When stochastic errors are added to data from a distribution with a sharp boundary, such as a changepoint or a frontier, nonparametric estimation of the boundary can be interpreted as a problem of deconvolution. We argue that, rather than attempting to estimate the distribution of the...

This study focuses on the semiparametric efficient estimation of random effect panel models containing AR(1) disturbances. We also consider such estimators when the effects and regressors are correlated.

This paper discusses statistical procedures for testing various restrictions in the context of nonparametric models of technical efficiency. In particular, tests for whether inputs or outputs are irrelevant, as well as tests of whether inputs or outputs may be aggregated are formulated.

The economic literature proposes several nonparametric frontier estimators based on the idea of enveloping the data (FDH and DEA-type estimators). Many have claimed that FDH and DEA techniques are non-statistical, as opposed to econometric approaches where particular parametric expressions are...

This paper proposes a general methodology for bootstrapping in frontier models, extending the more restrictive method proposed in Simar and Wilson (1998a) by allowing for heterogeneity in the structure of efficiency. A numerical illustration with real data is provided to illstrate the methodology.

Several recent papers in the American Economic Review examined important questions regarding productivity growth and its sources in industrialized countries. We examine two sets of issues raised by these papers, and reassess what can be learned about productivity, efficiency, and technology from...

This paper further examines the bootstrap method proposed by Simar and Wilson (1998) for DEA efficiency estimators. Some simplifications are provided, and we provide Monte Carlo evidence on the coverage probabilities of confidence intervals estimated by the method.

This paper further examines the bootstrap method proposed by Simar and Wilson (1998) for DEA efficiency estimators. Some simplifications are provided, and we provide Monte Carlo evidence on the coverage probabilities of confidence intervals estimated by the method.