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多状态状态空间模型的滤波与平滑AI 解读

This paper improves Bayesian filtering techniques in regime-switching state-space models and develops a novel recursion-based smoother for latent variables. The smoother is computationally stable, adaptable to different filters, and easy to implement. We assess its performance in a New Keynesian DSGE model pairing it with three practical filters: the Generalized Pseudo-Bayesian filters of order one (GPB1) and two (GPB2, often referred to as the Kim or Kim–Nelson filter in…

以分数过程为积分器的随机积分表示与弱收敛AI 解读

This paper considers the asymptotic distribution of the sample covariance of a nonstationary fractionally integrated process with the stationary increments of another such process—possibly itself. Questions of interest include the relationship between the harmonic representation of these random variables, which we have analyzed in a previous paper (Davidson and Hashimzade, 2008), and the construction derived from moving average representations in the time domain. Depending…

分数布朗运动的不同频域与时域版本AI 解读

This paper compares models of fractional processes and associated weak convergence results based on moving average representations in the time domain with spectral representations. Both approaches have been applied in the literature on fractional processes. We point out that the conventional forms of these models are not equivalent, as is commonly assumed, even under a Gaussianity assumption. We show that it is necessary to distinguish between “two-sided” processes depending…