Comparing MIDAS and Bayesian VAR Models for GDP Forecasting: Insights from Simulation and Empirical Studies

This study compares Bayesian vector autoregression (BVAR) and mixed-data sampling (MIDAS) models, assessing their forecasting sensitivity to model misspecification through simulations. Using real-time series data, model efficiency is measured by root mean squared error values. We also analyze the effectiveness of MIDAS and BVAR in modeling mixed-frequency time series data. We examine model validity, selection methods, forecast precision, and aggregate forecasts. The results demonstrate that while both models offer good GDP forecasts, the Bayesian VAR model outperforms MIDAS. The study underscores the suitability of Bayesian VAR models for GDP forecasting in unstable economies like Palestine, emphasizing their superior performance compared to MIDAS models.