A simple and efficient simulation smoother for state space time series analysis

Durbin, J.; Koopman, Siem Jan

doi:10.1093/biomet/89.3.603

Cited by 539 publications

(510 citation statements)

References 8 publications

Supporting

Mentioning

509

Contrasting

Unclassified

Order By: Relevance

“…de Jong and Shephard (1995) and Durbin and Koopman (2002) have developed simulation smoothing methods for sampling α from g(α|y * ; ψ) in a computationally efficient way. The Kalman filter calculates g(y * ; ψ) via its evaluation of the likelihood function for the linear state space model (A.4).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Koopman

Scharth

2012

SSRN Journal

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

“…Two strategies allow us to improve the efficiency of f . First, we use antithetic variables for variance reduction; see for example Durbin and Koopman (2002). Second, we note that observations far in the past add little or no information about the current state α t , but contribute to the variance of the importance weights ω s .…”

Section: (B3)mentioning

confidence: 99%

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Koopman

Scharth

2012

SSRN Journal

View full text Add to dashboard Cite

“…Third, because the method does not require any auxiliary simulation (c.f. Durbin and Koopman, 2002), it is scalable as the size of t y grows.…”

Section: Efficient Estimation Of the Latent Statesmentioning

confidence: 99%

“…The methods are carefully reviewed in Kim and Nelson (1999) and Durbin and Koopman (2001) and the basic recursions are briefly summarised in Appendix A. Important further refinements of the simulation approach are offered in de Jong and Shephard (1995), using the distribution of the errors in the model and in Durbin and Koopman (2002), using auxiliary data samples. Another sampling improvement was recently introduced by , who suggested that because t G and t η enter multiplicatively in equation (1), they should be sampled jointly.…”

Section: Introductionmentioning

confidence: 99%

Efficient simulation and integrated likelihood estimation in state space models

Chan¹,

Jeliazkov²

2009

IJMMNO

250

284

View full text Add to dashboard Cite

Abstract: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 integrated likelihood, defined as the density of the data given the parameters but marginal of the state vector. We show that this high-dimensional integral can be easily evaluated with minimal computational and conceptual difficulty. Two empirical applications in macroeconomics demonstrate that the methods are versatile and computationally undemanding. In one application, involving a time-varying parameter model, we show that the methods allow for efficient handling of large state vectors. In our second application, involving a dynamic factor model, we introduce a new blocking strategy which results in improved MCMC mixing at little cost. The results demonstrate that the framework is simple, flexible and efficient.

show abstract

“…Alternative algorithms are provided by Carter and Kohn (1994), De Jong and Shephard (1995), Durbin and Koopman (2002), Frühwirth-Schnatter (2004) and Strickland, Turner, Denham, and Mengerson (2009). PySSM contains a simulation smoothing class, which uses the most computationally efficient, of the aforementioned simulation smoothing algorithms, given the specified SSM.…”

Section: Bayesian Estimation Of Ssmsmentioning

confidence: 99%

Journal of Statistical Software

Leeuw

2009

WIREs Computational Stats

View full text Add to dashboard Cite

PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models. PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries NumPy and SciPy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimized and parallelized Fortran routines. These Fortran routines heavily utilize basic linear algebra and linear algebra Package functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

show abstract

A simple and efficient simulation smoother for state space time series analysis

Abstract: This document contains my notes relating to the paper, "A simple and efficient simulation smoother for state space time series analysis", by Durbin and Koopman(2002).

Cited by 539 publications

References 8 publications

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Efficient simulation and integrated likelihood estimation in state space models

Journal of Statistical Software

Contact Info

Product

Resources

About