Likelihood functions for state space models with diffuse initial conditions
نویسندگان
چکیده
منابع مشابه
Efficient Likelihood Estimation in State Space Models
Motivated by studying asymptotic properties of the maximum likelihood estimator (MLE) in stochastic volatility (SV) models, in this paper we investigate likelihood estimation in state space models. We first prove, under some regularity conditions, there is a consistent sequence of roots of the likelihood equation that is asymptotically normal with the inverse of the Fisher information as its va...
متن کاملLikelihood based inference for diffusion driven state space models
In this paper we develop likelihood based inferential methods for a novel class of (potentially non-stationary) diffusion driven state space models. Examples of models in this class are continuous time stochastic volatility models and counting process models. Although our methods are sampling based, making use of Markov chain Monte Carlo methods to sample the posterior distribution of the relev...
متن کاملEfficient simulation and integrated likelihood estimation in state space models
We consider the problem of implementing simple and efficient Markov chain Monte Carlo (MCMC) estimation algorithms for state space models. A conceptually transparent derivation of the posterior distribution of the states is discussed, which also leads to an efficient simulation algorithm that is modular, scalable, and widely applicable. We also discuss a simple approach for evaluating the integ...
متن کاملChoosing the observational likelihood in state-space stock assessment models
Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations a priori. By implementing several observational likelihoods, modelling both numbersand proportions-at-a...
متن کاملNewton-based maximum likelihood estimation in nonlinear state space models ?
Maximum likelihood (ML) estimation using Newton’s method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the loglikelihood and its gradient and Hessian. We estimate the gradient and Hessian using Fisher’s identity in combination with a smoothing algorithm. We explore two approximations of the log-likelihood and of the solution of the smoot...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Time Series Analysis
سال: 2010
ISSN: 0143-9782
DOI: 10.1111/j.1467-9892.2010.00673.x