The paper provides significant simplifications and extensions of results obtained by Gorsich, Genton, and Strang (J. Multivariate Anal. 80 (2002) 138) on the structure of spatial design matrices.

This paper proposes a new class of HAC covariance matrix estimators.

GEL methods which generalize and extend previous contributions are defined and analysed for moment condition models specified in terms of weakly dependent data.

This paper considers a linear triangular simultaneous equations model with conditional quantile restrictions.

In a number of semiparametric models, smoothing seems necessary in order to obtain estimates of the parametric component which are asymptotically normal and converge at parametric rate.

This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables.

In additive error models with a discrete endogenous variable identification cannot be achieved under a marginal covariation condition when the support of instruments is sparse relative to the support of the endogenous variable

This lecture explores conditions under which there is identification of the impact on an outcome of exogenous variation in a variable which is endogenous when data are gathered.

Recent developments in the theory of choice under uncertainty and risk yield a pessimistic decision theory that replaces the classical expected utility criterion with a Choquet expectation that accentuates the likelihood of the least favorable outcomes.

I show that a class of fixed effects estimators is reasonably robust for estimating the population-averaged slope coefficients in panel data models with individual-specific slopes, where the slopes are allowed to be correlated with the covariates.