We propose a methodology for filtering, smoothing and assessing parameter and filtering uncertainty in score-driven models. Our technique is based on a general representation of the Kalman filter and smoother recursions for linear Gaussian models in terms of the score of the conditional log-likelihood. We prove that, when data is generated by a nonlinear non-Gaussian state-space model, the proposed methodology results from a local expansion of the true filtering density. A formal characterization of the approximation error is provided. As shown in extensive Monte Carlo analyses, our methodology performs very similarly to exact simulation-based methods, while remaining computationally extremely simple. We illustrate empirically the advantages in employing score-driven models as approximate filters rather than purely predictive processes.

翻译：我们建议采用一种方法来过滤、平滑和评估参数,并过滤得分驱动模型的不确定性。我们的技术基于卡尔曼过滤器的一般代表性和线性高斯模型在有条件日志相似度的分数方面的平稳循环。我们证明,当数据来自非线性非古西文国家空间模型时,拟议方法来自真实过滤密度的局部扩展。提供了近似误差的正式特征描述。正如广泛的蒙特卡洛分析所显示的那样,我们的方法与精确的模拟法非常相似,同时仍然非常简单。我们从经验上说明了使用得分模型作为近似过滤器而不是纯粹的预测过程的优势。