Dalimunthe, Amir Abduljabbar
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Performance Evaluation of ARDL Model Stacked with Boosted Ridge Regression on Time Series Data with Multicollinearity: Evaluasi Kinerja Estimasi Model ARDL stacked with Boosted Ridge Regression pada Data Deret Waktu dengan Multikolinearitas Dalimunthe, Amir Abduljabbar; Soleh, Agus Mohamad; Afendi, Farit Mochamad
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p136-144

Abstract

Time series data plays a vital role in financial and economic study. Two commonly applied models for such data are Vector Autoregression (VAR) and Autoregressive Distributed Lags (ARDL). Nonetheless, interdependence among explanatory variables often leads to multicollinearity, posing challenges for model reliability. This study investigates the effectiveness of the ARDL model integrated with boosted ridge regression as a method to mitigate multicollinearity. Due to limitations in available empirical data, simulation data will be generated to support the analysis. The research consists of two stages: synthetic data generation and analysis on simulated data. Results suggest that ARDL performs well under various multicollinearity conditions, particularly when the training set is sufficiently large and model structure is correctly specified. For smaller training sets, the ARDL Ridge variant demonstrates improved predictive performance.