In this paper, we extend Bai and Perron’s (1998, Econometrica, p.47-78) framework for multiple break testing to linear models estimated via Two Stage Least Squares (2SLS). Within our framework, the break points are estimated simultaneously with the regression parameters via minimization of the residual sum of squares on the second step of the 2SLS estimation. We establish the consistency of the...

The two-stage least-squares inferential statistics (2SLS-IS) scripts in R provided here help to discriminate the quality of results in regression models, improving on prototypes by Dow (2007) and Eff and Dow (2009) that implemented a new two-stage least squares (2SLS) standard for regression models with missing data imputation and controls for autocorrelation. Major 2SLS-IS modeling improvement...

In a seminal paper Nagar (1959) obtained …rst and second 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.Later Mikhail (1972) obtained a higher-order bias approximation for 2SLS under the same assumptions as Nagar while Iglesias and Phillips (2010) obtain...

In Mendelian randomization (MR) studies, where genetic variants are used as proxy measures for an exposure trait of interest, obtaining adequate statistical power is frequently a concern due to the small amount of variation in a phenotypic trait that is typically explained by genetic variants. A range of power estimates based on simulations and specific parameters for two-stage least squares (2...

The two-stage least-squares (2SLS) estimator is known to be biased when its first-stage fit is poor. I show that better first-stage prediction can alleviate this bias. In a two-stage linear regression model with Normal noise, I consider shrinkage in the estimation of the first-stage instrumental variable coefficients. For at least four instrumental variables and a single endogenous regressor, I...

We examine how corporate innovations influence firms’ industrial diversification decisions and how innovation-related diversification affects firm value. Our fixed effect, 2SLS and GMM models consistently show that corporate innovations lead to greater extent of diversification. The 2SLS model uses the staggered implementation of U.S. state-level R&D tax credits as an instrumental variable for ...

This paper considers the linear model with endogenous regressors and multiple changes in the parameters at unknown times. It is shown that minimization of a Generalized Method of Moments criterion yields inconsistent estimators of the break fractions, but minimization of the Two Stage Least Squares (2SLS) criterion yields consistent estimators of these parameters. We develop a methodology for e...

We explore the use of instrumental variables (IV) analysis with a multi-site randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, as assumption known in the instrumental variables literature as the exclusion restriction. We use a random-coefficient...

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...