Bayesian inference for nonlinear structural time series models

TY - JOUR

T1 - Bayesian inference for nonlinear structural time series models

AU - Hall, Jamie Hall

AU - Pitt, Michael Kirkby

AU - kohn, robert

PY - 2014/4

Y1 - 2014/4

N2 - We consider efficient methods for likelihood inference applied to structural models. In particular, we introduce a particle filter method which concentrates upon disturbances in the Markov state of the approximating solution to the structural model. A particular feature of such models is that the conditional distribution of interest for the disturbances is often multimodal. We provide a fast and effective method for approximating such distributions. We estimate a neoclassical growth model using this approach. An asset pricing model with persistent habits is also considered. The methodology we employ allows many fewer particles to be used than alternative procedures for a given precision.

AB - We consider efficient methods for likelihood inference applied to structural models. In particular, we introduce a particle filter method which concentrates upon disturbances in the Markov state of the approximating solution to the structural model. A particular feature of such models is that the conditional distribution of interest for the disturbances is often multimodal. We provide a fast and effective method for approximating such distributions. We estimate a neoclassical growth model using this approach. An asset pricing model with persistent habits is also considered. The methodology we employ allows many fewer particles to be used than alternative procedures for a given precision.

U2 - 10.1016/j.jeconom.2013.10.016

DO - 10.1016/j.jeconom.2013.10.016

M3 - Article

SN - 0304-4076

VL - 179

SP - 99

EP - 111

JO - JOURNAL OF ECONOMETRICS

JF - JOURNAL OF ECONOMETRICS

IS - 2

ER -