Nonparametric Scanning For Nonrandom Missing Data With Continuous Instrumental Variables

TL;DR

This paper introduces a nonparametric instrumental variable method that scans over continuous dichotomizations to efficiently estimate parameters in data with nonrandom missingness, outperforming discretization-based approaches.

Contribution

It proposes a novel nonparametric scanning approach that avoids arbitrary discretizations and establishes asymptotic properties for improved estimation.

Findings

The method achieves asymptotic normality of the estimator.

Simulation studies show improved efficiency over discretization methods.

Real data analysis confirms practical advantages.

Abstract

This article introduces a new instrumental variable approach for estimating unknown population parameters with data having nonrandom missing values. With coarse and discrete instruments, Shao and Wang (2016) proposed a semiparametric method that uses the added information to identify the tilting parameter from the missing data propensity model. A naive application of this idea to continuous instruments through arbitrary discretizations is apt to be inefficient, and maybe questionable in some settings. We propose a nonparametric method not requiring arbitrary discretizations but involves scanning over continuous dichotomizations of the instrument; and combining scan statistics to estimate the unknown parameters via weighted integration. We establish the asymptotic normality of the proposed integrated estimator and that of the underlying scan processes uniformly across the instrument sample space. Simulation studies and the analysis of a real data set demonstrate the gains of the methodology over procedures that rely either on arbitrary discretizations or moments of the instrument.