We consider an approximate posterior approach to making joint probabilistic inference on the action and the associated risk in data mining. The posterior probability is based on a profile empirical likelihood, which imposes a moment restriction relating the action to the resulting risk, but does...

This paper presents a study of the large-sample behavior of the posterior distribution of a structural parameter which is partially identified by moment inequalities. The posterior density is derived based on the limited information likelihood. The posterior distribution converges to zero...

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which is based on the projection of the data matrix onto a given linear space before performing the principal component analysis. When it applies to high-dimensional factor analysis, the projection removes...

Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with no large parameters and very many small...

Inference on partially identified models plays an important role in econometrics. This paper proposes novel Bayesian procedures for these models when the identified set is closed and convex and so is completely characterized by its support function. We shed new light on the connection between...

Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with no large parameters and very many small...

It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas. One of the key features of our model is that we specify...

We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a large number of additional time-varying confounding variables. We allow the number of these additional confounding...