Research methods

This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/bootstrap theory applies, but at the expense of throwing away data and perhaps losing effciency. An alternative is to use some sort of imputation method, but this requires additional modelling assumptions, which we would rather avoid.1 We show how the sampling theory changes and how to modify the resampling algorithms to accommodate the problem of missing data.

I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models.

We develop inference tools in a semiparametric partially linear regression model with missing response data.

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

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

In this paper we develop a revealed preference-based nonparametric approach to characteristics models.

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.

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 describes an estimator of the additive components of a nonparametric additive model with a known link function.

We show that these two types of interval estimate are different in practice, the latter in general being shorter.

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

It is shown that a general form of pessimistic portfolio optimization based on the Choquet approach may be formulated as a problem of linear quantile regression.

We examine the contribution of human capital to economy-wide technological improvements through the two channels of innovation and imitation.

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.

This paper develops and implements semiparametric methods for estimating binary response (binary choice) models withcontinuous endogenous regressors.