In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross-sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by...

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...

This paper decomposes the causal effect of government defense spending into: (i) a local (or direct) effect, and (ii) a spillover (or indirect) effect. Using state-level defense spending data, we show that a negative cross-state spillover effect explains the existing simultaneous findings of a...

Many studies of historical persistence find that modern outcomes strongly reflect characteristics of the same places in the distant past. However they rely on data that often exhibit extreme spatial trends and autocorrelation, suggesting that their unusually large t-statistics may be due to...

We study entry and bidding in procurement auctions where contracts are awarded to the bid closest to a trimmed average bid. These auctions, common in public procurement, create incentives to coordinate bids to manipulate the bid distribution. We present statistical tests to detect coordinated...

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...

This paper presents a spatial econometric method for characterizing productivity comovement across sectors of the U.S. economy. Input-output relations provide an economic distance measure that is used to characterize interactions between sectors, as well as conduct estimation and inference. 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...