2019/12/10 by Alisa Yusupova, Yusupova, Alisa, Nicos G. Pavlidis +4 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Engineering · Mathematics · #Econometrics (econ.EM) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #Forecasting Techniques and Applications #Housing Market and Economics #Market Dynamics and Volatility #Methodology (stat.ME) #econ.EM #stat.ME
paper · pdf · doi:10.48550/arxiv.1912.04661
arxiv created 2019/12/10 · openalex publication_date 2019/12/10 · arxiv updated 2019/12/11 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28
Dynamic model averaging (DMA) combines the forecasts of a large number of dynamic linear models (DLMs) to predict the future value of a time series. The performance of DMA critically depends on the appropriate choice of two forgetting factors. The first of these controls the speed of adaptation of the coefficient vector of each DLM, while the second enables time variation in the model averaging stage. In this paper we develop a novel, adaptive dynamic model averaging (ADMA) methodology. The proposed methodology employs a stochastic optimisation algorithm that sequentially updates the forgetting factor of each DLM, and uses a state-of-the-art non-parametric model combination algorithm from the prediction with expert advice literature, which offers finite-time performance guarantees. An empirical application to quarterly UK house price data suggests that ADMA produces more accurate forecasts than the benchmark autoregressive model, as well as competing DMA specifications.