Cross-sectional averaging and instrumental variable estimation with many weak instruments

Department of Economics Cross - sectional Averaging and Instrumental Variable Estimation

Instrumental Variables Estimation with Many Weak Instruments Using Regularized Jive

We consider instrumental variables regression in a setting where the number of instruments is large and the first stage prediction signal is not necessarily sparse. In particular, we work with models where the number of available instruments may be larger than the sample size and consistent model selection in the first stage may not be possible. Such a situation may arise when there are many we...

Instrumental Variable Estimation and Selection with Many Weak and Irrelevant Instruments

This paper proposes a new two stage least squares (2SLS) estimator which is consistent and asymptotically normal in the presence of many weak and irrelevant instruments and heteroskedasticity. In the first stage the estimator uses an adaptive absolute shrinkage and selection operator (LASSO) that selects the relevant instruments with high probability. However, the adaptive LASSO estimates have ...

Shrinkage Priors for Linear Instrumental Variable Models with Many Instruments

This paper addresses the weak instruments problem in linear instrumental variable models from a Bayesian perspective. The new approach has two components. First, a novel predictor-dependent shrinkage prior is developed for the many instruments setting. The prior is constructed based on a factor model decomposition of the matrix of observed instruments, allowing many instruments to be incorporat...

Testing with Many Weak Instruments

This paper establishes the asymptotic distributions of the likelihood ratio (LR), Anderson-Rubin (AR), and Lagrange multiplier (LM) test statistics under “many weak IV asymptotics.” These asymptotics are relevant when the number of IVs is large and the coefficients on the IVs are relatively small. The asymptotic results hold under the null and under suitable alternatives. Hence, power compariso...