A New Algorithm for the Computation of the Covariance Matrix Implied by a Structural Recursive Model using the Finite Iterative Method
M'Barek Iaousse, Amal Hmimou, Zouhair Elhadri, Mohammed Yousfi El Kettani
Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
14 December 2019 |
29 December 2019 |
10 March 2020 |
Abstract: Structural Equation Modeling (SEM) is a set of statistical techniques that assesses a hypothesized causal model by showing whether or not, it fits the available data (Bagozzi and Yi, 2012). One of the major steps in SEM is the computation of the covariance matrix implied by the specified model (Jöreskog et al, 2016). This matrix is crucial in estimating the parameters, testing the validity of the model and, make useful interpretations. Two major methods are used for this purpose: the Jöreskog's formula (Jöreskog, 1970) and the finite iterative method (Elhadri and Hanafi, 2015; Elhadri et al, 2019). In this communication, we present a new algorithm based on the finite iterative method for the computation of this matrix. The proposed algorithm aims to make the computation more simplistic and the assumptions less restrictive. To illustrate the proposed method, an illustrative example is presented. Furthermore, theoretical and numerical comparisons between the exposed methods with the proposed algorithm are discussed and illustrated through a Python program.
