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...

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...

In a seminal paper Nagar (1959) obtained moment approximations for the k-class of estimators in a general static simultaneous equation model under the assumption that the structural disturbances were i.i.d and normally distributed. While it has been known for some time that the normality assumption can be relaxed when obtaining the bias approximation, it has not been demonstrated that the secon...

In randomised controlled trials that have non-compliance with the treatment assigned, policy makers require unbiased estimates of the causal effect of the treatment received. Instrumental variable (IV) approaches provide complier average causal effects (CACE) estimates. Common IV methods such as two-stage least squares (2SLS) have not been extended to settings with multivariate outcomes. We pro...

Applied researchers often confront two issues when using the fixed effect-two-stage least squares (FE-2SLS) estimator for panel data models. One is that it may lose its consistency due to too many instruments. The other gain of FE-2SLS not exceed loss endogeneity weak. In this paper, an L2Boosting regularization procedure models proposed tackle instruments issue. We then construct a Stein-like ...

We propose two simple bias reduction procedures that apply to estimators in a general static simultaneous equation model. Standard jackknife estimators, as applied to 2SLS, may not reduce the bias of the exogenous variable coefficient estimators since the estimator biases are not monotonically non-increasing with sample size (a necessary condition for successful bias reduction) and they have mo...