Identification and Estimation of Regression Models with Misclassification

By Aprajit Mahajan. 2006

162 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6433de958db53873dde8a7127822911175adacdb

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Estimation of Average Treatment Effects With Misclassification (2007, background): “Mahajan (2005) obtains identification of h∗(x,t) assuming that E(Y | Z,V,T∗) =”
On the estimation of treatment effects with endogenous misreporting (2016, unknown): “…studies on misclassified binary regressors are mostly concerned with exogenous or random misreporting (Aigner, 1973; Brachet, 2008; Lewbel, 2007; Mahajan, 2006; Frazis and Loewenstein, 2003), where it is commonly assumed that misclassification probabilities depend only on the true treatment…”
Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information (2012, background): “See Newey (2001), Mahajan (2006), Hu and Schennach (2008), and Carroll, Chen, and Hu (2010a), among others, for other examples of the use of sieve maximum likelihood in measurement error models and Schennach (2009) and Carroll, Chen, and Hu (2010b) for further details, and an extensive discussion…”
Using mixtures in econometric models: a brief review and some new results (2016, background): “Further references in the misclassification literature include Mahajan (2006), Molinari (2008), Chen et al. (2011) and Hausman (2001).”
Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution (2008, unknown): “This assumption generalizes Assumption 5 in Mahajan (2006) with choices of a user-specified function $ð Þ.”
In a Small Moment: Class Size and Moral Hazard in the Italian Mezzogiorno (2016, unknown): “Similar results for the consequences of classification error under the same assumptions appear in Kane et al. (1999), Mahajan (2006), and Lewbel (2007), among others, though our work focuses on the consequences for the coefficient on a variable subject to error rather than implications for other…”
Instrumental Variable Treatment of Nonclassical Measurement Error Models (2008, background): “…discretely distributed regressors, instrumental variable estimators that are robust to the presence of such “nonclassical” measurement error have been developed for binary regressors (Mahajan (2006), 1S. M. Schennach acknowledges support from the National Science Foundation via Grant SES-0452089.”
Bias From Censored Regressors (2009, methodology): “Various semiparametric estimation methods for missing data and for nonstandard measurement error have been studied in recent work, some of which can be applied to situations with censored regressors (see, e.g., Ai 1997; Horowitz and Manski 1998, 2000; Black, Berger, and Scott 2000; Liang et al. 2004”