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条件得分残差与时间序列模型的序列依赖诊断AI 解读

This paper introduces conditional score residuals and proposes a general framework for the diagnostic analysis of time series models. Conditional score residuals encompass commonly used definitions of residuals in time series models, including ARMA residuals, squared residuals, and Pearson residuals. In particular, these residuals are special cases of conditional score residuals when the conditional distribution of the model belongs to the exponential family. On the other…

连续响应观测驱动时间序列模型的信息论最优性AI 解读

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…

非线性非高斯状态空间模型的蒙特卡洛估计AI 解读

We develop a proposal or importance density for state space models with a nonlinear non-Gaussian observation vector y ∼ p(y¦θ) and an unobserved linear Gaussian signal vector θ ∼ p(θ). The proposal density is obtained from the Laplace approximation of the smoothing density p(θ¦y). We present efficient algorithms to calculate the mode of p(θ¦y) and to sample from the proposal density. The samples can be used for importance sampling and Markov chain Monte Carlo methods. The…