Instrumental variable (IV) methods are widely used to identify causal effects in models with endogenous explanatory variables. Often the instrument exclusion restriction that underlies the validity of the usual IV inference is suspect; that is, instruments are only plausibly exogenous. We...

We develop a Bayesian semi-parametric approach to the instrumental variable problem. We assume linear structural and reduced form equations, but model the error distributions non-parametrically. A Dirichlet process prior is used for the joint distribution of structural and instrumental variable...

This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing t and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed...

This paper presents results from a Monte Carlo study concerning inference with spatially dependent data. It investigates the impact of location/distance measurement errors upon the accuracy of parametric and nonparametric estimators of asymptotic variances.

This paper examines whether spillovers from local market human capital are important in explaining the distribution of productivity across Malaysia. We develop an empirical method for describing local human capital distributions based on the idea that spillovers are limited in scope by costs of...

In this paper we develop new estimation methods for identifying causal effects based on the idea that the amount of selection on the observed explanatory variables in a model provides a guide to the amount of selection on the unobservables. Our approach involves the use of factor model as a way...