Abstract
Abstract
The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.