Inference on conditional moment restriction models with generated variables

Ryo Kimoto, Taisuke Otsu

Research output: Contribution to journalArticlepeer-review

Abstract

A seminal work by Domínguez and Lobato (2004) proposed a consistent estimation method for conditional moment restrictions, which does not rely on additional identification assumptions as in the GMM estimator using unconditional moments and is free from any user-chosen number. Their methodology is further extended by Domínguez and Lobato (2015, 2020) for consistent specification testing of conditional moment restrictions, which may involve generated variables. We follow up this literature and derive the asymptotic distribution of Domínguez and Lobato's (2004) estimator that involves generated variables. Our simulation result illustrates that ignoring proxy errors in the generated variables may cause severer distortions for the coverage or size properties of statistical inference on parameters.

Original languageEnglish
Article number110454
JournalEconomics Letters
Volume215
DOIs
Publication statusPublished - 2022 Jun

Keywords

  • Conditional moment restriction
  • Generated variable
  • GMM

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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