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• W2117323258 abstract "The literature on nonclassical measurement error traditionally relies on the availability an auxiliary dataset containing correctly measured observations to achieve consistent estimation. Recently, methods that rely instead on instruments have been developed to handle misclassification errors in discrete covariates. To complement these new methods, we establish that the availability of instruments also enables the identification of a large class of nonclassical nonlinear errors-in-variables models with continuously distributed variables. Our main identifying assumption is that, conditional on the value of the true regressors, some “measure of location” of the distribution of the measurement error (e.g. its mean, mode or median) is equal to zero. The instruments must satisfy the intuitive requirement that they provide no more information regarding the variables than the true regressors do. The proposed approach relies on the eigenvalue-eigenfunction decomposition of an integral operator associated with specific joint probability densities. The main identifying assumption is used to order the eigenfunctions so that the decomposition is unique. We propose a sieve-based estimator that is relatively simple to implement. The asymptotic properties of this estimator are derived and its finite-sample behavior is investigated through Monte Carlo simulations. An example of application to the relationship between earnings and divorce rates is also provided." @default.
• W2117323258 created "2016-06-24" @default.
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• W2117323258 date "2006-09-11" @default.
• W2117323258 modified "2023-09-26" @default.
• W2117323258 title "Identification and estimation of nonclassical nonlinear errors-in-variables models with continuous distributions using instruments" @default.
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• W2117323258 doi "https://doi.org/10.1920/wp.cem.2006.1706" @default.
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