This paper extends the work of Baltagi et al. (2018) to the popular dynamic panel data model. We investigate the robustness of Bayesian panel data models to possible misspecication of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in...

The present paper studies the panel data auto regressive (PAR) time series model for testing the unit root hypothesis. The posterior odds ratio (POR) is derived under appropriate prior assumptions and then empirical analysis is carried out for testing the unit root hypothesis of Net Asset Value...

The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior...

The main objective of present paper is to consider robust Bayesian analysis of the Weibull failure model under an <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\varepsilon $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi mathvariant="italic">ε</mi> </math> </EquationSource> </InlineEquation>-contamination class of priors for the parameters. The Bayes estimators for the mean life, reliability function and failure rate are obtained under the...</equationsource></equationsource></inlineequation>

This paper deals with the problem of Stein-rule prediction in a general linear model. Our study extends the work of Gotway and Cressie (1993) by assuming that the covariance matrix of the model's disturbances is unknown. Also, predictions are based on a composite target function that...

In this paper, we consider a family of feasible generalised double k-class estimators in a linear regression model with non-spherical disturbances. We derive the large sample asymptotic distribution of the proposed family of estimators and compare its performance with the feasible generalized...