Imperial College London
Alternate

Dr Nikolas Kantas

Faculty of Natural Sciences, Department of Mathematics

Reader in Statistics
 
 
 
//

Contact

 

+44 (0)20 7594 2772n.kantas Website

 
 
//

Location

 

538Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Jasra:2014:10.1145/2592254,
author = {Jasra, A and Kantas, N and Ehrlich, E},
doi = {10.1145/2592254},
journal = {ACM Transactions on Modeling and Computer Simulation},
title = {Approximate inference for observation-driven time series models with intractable likelihoods},
url = {http://dx.doi.org/10.1145/2592254},
volume = {24},
year = {2014}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In this article, we consider approximate Bayesian parameter inference for observation-driven time series models. Such statistical models appear in a wide variety of applications, including econometrics and applied mathematics. This article considers the scenario where the likelihood function cannot be evaluated pointwise; in such cases, one cannot perform exact statistical inference, including parameter estimation, which often requires advanced computational algorithms, such as Markov Chain Monte Carlo (MCMC). We introduce a new approximation based upon Approximate Bayesian Computation (ABC). Under some conditions, we show that as n → ∞, with n the length of the time series, the ABC posterior has, almost surely, a Maximum A Posteriori (MAP) estimator of the parameters that is often different from the true parameter. However, a noisy ABC MAP, which perturbs the original data, asymptotically converges to the true parameter, almost surely. In order to draw statistical inference, for the ABC approximation adopted, standard MCMC algorithms can have acceptance probabilities that fall at an exponential rate in n and slightly more advanced algorithms can mix poorly. We develop a new and improved MCMC kernel, which is based upon an exact approximation of a marginal algorithm, whose cost per iteration is random, but the expected cost, for good performance, is shown to be O(n2) per iteration. We implement our new MCMC kernel for parameter inference from models in econometrics.
AU  - Jasra,A
AU  - Kantas,N
AU  - Ehrlich,E
DO  - 10.1145/2592254
PY  - 2014///
SN  - 1558-1195
TI  - Approximate inference for observation-driven time series models with intractable likelihoods
T2  - ACM Transactions on Modeling and Computer Simulation
UR  - http://dx.doi.org/10.1145/2592254
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000341073600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/40829
VL  - 24
ER  -
Download