Functional data is a common and important type in econometrics and has been easier and easier to collect in the big data era. To improve estimation accuracy and reduce forecast risks with functional data, in this paper, we propose a novel cross-validation model averaging method for generalized...

Considering partially linear single-index errors-in-variables model which can be described as Y = n(X T a) + ZT ßo + e when the Z' s are measured with additive errors. The general estimators established in literature are biased when ignoring the measurement errors. We proposed two estimators in...

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT ß + g (T) when the X's are measured with additive error. The semiparametric likelihood estimate of Severini and Staniswalis (1994) leads to biased estimates of both the parameter ß and the...

We consider the problem of estimating quantile regression coefficients in errors-in-variables models. When the error variables for both the response and the manifest variables have a joint distribution that is spherically symmetric but otherwise unknown, the regression quantile estimates based...

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT Ø + g(T) when the T's are measured with additive error. We derive an estimator of Ø by modification local-likelihood method. The resulting estimator of Ø is shown to be asymptotically...