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Structural Equation Modeling
Kuzminskaya, Kseniya ; Pešta, Michal (advisor) ; Lachout, Petr (referee)
Structural Equation Models (SEM) - also called Simultaneous Equation Models - are used to describe relationships among a set of variables. Similarly as in multivariate regression models, some of the variables are treated as predictors and the others as outcomes. However, unlike in a classical regression model, a variable, which is outcome in one equation, can become a predictor in another equation. SEM are even able to handle variables, which are not measured directly but only through their effects. They are often used in econometrics or socio-economics.
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Structural Equation Models with Application in Social Sciences
Veselý, Václav ; Pešta, Michal (advisor) ; Maciak, Matúš (referee)
We investigate possible usage of Errors-in-Variables estimator (EIV), when esti- mating structural equations models (SEM). Structural equations modelling pro- vides framework for analysing complex relations among set of random variables where for example the response variable in one equation plays role of the predic- tor in another equation. First an overview of SEM and some common covariance based estimators is provided. Special case of linear regression model is investi- gated, showing that the covariance based estimators yield the same results as ordinary least squares. A compact review of EIV models follows, Errors-in-Variables models are re- gression models where not only response but also predictors are assumed to be measured with an error. Main contribution of this paper then lies in defining modifications of the EIV estimator to fit in the SEM framework. General opti- mization problem to estimate the parameters of structural equations model with errors-in-variables si postulated. Several modifications of two stage least squares are also proposed for future research. Equation-wise Errors-in-Variables estimator is proposed to estimate the coeffi- cients of structural equations model. The coefficients of every structural equation are estimated separately using EIV estimator. Some theoretical conditions...
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Structural Equation Modeling
Kuzminskaya, Kseniya ; Pešta, Michal (advisor) ; Lachout, Petr (referee)
Structural Equation Models (SEM) - also called Simultaneous Equation Models - are used to describe relationships among a set of variables. Similarly as in multivariate regression models, some of the variables are treated as predictors and the others as outcomes. However, unlike in a classical regression model, a variable, which is outcome in one equation, can become a predictor in another equation. SEM are even able to handle variables, which are not measured directly but only through their effects. They are often used in econometrics or socio-economics.
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