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• W2122352702 abstract "This paper considers identi…cation and inference of a general latent nonlinear model using two samples, in which a covariate contains arbitrary measurement errors in both samples, and neither sample contains an accurate measurement of the corresponding true variable. The primary sample consists of some dependent variables, some errorfree covariates, and an error-ridden covariate, in which the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values. The auxiliary sample consists of another noisy measurement of the mismeasured covariate and some error-free covariates. We …rst show that a general latent nonlinear model is nonparametrically identi…ed using the two samples when both could have nonclassical errors, with no requirement of instrumental variables nor independence between the two samples. When the two samples are independent and the latent nonlinear model is parameterized, we propose sieve quasi maximum likelihood estimation (MLE) for the parameter of interest, and establish its root-n consistency and asymptotic normality under possible misspeci…cation, and its semiparametric e¢ ciency under correct speci…cation. We also provide a sieve likelihood ratio model selection test to compare two possibly misspeci…ed parametric latent models. A small Monte Carlo simulation and an empirical example are presented." @default.
• W2122352702 created "2016-06-24" @default.
• W2122352702 creator A5066350606 @default.
• W2122352702 creator A5074717637 @default.
• W2122352702 date "2006-11-01" @default.
• W2122352702 modified "2023-09-23" @default.
• W2122352702 title "Identi…cation and Inference of Nonlinear Models Using Two Samples With Arbitrary Measurement Errors" @default.
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• W2122352702 hasPublicationYear "2006" @default.
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