All Times EDT
Keywords: Bifurcating, Autoregressive, Bootstrap, Binary tree
In this study, bootstrap bias correction techniques are applied to stationary first order bifurcating autoregressive (BAR) models. The proposed resampling procedures are implemented to correct the bias that found with the least squares estimators of the BAR(1) model. The effectiveness of proposed approaches is studied empirically through extensive Monte Carlo simulations. In our simulation, we compared the mean squares errors and adjusted mean squares errors for the single bootstrap bias-corrected estimators, double bootstrap bias-corrected estimators, and fast double bootstrap bias-corrected estimators. Inference about the corrected estimators is implemented to demonstrate the confidence interval coverage.