The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying lengths (T = 25, 50, 75, 100, 125) using Statistical Analysis System (SAS) version 9.2. Autoregressive components for the 5 series vectors included coefficients of 0.80, 0.70, 0.65, 0.50 and 0.40. Error variance components included values of 0.20, 0.20, 0.10, 0.15, and 0.15, with cross-lagged coefficients of 0.10, 0.10, 0.15, 0.10, and 0.10. A Monte Carlo study revealed that in comparison to frequentist methods, the Bayesian approach provided increased sensitivity for hypothesis testing and detecting Type I error. (Contains 5 tables and 1 figure.)