OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
This paper analyzes a model in which an outcome equals a frontier function of inputs minus a nonnegative unobserved deviation. We allow the deviation’s distribution to depend on inputs, thereby allowing for endogeneity. If zero lies in the support of the deviation given inputs—an assumption we term assignment at the frontier—then the frontier is identified by the supremum of the outcome at those inputs, obviating the need for instrumental variables. We then estimate the frontier, allowing for random error whose distribution may also depend on inputs. Finally, we derive a lower bound on the mean deviation, using only variance and skewness, that is robust to a scarcity of data near the frontier. We apply our methods to estimate a firm-level frontier production function and inefficiency.
