Performance Evaluation of ARDL Model Stacked with Boosted Ridge Regression on Time Series Data with Multicollinearity

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.