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Sequential Monte Carlo sampling for state space models

Wüthrich, Mario V. - In: Robustness in econometrics, (pp. 25-50). 2017

The aim of these notes is to revisit sequential Monte Carlo (SMC) sampling. SMC sampling is a powerful simulation tool …

Persistent link: https://www.econbiz.de/10011800920

ML-estimation of sampled stochastic differential equations

Singer, Hermann - 2009

Persistent link: https://www.econbiz.de/10003876985

Langevin and Kalman importance sampling for nonlinear continuous-discrete state space models

Singer, Hermann - 2017

Persistent link: https://www.econbiz.de/10011619759

Numerically accelerated importance sampling for nonlinear non-Gaussian state-space models

Koopman, Siem Jan; Lucas, André; Scharth, Marcel - In: Journal of business & economic statistics : JBES ; a … 33 (2015) 1, pp. 114-127

Persistent link: https://www.econbiz.de/10011389921

Flexible model comparison of unobserved components models using particle Gibbs with ancestor sampling

Nonejad, Nima - In: Economics letters 133 (2015), pp. 35-39

Persistent link: https://www.econbiz.de/10011431849

The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models

Grothe, Oliver - 2017

Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the … generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance … sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially …

Persistent link: https://www.econbiz.de/10012970355