Identification in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of non- and semi- parametric identification in such models are re-examined. Nonparametric ...
The multivariate errors-in-variables regression model is applicable when both dependent and independent variables in a multivariate regression are subject to measurement errors. In such a scenario it is long established that the traditional least squares approach to estimating the model parameters is ...
Local polynomial regressionErrors-in-variablesVarying coefficient models inherit the simplicity and easy interpretation of classical linear models while enjoying the flexibility of nonparametric models. They are very useful in analyzing the relation between a response and a set of predictors. There has ...
TheGeneralIVRegressionModel CheckingInstrumentValidity Application WhereDoValidInstrumentsComeFrom? Threeimportantthreatstointernalvalidityare: omittedvariablebiasfromavariablethatiscorrelatedwithXbutisunobserved,socannotbeincludedintheregression; simultaneouscausalitybias(XcausesY,YcausesX); ...
Summary Errors-in-variables regression is important in many areas of science and social science, e.g. in economics where it is often a feature of hedonic models, in environmental science where air quality indices are measured with error, in biology where the vegetative mass of plants is frequen...
WaveletsThis paper studies the strong consistency of some estimators for an errors-in-variables regression model. We first provide an extension of Meister's theorem. Then, the same problem is dealt with under the Fourier-oscillating noises. Finally, we prove two strong consistency theorems for ...
This paper studies the expectile regression with error-in-variables to reduce the data error and describe the overall data distribution. Specifically, the asymptotic normality of the proposed estimator is thoroughly investigated, and an IRWLS algorithm based on orthogonal distance expectile regression (ODE...
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(1994), `Parameter Estimation in Regression Model with Errors in the Variable and Autocorrolated Disturbances', Journal of Econometrics, Vol. 64, pp. 145-63.Dagenais, M. (1994), \Parameter Estimation in Regression Models With Errors in Variables and Auto- correlated Disturbances." Journal of...
Orthogonal regression is one of the standard linear regression methods to correct for the effects of measurement error in predictors. We argue that orthogonal regression is often misused in errors-in-variables linear regression because of a failure to account for equation errors. The typical result ...