We develop an “omitted variable bias” framework for sensitivity analysis of instrumental variable (IV) estimates that is immune to “weak instruments,” naturally handles multiple “side-effects” (violations of the exclusion restriction assumption) and “confounders” (violations of...

This paper studies panel data models with interactive fixed effects where the regressors are allowed to be correlated with the idiosyncratic error terms. We propose a two-step profile GMM estimation procedure to estimate the parameters of interest. In the first step we obtain a preliminary...

Instrumental variables (IV) are often used to provide exogenous variation in the impulse response analysis but the heterogeneous effects the IV may identify are rarely discussed. In microeconometrics, on the other hand, it is well understood that an IV identifies the local average treatment...

In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), several of which are based on Abadie (2003)'s kappa theorem. Our framework presumes a binary endogenous explanatory variable ("treatment") and a binary...

Nonparametric identification strategy is employed to capture causal relationships without imposing any variant of monotonicity existing in the nonseparable nonlinear error model literature. This is important as when monotonicity is applied to the instrumental variables it limits their...

This paper discusses the empirical content of the exogeneity (zero-covariance) assumption, the key assumption for identification in the linear IV model. Contrary to the general belief, we show that whenever the outcome is bounded, the exogeneity assumption imposes some testable restrictions on...

In this paper, we revisit the issue of excess sensitivity of consumption to income and address the weak instrument problem that is well documented in this literature. Using quarterly data for the U.S. economy, we first highlight the weak instrument problem by showing that the use of conventional...

This paper studies the averaging generalized method of moments (GMM) estimator that combines a conservative GMM estimator based on valid moment conditions and an aggressive GMM estimator based on both valid and possibly misspecified moment conditions, where the weight is the sample analog of an...

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the...

The mean squared prediction error of the linear regression model is examined when estimation is performed with instrumental variables. It is shown that increasing the number of instruments in the estimation procedure, can reduce the mean squared prediction error of the model through more...