Inference on a Distribution from Noisy Draws

TL;DR

This paper analyzes the bias introduced when estimating a distribution from noisy data, providing new bias correction methods and demonstrating their effectiveness through simulations and an empirical example.

Contribution

It introduces novel asymptotic bias calculations for empirical distributions with noise and develops correction techniques that improve inference accuracy.

Findings

Bias in empirical distribution due to noise is quantifiable and correctable.

Analytical and jackknife corrections improve confidence interval coverage.

Corrected estimators show better sampling behavior in simulations.

Abstract

We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models for panel data. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function is equally obtained. These calculations are new in the literature, where only results on smooth functionals such as the mean and variance have been derived. We provide both analytical and jackknife corrections that recenter the limit distribution and yield confidence intervals with correct coverage in large samples. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the much-improved sampling behavior of the corrected estimators. An empirical illustration on heterogeneity in deviations from the law of one price is equally provided.