Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/129721 
Erscheinungsjahr: 
2015
Schriftenreihe/Nr.: 
Sveriges Riksbank Working Paper Series No. 307
Verlag: 
Sveriges Riksbank, Stockholm
Zusammenfassung: 
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using only a small fraction of the data. Our algorithm improves on the O(n) complexity of regular MCMC by operating over local data clusters instead of the full sample when computing the likelihood. The likelihood estimate is used in a Pseudo- marginal framework to sample from a perturbed posterior which is within O(m-1/2) of the true posterior, where m is the subsample size. The method is applied to a logistic regression model to predict firm bankruptcy for a large data set. We document a significant speed up in comparison to the standard MCMC on the full dataset.
Schlagwörter: 
Bayesian inference
Markov Chain Monte Carlo
Pseudo-marginal MCMC
estimated likelihood
GLM for large data
JEL: 
C11
C13
C15
C55
C83
Dokumentart: 
Working Paper
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