Bayesian Framework for Error Correction Model-Nonlinear Autoregressive Distributed Lag - Meidy Indhira Putri; Nurjannah Nurjannah; Achmad Efendi; Ani Budi Astuti (CAUCHY: Jurnal Matematika Murni dan Aplikasi, 2026)
This study proposes a Bayesian framework for estimating the Error Correction Model–Nonlinear Autoregressive Distributed Lag (ECM–NARDL) to analyze the dynamic and asymmetric relationship between inflation and economic growth in Indonesia. The model is estimated using Gibbs Sampling within a Markov Chain Monte Carlo (MCMC) framework, allowing parameter uncertainty to be evaluated through posterior distributions. Annual data from 1990–2024 are used for empirical analysis. Unit root tests indicate that the variables are integrated of order one, while the Bounds test confirms the existence of a long-run equilibrium relationship. The estimation results reveal a significant error-correction mechanism, suggesting that deviations from long-run equilibrium are corrected relatively quickly. The findings also indicate asymmetric effects of inflation, where decreases in inflation support long-run economic growth, while short-run increases in inflation negatively affect growth. Bayesian diagnostics and posterior predictive checks confirm the stability and adequacy of the proposed Bayesian ECM–NARDL framework.