Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Articles

APPLICATION OF THE PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL TO FORECASTING ECONOMIC GROWTH IN INDONESIA

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  • Aulia Rahman Al Madani,
  • Sandrina Najwa,
  • Budi Nurani Ruchjana
Aulia Rahman Al Madani
Magister of Applied Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia
Sandrina Najwa
Magister of Applied Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia
Budi Nurani Ruchjana
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia
DOI: https://doi.org/10.30598/barekengvol19iss4pp2301-2316
Published September 1, 2025
Keywords
  • Economic Growth,
  • Forecasting,
  • Macroeconomic Indicator,
  • PCA-VARI,
  • Time Series
How to Cite
[1]
A. R. Al Madani, S. Najwa, and B. N. Ruchjana, “APPLICATION OF THE PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL TO FORECASTING ECONOMIC GROWTH IN INDONESIA”, BAREKENG: J. Math. & App., vol. 19, no. 4, pp. 2301-2316, Sep. 2025.

Copyright (c) 2025 Aulia Rahman Al Madani, Sandrina Najwa, Budi Nurani Ruchjana

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Abstract

Indonesia's economic growth has undergone significant fluctuations in recent years, driven by global shocks such as the 2020 COVID-19 pandemic, the 2013 taper tantrum, and the 2022 global energy crisis. These events underscore the urgent need for more accurate and robust forecasting models to support economic stability and policymaking. This study applies the Principal Component Analysis-Vector Autoregressive Integrated (PCA-VARI) model to forecast economic growth in Indonesia. PCA reduces seven economic variables into two principal components for ten years (2012-2022). The results show that the first component (PC1) shows the highest correlation with the variables of Money Supply, BI Rate, and Foreign Exchange Reserves, which reflect monetary policy and financial stability. Meanwhile, the second component (PC2) is highly correlated to the GDP Index, Exchange Rate, and Inflation variables, which reflect macroeconomic conditions. VARI, as a non-stationary multivariate time series model, is used to model the relationship between these components, with the third-order lag selected as the optimal lag based on the Akaike Information Criterion (AIC), Hannan-Quinn Criterion (HQ), and Final Prediction Error (FPE) values. The results show that the PCA-VARI(3) model is able to provide highly accurate forecasting with a MAPE of 1.21% for PC1 and 1.34% for PC2, and has met all the necessary model assumptions.

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