Oliver Linton

TL;DR: In this article, a method of constructing efficient semiparametric instrumental variable estimators is proposed, which involves the combination of a large number of possibly inefficient estimators rather than combining the instruments into an optimal instrument function, and the consistency and asymptotic normality is established for a class of estimators that are linear combinations of a set of consistent estimators whose cardinality increases with sample size.

TL;DR: In this article, the authors estimate a multiplicative correlation structure in the large-dimensional case, based on the estimation of a multi-dimensional correlation structure, in the Large Dimensional Case.

TL;DR: In this article, a weighted local polynomial regression smoother for nonparametric regression with conditional heteroskedasticity is proposed, which has lower asymptotic variance than the usual unweighted procedures.

TL;DR: In this paper, an estimation method based on local linear kernel smoother is proposed to estimate the single-index model without under-smoothing, which is asymptotically normal and most efficient in the semi-parametric sense.

TL;DR: In this article, the authors proposed a Kronecker product model for covariance matrices in the large dimensional case and established rates of convergence and central limit theorems (CLT) for their estimators.