Bayesian Computation and Analysis of C-PAR(1) Time Series Model with Structural Break
Panel data consists of repeated observations over time on the same set of cross-sectional units and impact of covariate may influence the estimation and testing procedures. Present paper proposes a covariate panel autoregressive (C-PAR(1)) unit root tests and estimation considering structural break under Bayesian approach. We use Monte Carlo simulation method to estimate the parameters using conditional posterior distribution and compared with ordinary least square estimator. For testing the unit root hypothesis, posterior odds ratio is derived and recorded satisfactory results. We have also studied empirical analysis on import of fertilizers to get more applicability of the model.