Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models

Tanya P. Garcia, Yanyuan Ma

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance–covariance. We construct root-n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or model error heteroskedasticity. Instead, we form the estimator under possibly incorrect working distribution models for the model error, error-prone covariate, or both. Empirical results demonstrate robustness to different incorrect working models in homoscedastic and heteroskedastic models with error-prone covariates.

Original languageEnglish (US)
Pages (from-to)194-206
Number of pages13
JournalJournal of Econometrics
Volume200
Issue number2
DOIs
StatePublished - Oct 2017

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Fingerprint Dive into the research topics of 'Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models'. Together they form a unique fingerprint.

Cite this