State-space models also known as Hidden Markov models are ubiquitous time series models in ecology, econometrics, engineering, statistics etc. Let fXngn1 be a latent/hidden Markov process de…ned by X1 µθ () and Xn j (Xn1 = xn1) fθ ( j xn1). We only have access to a process fYngn1 such that

, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact Expectation Maximization (EM) alogithm cannot be applied. However, we present a variational approximation which

Abstract. Resampling the innovations sequence of state space models has proved to be a useful tool in many respects. For example, while under general conditions, the Gaussian MLEs of the parameters of a state space model are asymptotically normal, several researchers have found that samples must

Nonlinear state-space models with state dependent variances (SDV) are commonly used in financial...

In this paper we consider the utilization of wavelets in conjunction with state space models. Specifically, the parameters in the system matrix are expanded in wavelet series and estimated via the Kalman Filter and the EM algorithm. In particular this approach is used for switching models. Two

State space modeling in multichannel active control systems ∗

This article provides a brief introduction to the state space modeling capabilities in SAS, a well-known statistical software system. SAS provides state space modeling in a few different settings. SAS/ETS, the econometric and time series analysis module of the SAS system, contains many procedures

Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space models

words: linear state-space models, Kalman filtering, functional neuroimaging, optical imaging, orientation columns. 1

This paper demonstrates how state space models can be fitted in EViews. We first briefly introduce EViews as an econometric software package. Next we fit a local level model to the Nile data. We then show how a multivariate “latent risk ” model can be developed, making use of the EViews programming