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Reduced-form models evaluate endogenous variables in terms of observable exogenous variables and serve to identify relationships between the variables. Structural models are derived from theory and often include unobservable parameters that help describe behavior at a deep level.

Structural equation modeling can be defined as a class of methodologies that seeks to represent hypotheses about the means, variances, and covariances of observed data in terms of a smaller number of 'structural' parameters defined by a hypothesized underlying conceptual or theoretical model.

Structural Equation Modeling (SEM) represents a series of cause-effect relationships between variables combined into composite testable models (Shipley, 2000). It is extensively used by researchers in different disciplines and is a technique often used in psychology.

Structural equation models often contain postulated causal connections among some latent variables (variables thought to exist but which can't be directly observed). Additional causal connections link those latent variables to observed variables whose values appear in a data set.

Steps of an SEM Model Specification – defines hypothetical relationships. Model Identification. ... Parameter Estimation – comparing actual and estimated covariance (i.e., maximum likelihood estimate) Model Evaluation- goodness of fit (i.e., Chi-square, Akaike Information Criterion (AIC), Comparative Fit Index (CFI))

Structural forms or equations are based on some underlying economic model. The reduced form of a set of structural equations, on the other hand, is the form produced by solving for each dependent variable such that the resulting equations express the endogenous variables as functions of the exogenous variables.

At a broad level a structural economic model is one where the structure of decision making is fully incorporated in the specification of the model. By identifying the 'deep' parameters that describe the preferences and constraints of the decision-making process, structural models deliver counterfactual predictions.