Computation of the Covariance Matrix Implied by a Structural Recursive Model with Latent Variables through the Finite Iterative Method

2019 ◽  
Author(s):  
Zouhair El hadri ◽  
Pasquale Dolce ◽  
Mohamed Hanafi ◽  
Yousfi Elkettani ◽  
Mrbarek Iaousse
Keyword(s):  
Iterative Method ◽  
Covariance Matrix ◽  
Latent Variables ◽  
Recursive Model ◽  
Finite Iterative Method

scholarly journals A Modified Algorithm for the Computation of the Covariance Matrix Implied by a Structural Recursive Model with Latent Variables Using the Finite Iterative Method

2020 ◽  
Vol 8 (2) ◽  
pp. 359-373 ◽  
Author(s):  
M'barek Iaousse ◽  
Amal Hmimou ◽  
Zouhair El Hadri ◽  
Yousfi El Kettani
Keyword(s):  
Structural Equation Modeling ◽  
Iterative Method ◽  
Covariance Matrix ◽  
Latent Variables ◽  
Structural Equation ◽  
Causal Model ◽  
Statistical Technique ◽  
Equation Modeling ◽  
Modified Algorithm ◽  
Finite Iterative Method

Structural Equation Modeling (SEM) is a statistical technique that assesses a hypothesized causal model byshowing whether or not, it fits the available data. One of the major steps in SEM is the computation of the covariance matrix implied by the specified model. This matrix is crucial in estimating the parameters, testing the validity of the model and, make useful interpretations. In the present paper, two methods used for this purpose are presented: the J¨oreskog’s formula and the finite iterative method. These methods are characterized by the manner of the computation and based on some apriori assumptions. To make the computation more simplistic and the assumptions less restrictive, a new algorithm for the computation of the implied covariance matrix is introduced. It consists of a modification of the finite iterative method. An illustrative example of the proposed method is presented. Furthermore, theoretical and numerical comparisons between the exposed methods with the proposed algorithm are discussed and illustrated

Finite iterative method for solving coupled Sylvester-transpose matrix equations

2014 ◽  
Vol 46 (1-2) ◽  
pp. 351-372 ◽  
Author(s):  
Caiqin Song ◽  
Jun-e Feng ◽  
Xiaodong Wang ◽  
Jianli Zhao
Keyword(s):  
Iterative Method ◽  
Matrix Equations ◽  
Finite Iterative Method

scholarly journals Avaliação da Curva de Juros Empregando Extensões do Modelo de Diebold & Li com Três Fatores

2015 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Alberto Ronchi Neto ◽  
Osvaldo Candido
Keyword(s):  
Stochastic Volatility ◽  
Covariance Matrix ◽  
Latent Variables ◽  
Cholesky Decomposition ◽  
Space Representation ◽  
Interest Rate Parity ◽  
State Space Representation ◽  
Out Of Sample ◽  
Rate Parity ◽  
The One

This paper evaluates methods that employ Kalman Filter to estimate Diebold and Li (2006) extensions in a state-space representation, applying the Nelson and Siegel (1987) function as measure equation and different specifications for the transition equation that determines level, slope and curvature dynamics. The models that were analyzed have the following structures in transition equation: (1) AR(1) specification, employing a diagonal covariance matrix for the residuals; (2) VAR(1) specification, employing a covariance matrix calculated with Cholesky decomposition; (3) VAR(1) extension, inserting variables related to the Covered Interest Rate Parity (CIRP); (4) VAR(1) extension, including stochastic volatility components. The major findings of this paper were: (1) evaluating the latent variables dynamics, the curvature was the factor that fitted better to the stochastic volatility component; (2) in a broad sense, even though the simplest VAR(1) model was the one that provided the best out-of-sample performance in the most part of maturities and forecasting horizons, the extension inserting variables related to the CIRP was able to overcome the former specification in some of these simulations.

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