Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model....

The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for datasets with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework where the likelihood function is estimated from a random subset of...

We develop a new model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a...

In multichannel retailing, customers often use a mix of a firm's online and physical stores for their search and buy activities. We define the customer's “channel engagement” as a latent attitude or predisposition towards the firm's online and offline channels which dynamically transitions...

In this paper, we propose a Markov Chain Quasi-Monte Carlo (MCQMC) approach for Bayesian estimation of a discrete-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under the conventional Bayesian estimation method for SV...

This paper revisits the non-Markovian regime switching model considered by Chib and Dueker (2004), who employ an autoregressive continuous latent variable in order to specify the dynamics of the latent regime-indicator variable. We show that, in spite of the non-Markovian nature of the regime...

The complexity of Markov Chain Monte Carlo (MCMC) algorithms arises from the requirement of a likelihood evaluation for the full data set in each iteration. Payne and Mallick (2014) propose to speed up the Metropolis-Hastings algorithm by a delayed acceptance approach where the acceptance...

This paper proposes a Differential-Independence Mixture Ensemble (DIME) sampler for the Bayesian estimation of macroeconomic models. It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may also be computationally expensive to evaluate. DIME...

Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes. The switching between the two VAR processes is governed by a two state Markov chain with transition probabilities that depend on how long...