Syarifah Inayati, Nur Iriawan, Irhamah, Uha Isnaini
Highlights: What are the main findings? A Bayesian Markov switching autoregressive model with time-varying parameters (Bayesian MSAR-TVP) is proposed for forecasting nonlinear time series data with stochastic structural variations. The Bayesian MSAR-TVP model outperforms the Classical MSAR, Classical MSAR-TVP, and Bayesian AR-TVP models in forecasting U.S. real GNP, particularly in handling complex datasets and out-of-sample forecasting. What are the implications of the main findings? The proposed model addresses parameter uncertainty and adapts dynamically to regime shifts, enhancing the accuracy and reliability of economic forecasting. Its ability to capture economic volatility and structural breaks makes it an important tool for policy formulation and long-term economic predictions. This research tackles the challenge of forecasting nonlinear time series data with stochastic structural variations by proposing the Markov switching autoregressive model with time-varying parameters (MSAR-TVP). Although effective in modeling dynamic regime transitions, the Classical MSAR-TVP faces challenges with complex datasets. To address these issues, a Bayesian MSAR-TVP framework was developed, incorporating flexible parameters that adapt dynamically across regimes. The model was tested on two periods of U.S. real GNP data: a historically stable segment (1952–1986) and a more complex, modern segment that includes more economic volatility (1947–2024). The Bayesian MSAR-TVP demonstrated superior performance in handling complex datasets, particularly in out-of-sample forecasting, outperforming the Bayesian AR-TVP, Classical MSAR-TVP, and Classical MSAR models, as evaluated by mean absolute percentage error (MAPE) and mean absolute error (MAE). For in-sample data, the Classical MSAR-TVP retained its stability advantage. These findings highlight the Bayesian MSAR-TVP’s ability to address parameter uncertainty and adapt to data fluctuations, making it highly effective for forecasting dynamic economic cycles. Additionally, the two-year forecast underscores its practical utility in predicting economic cycles, suggesting continued expansion. This reinforces the model’s significance for economic forecasting and strategic policy formulation. © 2025 by the authors.
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta, Yogyakarta, 55281, Indonesia; Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia; Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
