.doi.org/10.1080/17518423.2017.1368728%0Ahttps://doi.org/10.1016/j.ridd.2020.103766%0Ahttps://doi.org/10.1080/02640414.2019.1689076%0Ahttps://doi.org/
  42. Sohibien, G. P. D. (2015). Analisis Hubungan Produk Domestik Bruto Dan Ekspor Indonesia Dengan Threshold Vector Error Correction Model. Aplikasi Statistika & Komputasi Statistik, 8, 14
  43. Todaro, M. P., & Smith, S. C. (2020). Economic Development. Thirteenth Edition. In Pearson (Nomor 13th Edition). https://www.mkm.ee/en/objectives-activities/economic-development
  44. Ulfa, Z. R., & Fisabilillah, L. W. P. (2023). Analisis Pengaruh Jumlah Uang Beredar Terhadap Produk Domestik Bruto (PDB) Indonesia. INDEPENDENT. Journal Of Economics, 3(3), 123–130. https://ejournal.unesa.ac.id/index.php/independent
  45. Virbickaitė, A., Nguyen, H., & Tran, M. N. (2023). Bayesian predictive distributions of oil returns using mixed data sampling volatility models. Resources Policy, 86(August). https://doi.org/10.1016/j.resourpol.2023.104167
  46. Warjiyo, P., & Solikin. (2003). Kebijakan Moneter Indonesia. In Jurnal Manajemen Maranatha (Vol. 3, Nomor 1)
  47. Winarto, H., Poernomo, A., & Prabawa, A. (2021). Analisis Dampak Kebijakan Moneter terhadap Pertumbuhan Ekonomi di Indonesia. J-MAS (Jurnal Manajemen dan Sains), 6(1), 34. https://doi.org/10.33087/jmas.v6i1.216
  48. Xu, X., & Liao, M. (2022). Prediction of Carbon Emissions in China’s Power Industry Based on the Mixed-Data Sampling (MIDAS) Regression Model. Atmosphere, 13(3). https://doi.org/10.3390/atmos13030423
  49. Xu, Z. Q., Xue, T., Chen, X. Y., Feng, J., Zhang, G. W., Wang, C., Lu, C. H., Chen, H. S., & Ding, Y. H. (2025). Wind power correction model designed by the quantitative assessment for the impacts of forecasted wind speed error. Advances in Climate Change Research, xxxx. https://doi.org/10.1016/j.accre.2024.12.006
  50. Zulaikah. (2024). Peran Kebijakan Fiskal dan Moneter dalam Menjaga Stabilitas Ekonomi Makro , per. 6(1), 95–108

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