We review developments in conducting inference for model parameters in the presence of intertemporal and spatial dependence with an emphasis on panel data applications. We review the use of heteroscedasticity and autocorrelation consistent (HAC) standard error estimators, which include the...

We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include...

Instrumental variables (IVs) are widely used to identify effects in models with potentially endogenous explanatory variables. In many cases, the instrument exclusion restriction that underlies the validity of the usual IV inference holds only approximately; that is, the instruments are...

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 and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and...