The purpose of this essay is to discuss two approaches to inference, and how "human capital" can provide a way to combine them. The first approach, ubiquitous in economics, is based upon the Rubin/Holland potential outcomes model and relies upon randomized treatment to measure the causal effect...

Making inferences about aggregate business cycles from regional variation alone is difficult because of economic channels and shocks that differ between regional and aggregate economies. However, we argue that regional business cycles contain valuable information that can help discipline models...

We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication...

In this paper, we show how to estimate the asymptotic (conditional) covariance matrix, which appears in central limit theorems in high-frequency estimation of asset return volatility. We provide a recipe for the estimation of this matrix by subsampling; an approach that computes rescaled copies...

This paper develops extremum estimation and inference results for nonlinear models with very general forms of potential identification failure when the source of this identification failure is known. We examine models that may have a general deficient rank Jacobian in certain parts of the...

Calculating the welfare implications of changes to economic policy or shocks requires economists to decide on a normative criterion. One approach is to elicit the relevant moral criteria from real-world policy choices, converting a normative decision into a positive inference, as in the recent...

Solutions to the parameter estimation problem of the multivariate Pareto distribution of Asimit et al. (2010) are developed and exemplified numerically. Namely, a density of the aforementioned multivariate Pareto distribution with respect to a dominating measure, rather than the corresponding...

We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we derive and tabulate...