Mixed-Data Sampling Quantile Regression (MIDAS-QR) for Nowcasting Indonesia’s Economic Growth-at-Risk of Non-Oil and Gas Processing Industry Sector

Authors

  • Muhammad Rizal Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, INDONESIA
  • Dedy Dwi Prastyo Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, INDONESIA
  • Muhammad Sjahid Akbar Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, INDONESIA

DOI:

https://doi.org/10.22452/mjs.vol45sp2.11

Keywords:

GaR, MIDAS-QR, FCI, GDP Growth

Abstract

The non-oil and gas processing industry plays a vital role in economic stability, making timely Gross Domestic Product (GDP) nowcasting essential for managing economic fluctuations and shaping effective macroeconomic policies. This study applies the Mixed Data Sampling Quantile Regression (MIDAS-QR) model to nowcast GDP growth in the non-oil and gas processing industry, a key driver of economic stability. By integrating high-frequency financial indicators, such as the Financial Stress Index (FSI) and Financial Condition Index (FCI), derived through principal component analysis, the model effectively captures non-linear patterns using an Exponential Almon Lag Polynomial function. Covering data from January 2011 to December 2023, the MIDAS-QR model demonstrates strong predictive accuracy, particularly in the electronic goods and basic metals sectors. The best performing model for nowcasting is found in the median quantile (τ = 0.5), which produces the smallest error in both the electronic goods and base metals sectors. This is because the median value tends to be closer to the actual value than the other quantiles. The symmetric Mean Absolute Percentage Error (sMAPE) value in the training data for the electronic goods sector is 7.32% and testing is 9.16%, while for the base metal sector, the sMAPE value in the training data is 5.21% and testing is 4.09%. These findings enhance Growth-at-Risk (GaR) assessments, providing policymakers and industry leaders with critical insights for navigating economic uncertainties and shaping effective macroeconomic policies.

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Published

15-11-2026

Issue

Vol. 45 No. Sp2 (2026): Special Issue: 5th International Conference on Applied & Industrial Mathematics and Statistics 2025 (ICoAIMS 2025)

Section

Special Issue - 5th ICoAIMS 2025

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