Journal of Business and Economic Statistics· 2012Q1
Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models
- 2,609citations
- Q1SCImago
- 2012year
Short summary
A new method uses heteroscedasticity to identify and estimate models with mismeasured or endogenous regressors, even without instrumental variables or repeated measurements.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a new identification method for models with mismeasured/endogenous regressors.
- Leverages heteroscedasticity as the source of identification.
- Applicable when instrumental variables or repeated measurements are absent.
- Provides two-stage least squares and GMM estimators.
- Derives set identification bounds and offers an empirical application.
AI-generated from the title and abstract; the full text is not read.
Abstract
This article proposes a new method of obtaining identification in mismeasured regressor models, triangular systems, and simultaneous equation systems. The method may be used in applications where other sources of identification, such as instrumental variables or repeated measurements, are not available. Associated estimators take the form of two-stage least squares or generalized method of moments. Identification comes from a heteroscedastic covariance restriction that is shown to be a feature of many models of endogeneity or mismeasurement. Identification is also obtained for semiparametric partly linear models, and associated estimators are provided. Set identification bounds are derived for cases where point-identifying assumptions fail to hold. An empirical application estimating Engel curves is provided.
The authors' abstract, as published at the source. Journal of Business and Economic Statistics, 2012 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics