Efficient Particle MCMC with GMM Likelihood Representation

Gmm and Empirical Likelihood with Incomplete Data

In applied work economists often encounter data generating mechanisms that produce censored or truncated observations. These dgp’s induce a probability distribution on the realized observations that differs from the underlying distribution for which inference is to be made. If this dichotomy between the target and realized populations is not taken into account, statistical inference can be seve...

Dimension-independent likelihood-informed MCMC

Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that, in principle, can be described as functions. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. Firs...

Efficient GMM with nearly-weak identification

This paper is in the line of the recent literature on weak instruments, which, following the seminal approach of Staiger and Stock (1997) and Stock and Wright (2000) captures weak identification by drifting population moment conditions. In contrast with most of the existing literature, we do not specify a priori which parameters are strongly or weakly identified. We rather consider that weaknes...

Conditional log-likelihood MDL and Evolutionary MCMC

Augmentation schemes for particle MCMC

Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters. We show how particle MCMC can be generalised beyond this. Our key idea is to introduce new...