Department of Economics and Business Economics

Modified efficient importance sampling for partially non-Gaussian state space models

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Modified efficient importance sampling for partially non-Gaussian state space models. / Koopman, Siem Jan; Lit, Rutger; Nguyen, Thuy Minh.

In: Statistica Neerlandica, Vol. 73, No. 1, 2019, p. 44-62.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Koopman, Siem Jan ; Lit, Rutger ; Nguyen, Thuy Minh. / Modified efficient importance sampling for partially non-Gaussian state space models. In: Statistica Neerlandica. 2019 ; Vol. 73, No. 1. pp. 44-62.

Bibtex

@article{b7d12c22f2274c769a9d7d5c36347d97,
title = "Modified efficient importance sampling for partially non-Gaussian state space models",
abstract = "The construction of an importance density for partially non-Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom-built procedure. For the class of partially non-Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.",
keywords = "efficient importance sampling, Kalman filter, Monte Carlo maximum likelihood, non-Gaussian dynamic models, simulation smoothing",
author = "Koopman, {Siem Jan} and Rutger Lit and Nguyen, {Thuy Minh}",
note = "doi: 10.1111/stan.12128",
year = "2019",
doi = "10.1111/stan.12128",
language = "English",
volume = "73",
pages = "44--62",
journal = "Statistica Neerlandica",
issn = "0039-0402",
publisher = "Wiley-Blackwell Publishing Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Modified efficient importance sampling for partially non-Gaussian state space models

AU - Koopman, Siem Jan

AU - Lit, Rutger

AU - Nguyen, Thuy Minh

N1 - doi: 10.1111/stan.12128

PY - 2019

Y1 - 2019

N2 - The construction of an importance density for partially non-Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom-built procedure. For the class of partially non-Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.

AB - The construction of an importance density for partially non-Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom-built procedure. For the class of partially non-Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.

KW - efficient importance sampling

KW - Kalman filter

KW - Monte Carlo maximum likelihood

KW - non-Gaussian dynamic models

KW - simulation smoothing

U2 - 10.1111/stan.12128

DO - 10.1111/stan.12128

M3 - Journal article

VL - 73

SP - 44

EP - 62

JO - Statistica Neerlandica

JF - Statistica Neerlandica

SN - 0039-0402

IS - 1

ER -