#### Filter Results:

- Full text PDF available (13)

#### Publication Year

1998

2016

- This year (0)
- Last 5 years (2)
- Last 10 years (5)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

In this paper we provide methods for estimating non-Gaussian time series models. These techniques rely on Markov chain Monte Carlo to carry out simulation smoothing and Bayesian posterior analysis of parameters, and on importance sampling to estimate the likelihood function for classical inference. The time series structure of the models is used to ensure… (More)

We propose a factor model which allows a parsimonious representation of the time series evolution of covariances when the number of series being modelled becomes very large. The factors arise from a standard stochastic volatility model as does the idiosyncratic noise associated with each series. We use an efficient method for deriving the posterior… (More)

This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility models and counting process models. The diffusions can potentially be nonstationary. Although our methods are sampling based, making use of Markov chain Monte… (More)

We model a time series fyt; t = 1; :::; ng using a state space framework with the fytj tg being independent and with the state f tg assumed to be Markovian. The task will be to use simulation to estimate f( tjFt), t = 1; :::; n, where Ft is contemporaneously available information. We assume a known `measurement' density f(ytj t) and the ability to simulate… (More)

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… (More)

In this paper we provide a unified methodology in order to conduct likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space form, approximating the likelihood for the parameters is conducted… (More)

This paper provides methods for carrying out likelihood based inference on non-linear observed and partially observed non-linear diffusions. The diffusions can potentially be non-stationary. The methods are based on innovative Markov chain Monte Carlo methods combined with an augmentation strategy. We study the performance of the methods as the degree of… (More)

In this paper, we provide a method for modelling stationary time series. We allow the family of marginal densities for the observations to be speci ed. Our approach is to construct the model with a speci ed marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation structure.… (More)

- Minh-Ngoc Tran, Michael K. Pitt, Robert Kohn
- Statistics and Computing
- 2016

This article develops a general-purpose adaptive sampler for sampling from a high-dimensional and/or multimodal target. The adaptive sampler is based on reversible proposal densities each ofwhich has amixture ofmultivariate t densities as its invariant density. The reversible proposals are a combination of independent and correlated components that allow… (More)

- Michael K. Pitt
- 1998

In this paper, we provide a new method for modelling stationary time series, concentrating on volatility models. Knowledge about the marginalfamily for the observables is usually quite speciic, for example, exchange rate returns are thought to follow a Student-t. Our approach is to construct the model with a speciied marginal family and build the dependence… (More)