TwoStage Least Squares (2SLS) and Structural （二级最小二乘(2 sls)和结构）.pdf

Two-Stage Least Squares (2SLS) and Structural Equation Models (SEM) by Eddie Oczkowski .au/~eoczkows/home.htm May 2003 These notes describe the 2SLS estimator for latent variable models developed by Bollen (1996). The technique separately estimates the measurement model and structural model of SEM. One can therefore use it either as a stand alone procedure for a full SEM or combine it with factor analysis, for example, establish the measurement model using factor analysis and then employ 2SLS for the structural model only. The advantages of using 2SLS over the more conventional maximum likelihood (ML) method for SEM include: • It does not require any distributional assumptions for RHS independent variables, they can be non-normal, binary, etc. • In the context of a multi-equation non-recursive SEM it isolates specification errors to single equations, see Bollen (2001). • It is computationally simple and does not require the use of numerical optimisation algorithms. • It easily caters for non-linear and interactions effects, see Bollen and Paxton (1998). • It permits the routine use of often ignored diagnostic testing procedures for problems such as heteroscedasticity and specification error, see Pesaran and Taylor (1999). • Simulation evidence from econometrics suggests that 2SLS may perform better in small samples than ML, see Bollen (1996, pp120-121). There are however some disadvantages in using 2SLS compared to ML, these include: • The ML estimator is more efficient than 2SLS given its simultaneous estimation of all rela