Bandwidth plays an important role in determining the performance of local linear estimators. In this paper, we propose a Bayesian approach to bandwidth selection for local linear estimation of time–varying coefficient time series models, where the errors are assumed to follow the Gaussian...

We propose a sampling approach to bandwidth estimation for a nonparametric regression model with continuous and discrete types of regressors and unknown error density. The unknown error density is approximated by a location-mixture of Gaussian densities with means being the individual errors,...

The multiple try Metropolis (MTM) method is a generalization of the classical Metropolis–Hastings algorithm in which the next state of the chain is chosen among a set of samples, according to normalized weights. In the literature, several extensions have been proposed. In this work, we show...

Markov chain Monte Carlo (MCMC) methods are powerful computational tools for analysis of complex statistical problems. However, their computational efficiency is highly dependent on the chosen proposal distribution, which is generally difficult to find. One way to solve this problem is to use...

This paper provides a general procedure to estimate structural VARs. The algorithm can be used in constant or time varying coefficient models, and in the latter case, the law of motion of the coefficients can be linear or non-linear. It can deal in a unified way with just-identified (recursive...

The condition of detailed balance has long been used as a proxy for the more difficult-to-prove condition of total balance, which along with ergodicity is required to guarantee convergence of a Markov Chain Monte Carlo (MCMC) simulation to the correct probability distribution. However, some...