Xiaohong Chen

This paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions.

This paper characterises the semiparametric efficiency bound for a class of semiparametric models in which the unknown nuisance functions are identifi…ed via nonparametric conditional moment restrictions with possibly non-nested or over-lapping conditioning sets, and the …finite dimensional parameters are potentially over-identi…fied via unconditional moment restrictions involving the nuisance functions.

This paper presents estimates of key preference parameters of the Epstein and Zin (1989, 1991) and Weil (1989) (EZW) recursive utility model, evaluates the model’s ability to …to fit asset return data relative to other asset pricing models, and investigates the implications of such estimates for the unobservable aggregate wealth return.

We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of √ n− consistent estimators whose cardinality increases with sample size.

The goal of this paper is to develop techniques to simplify semiparametric inference.

This paper studies nonparametric estimation of conditional moment restrictions in which the generalized residual functions can be nonsmooth in the unknown functions of endogenous variables.

In this paper we clarify the relations between the existing sets of regularity conditions for convergence rates of nonparametric indirect regression (NPIR) and nonparametric instrumental variables (NPIV) regression models.

This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models.

This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (Θ) and unknown functions (h)of endogenous variables.

This paper considers identification and estimation of a nonparametric regression model with an unobserved discrete covariate.

Estimation of local, quantile-specific copula-based time series models offers some salient advantages over classical global parametric approaches.