We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002) does not necessarily lead to consistent estimation of common parameters...

In the context of an autoregressive panel data model with fixed effect, we examine the relationship between consistent parameter estimation and consistent model selection. Consistency in parameter estimation is achieved by using the transformation of the fixed effect proposed by Lancaster...

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....

In this paper we consider the problem of identifying an autoregressive model for an observed time series and detecting a possible unit root in its characteristic polynomial. This is a big issue concerned with distinguishing stationary time series from time series for which differencing is...

The full Bayesian treatment of error component models typically relies on data augmentation to produce the required inference. Never stricly necessary a direct approach is always possible though not necessarily practical. The mechanics of direct sampling are outlined and a template for including...

The full Bayesian treatment of error component models typically relies on data augmentation to produce the required inference. Never stricly necessary a direct approach is always possible though not necessarily practical. The mechanics of direct sampling are outlined and a template for including...

In this paper we deal with the identification of an autoregressive model for an observed time series, and the detection of a unit root in its characteristic polynomial. This is a big issue concerned with distinguishing stationary time series from time series for which differencing is required to...

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set...

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with...

This paper discusses Bayesian procedures for factor selection in dynamic term structure models through simulation methods based on Markov Chain Monte Carlo. The number of factors, besides influencing the fitting and prediction of observed yields, is also relevant to features such as the...