New forecasting model reveals how economic seasonality shifts over decades
Researchers have developed a statistical model that can track how seasonal patterns in economic activity change over time—revealing surprising shifts during crises like the Great Depression and COVID-19. The advance matters because businesses and policymakers rely on seasonal forecasts for inventory, staffing, and fiscal planning; a tool that adapts to structural economic change could improve accuracy when it matters most.
Originaltitel: Time-Varying Multi-Seasonal AR Models
<p>We propose a seasonal AR model with time-varying parameter processes in both the regular and seasonal parameters. The model is parameterized to guarantee stability at every time point and can accommodate multiple seasonal periods. The time evolution is modeled by dynamic shrinkage processes to allow for long periods of essentially constant parameters, periods of rapid change, and abrupt jumps. A Gibbs sampler is developed with a particle Gibbs update step for the AR parameter trajectories. We show that the near-degeneracy of the model, caused by the dynamic shrinkage processes, makes it challenging to estimate the model by particle methods. To address this, a more robust, faster, and accurate approximate sampler based on the extended Kalman filter is proposed. The model and the numerical effectiveness of the Gibbs sampler are investigated on simulated data. An application to more than a century of monthly US industrial production data shows interesting clear changes in seasonality over time, particularly during the Great Depression and the recent Covid-19 pandemic.</p>