Recent Advances of Dynamic Model Averaging Theory and Its Application in Econometrics

Abstract

Dynamic Model Averaging (DMA) was first proposed by Raftery et al. (2010) when predicting the output strip thickness of cold rolling mills and is a recursive implementation of standard Bayesian model averaging, also known as recursive model averaging. Since Gary Koop and Dimitris Korobilis introduced DMA into the field of econometrics in 2012, dynamic model averaging has become a widely used estimation technique in macroeconomic applications because of its ability to adapt to the temporal change of parameters and the advantages of the specification of optimal prediction models, the method has good application prospects. This paper focuses on dynamic model averaging as a solution to model uncertainty problems, focusing on recent theoretical developments and their applications in econometrics. Discussions focused on uncertainties contained in covariates in regression models, such as normal linear regression and its extensions, and on advances in designing models to handle more challenging situations, such as time-dependent, spatially dependent, or endogenous data. The results show that the DMA method has good prediction accuracy, is a powerful tool for actual prediction, and provides important technical support for risk avoidance in management.