A novel recursive bias method is proposed for dynamic panel data models to reduce the estimator bias without large N or T, or both. Recursively, it decomposes the estimator bias into systematic and random components. The application compares three different sets of estimators with different methodologies, all using a small sample size of 60 and Maximum Likelihood Estimation. The first set uses the novel recursive bias method with synthetic data, the second set uses the expo-power utility method using real data, and the third set uses the traditional asymptotic bias method based on Monte Carlo simulations. This comparison demonstrates that synthetic estimators gain efficiency and are closest to the "true parameter value". The novel method is less expensive in computational power and processing time than Monte Carlo simulations. As a result, the proposed method could be a feasible option to provide efficient estimators for robust statistical inference and decision making.
