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All Journal Media Statistika Jurnal Ilmu Dasar Jurnal Sains dan Seni ITS CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan Inferensi
Muhammad Sjahid Akbar
Departemen Statistika Institut Teknologi Sepuluh Nopember Surabaya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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NOWCASTING GROWTH AT RISK IN INDONESIA: APPLICATION OF MIDAS-QUANTILE REGRESSION MODEL Latifah, Turfah; Akbar, Muhammad Sjahid; Prastyo, Dedy Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0673-0690

Abstract

One of the main problems faced by policymakers in economic monitoring is the limited availability of predictive tools that can comprehensively and in real time measure economic growth risks, particularly amid financial market volatility and rapid changes in economic indicators. This study aims to nowcast Indonesian economic growth using the Growth at Risk (GaR) approach by applying the Mixed Data Sampling-Quantile Regression (MIDAS-QR) model. This approach predicts economic risks across different quantiles, capturing best- and worst-case scenarios by integrating multi-frequency indicators, namely the Financial Conditions Index (FCI), External Financial Environment Index (EFEI), and Macroeconomic Prosperity Leading Index (MPLI), summarized using Principal Component Analysis (PCA). Prediction accuracy is evaluated using Quantile Mean Absolute Error (QMAE), Quantile Root Mean Squared Error (QRMSE), and Clark-West (CW) test metrics. The analysis utilizes a dataset of Indonesia covering the period from January 2001 to March 2025, combining quarterly GDP growth data as the dependent variable and monthly predictor variables sourced from the Central Statistics Agency (BPS), Bank Indonesia, and the Indonesia Stock Exchange. The findings show that the MIDAS-QR model significantly improves the accuracy of GaR forecasting in Indonesia relative to conventional approaches. It effectively captures risk asymmetries across quantiles, minimizes predictive errors, and facilitates the timely detection of economic downturns, offering valuable insights for early action. This study highlights the strategic role of high-frequency data in enhancing forecast precision and real-time economic risk monitoring in Indonesia. The application of the MIDAS-QR model presents a valuable tool for policymakers in formulating proactive responses to global economic uncertainty and fostering resilient economic growth.
Co-Authors Adatul Mukarromah Bambang Widjanorko Otok Bambang Widjanorko Otok Bella Puspa Dewani Damayanti, Salma Dedy Dwi Prastyo Destri Susilaningrum Dina Yuanita Fadhilah, Rahmi Fredi Suryadi Fredi Suryadi Lalita Paramita Latifah, Turfah Nita Cahyani Puthy, Khem Riskianto, Riskianto Safira, Dinda Ayu Sri Harini Suhartono Suhartono Supriyatna, Putu Krishnanda Wibawati Wibawati Yani Trimardiani