Malik, Sheheryar, Pitt, Michael K.
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...
State Space Modelling and Simulation Filter Methods (2007)
Michael K. Pitt, Neil Shephard
Introduction We model a time series y t , t =1;:::;n, using a state space model. That is y t is conditionally independentgiven an unobserved su#cient state # t ; which is itself assumed to be...
Antithetic Variables For MCMC Methods Applied To Non-Gaussian State Space Models (2007)
Michael K. Pitt, Neil Shephard
this paper we combine the blocking method of (Shephard and Pitt 1997) with the antithetic approach of (Green and Han 1990) for non-Gaussian state space models. The introduction of antithetic methods...
Michael K Pitt, Michael K Pitt, Stephen G Walker, Stephen G Walker
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...
Siddhartha Chib, Michael K Pitt, Neil Shephard
based inference for diffusion driven state space models
Smooth particle filters for likelihood evaluation and maximisation (2002)
In this paper,a method is introduced for approximating the likelihood for the unknown parameters of a state space model.The approximation converges to the true likelihood as the simulation size goes...
Smooth particle filters for likelihood evaluation and maximisation (2002)
Michael Kpit Department, Michael K Pitt, Michael K Pitt
In this pape r a method is intr ducedfor appr ximating the likelihoodfor the unknown par"532KM of a state space model. Theappr ximation conver99 to thetr3 likelihood as the simulation size goes...
Pitt, Michael K., Walker, S. G. (Stephen G.)
In this paper, we provide a method for modelling stationary time series. We allow the family of marginal densities for the observations to be specified. Our approach is to construct the model with a...
Marginal Construction of Stationary Time Series with application to Volatility Models (1998)
Michael K. Pitt, Stephen, Stephen G. Walker
In this paper, we provide a new method for modelling stationary time series, concentrating on volatility models. Knowledge about the marginal family for the observables is usually quite specific, for...
Bayesian inference for non-Gaussian state space models using simulation /--Michael K. Pitt. (1997)
Pitt, Michael K., University Of Oxford.--Faculty Of Social Studies.--Thesis.
BLDSC reference no.: D198188.
Likelihood analysis of non-Gaussian measurement time series (1997)
SHEPHARD, NEIL, PITT, MICHAEL K.
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...
Likelihood based inference for diffusion driven models
Siddhartha Chib, Michael K Pitt, Neil Shephard
This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility...
Smooth Particle Filters for Likelihood Evaluation and Maximisation
In this paper, a method is introduced for approximating the likelihood for the unknown parameters of a state space model. The approximation converges to the true likelihood as the simulation size...
Bell, Brian D, Pitt, Michael K
This paper assesses the impact of changes in union density on the male structure in the United Kingdom over the 1980s. Using four separate data sets, the authors estimate the kernel density of hourly...
Likelihood based inference for diffusion driven models
Siddhartha Chib, Michael K Pitt, Neil Shephard
This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility...
In this paper we obtain a closed form expression for the convergence rate of the Gibbs sampler applied to an AR(1) plus noise model in terms of the parameters of the model. We also provide evidence...
Extended constructions of stationary autoregressive processes
Pitt, Michael K., Walker, Stephen G.
This paper extends recent ideas for constructing classes of stationary autoregressive processes of order 1. A Gibbs sampler representation of such processes is extended in a straightforward way to...
Malik, Sheheryar, Pitt, Michael K
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...