Loading...
Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
Durbin,J. Koopman,S.J.M.
Durbin,J.
Koopman,S.J.M.
Abstract
The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian disturbances for the state equation as well as for the observation equation are considered. Methods for estimating conditional and posterior means of functions of the state vector given the observations, and the mean square errors of their estimates, are developed. These methods are extended to cover the estimation of conditional and posterior densities and distribution functions. Choice of importance sampling densities and antithetic variables is discussed. The techniques work well in practice and are computationally effcient. Their use is illustrated by applying to a univariate discrete time series, a series with outliers and a volatility series.
Description
Pagination: 26
Date
1998
Journal Title
Journal ISSN
Volume Title
Publisher
Econometrics
Files
Loading...
142.pdf
Adobe PDF, 317.35 KB
Research Projects
Organizational Units
Journal Issue
Keywords
Antithetic variables, Conditional and posterior statistics, Exponential family distributions, Heavy-tailed distributions, Importance sampling, Kalman filtering and smoothing, Monte Carlo simulation, Non-Gaussian time series models, Posterior distributions, C15 - Statistical Simulation Methods: General, C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
Citation
Durbin, J & Koopman, S J M 1998 'Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives' CentER Discussion Paper, vol. 1998-142, Econometrics, Tilburg.