This paper shows that polynomial sieve estimators can predict arbitrary continuous functions on closed and bounded subsets of the reals. These predictions can be arbitrarily close irrespective of whether the sieve is estimated on the full domain or a strict and non-dense subset of the domain....

The paper presents conditions for non-parametric identification of a joint distribution when the marginal distributions are identified and the joint distribution is only identified for a subset of the domain. This result is applied to sealed bid auctions where bidders face competing auctions for...

This paper considers estimation of the treatment effect where the researcher has data from a large number of related experiments. The paper considers two cases. In the first, there are a large number of treated units. The paper shows that standard analogue estimate of higher moments of the...

The paper presents sharp bounds on the identified set for classical factor models and non-parametric topic models based on results from the non-negative factorization literature. It compares the standard assumption (for factor models) of orthonormality of the factors (principal components...

The paper generalizes ideas presented on bounds of treatment effects (Manski (1990)) to bounding the joint distribution of treatment outcomes. Frechet-Hoeffding bounds are sharp for the distribution of treatment outcomes from unconfounded data and Manski bounds are sharp for the distribution of...

It is not possible to directly observe the distribution of individual treatment effects. However, this paper presents new results showing that the distribution may be inferred with data usually available from randomized control trials. Developing upon recent results presented in the computer...