• Publications
  • Influence
Double/Debiased Machine Learning for Treatment and Structural Parameters
We revisit the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0. We depart from the classical setting byExpand
Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
We develop results for the use of LASSO and Post-LASSO methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p,Expand
An MCMC Approach to Classical Estimation
This paper studies computationally and theoretically attractive estimators referred here as to the Laplace type estimators (LTE). The LTE include means and quantiles of Quasi-posterior distributionsExpand
An IV Model of Quantile Treatment Effects
The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications.Expand
Instrumental quantile regression inference for structural and treatment effect models
We introduce a class of instrumental quantile regression methods for heterogeneous treatment effect models and simultaneous equations models with nonadditive errors and offer computable methods forExpand
Inference on Treatment Effects after Selection Amongst High-Dimensional Controls
TLDR
This work develops a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the "post-double-selection" method, which resolves the problem of uniform inference after model selection for a large, interesting class of models. Expand
BAYESIAN ECONOMETRICS
Suppose a data vector X = (X1, ..., Xn) follows a distribution with a density function pn(x|θ) which is fully characterized by some parameter vector θ = (θ1, ..., θd)′. Suppose that the prior beliefExpand
Estimation and Confidence Regions for Parameter Sets in Econometric Models
This paper develops a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities andExpand
L1-Penalized Quantile Regression in High Dimensional Sparse Models
We consider median regression and, more generally, quantile regression in high-dimensional sparse models. In these models the overall number of regressors p is very large, possibly larger than theExpand
SUPPLEMENT TO \GAUSSIAN APPROXIMATIONS AND MULTIPLIER BOOTSTRAP FOR MAXIMA OF SUMS OF HIGH-DIMENSIONAL RANDOM VECTORS"
We derive a Gaussian approximation result for the maximum of a sum of high-dimensional random vectors. Specifically, we establish conditions under which the distribution of the maximum isExpand
...
1
2
3
4
5
...