Information-theoretic optimality of observation-driven time series models for continuous responses

• 成果类型: Article
• 署名作者: Blasques, F.; Koopman, S. J.; Lucas, A.
• 署名单位: Vrije Universiteit Amsterdam
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu076
• 发表日期: 2015
• 页码: 325343
• 关键词: divergence; entropy; state

摘要

We investigate information-theoretic optimality properties of the score function of the predictive likelihood as a device for updating a real-valued time-varying parameter in a univariate observation-driven model with continuous responses. We restrict our attention to models with updates of one lag order. The results provide theoretical justification for a class of score-driven models which includes the generalized autoregressive conditional heteroskedasticity model as a special case. Our main contribution is to show that only parameter updates based on the score will always reduce the local Kullback-Leibler divergence between the true conditional density and the model-implied conditional density. This result holds irrespective of the severity of model mis-specification. We also show that use of the score leads to a considerably smaller global Kullback-Leibler divergence in empirically relevant settings. We illustrate the theory with an application to time-varying volatility models. We show that the reduction in Kullback-Leibler divergence across a range of different settings can be substantial compared to updates based on, for example, squared lagged observations.