Parameter Estimation for First-Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter

Abstract

In this article, we propose a new estimate algorithm for the parameters of a first-order Random Coefficient Autoregressive (RCA) Model. This algorithm turns out to be very reliable in estimating the true parameter values of a given model. It combines quasi-maximum likelihood method, the Kalman filter algorithm, and the Powell's method. Simulation results demonstrate that the algorithm is viable and promising.

Keywords:

• Brent's method
• Kalman Filter
• Maximum Likelihood
• Powell's method
• Random Coefficient Autoregressive (RCA) Models
• Simultaneous Perturbation Stochastic Approximation

Mathematics Subject Classification:

• 62F10
• 62M10
• 68U20

Acknowledgments

The authors express their gratitude to the anonymous referees. Their reports and their criticisms considerably improved the presentation from an earlier version of the article.