Among the several approaches proposed for estimating interactions in latent variable models in CBSEM, Bollen and Paxton (1998) and Little et al. (2006) describe procedures that do not require special-purpose software, and so are accessible to all CBSEM users. Bollen and Paxton described an approach using two-stage least squares (2SLS), an analytical approach used in regression to overcome estim...

This paper proposes a new two stage least squares (2SLS) estimator which is consistent and asymptotically normal in the presence of many weak instruments and heteroskedasticity. The first stage consists of two components: first, an adaptive absolute shrinkage and selection operator (LASSO) that selects the instruments and second, an OLS regression with the selected regressors. This procedure is...

A stochastic simulation procedure is proposed in this papa for obtaining median unbiased (Mu) estimates in macroeconometric models. MU estimates are computed for lagged dependent variable (LDV) coefficients in 18 equations of a macroeconometric model. The 2SLS bias for a coefficient, defined as the difference between the 2SLS estimate and the MU estimate, is on average smaller in absolute value...

This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS) estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown fu...

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. We offer a detailed analysis of the pooled two-stage least squares (pooled 2SLS) and fixed effects-2SLS (FE-2SLS) estimators and discuss complications in correcting for selection biases that arise when instruments are corr...

Partial least squares (PLS) is sometimes used as an alternative to covariance-based structural equation modeling (SEM). This paper briefly reviews currently available SEM techniques, and provides a critique of the perceived advantages of PLS over covariance-based SEM as commonly cited by PLS users. Specific attention is drawn to the primary disadvantage of PLS, namely the lack of consistency of...