ASYMPTOTIC DISTRIBUTIONS FOR TWO ESTIMATORS OF THE SINGLE-INDEX MODEL

By Yingcun Xia. Econometric Theory, 2006

167 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ff25144b0dcf3006577ce4b2382e7ca1b6391ee6

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Citations in the corpus

A SINGLE-INDEX QUANTILE REGRESSION MODEL AND ITS ESTIMATION (2012, methodology): “(1993) and Xia (2006), the commonly used bandwidth selection methods for nonparametric regression can be employed to estimate the link function as well as the parameters.”
Sliced Regression for Dimension Reduction (2008, methodology): “Consequently, MAVE is useful for both dimension reduction (Xia et al. 2002) and semiparametric modeling (Xia 2006).”
Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects (2013, methodology): “Xia (2006) established an asymptotic theory for this approach in time series models and Xia and Härdle (2006) further extended the approach and its asymptotic theory to partially linear singleindex time series models.”
Estimation for a partial-linear single-index model (2009, methodology): “Under very general regularity conditions and for q = 1, Xia et al. (2002) proposed the minimum average variance estimation (MAVE) and Xia (2006) a refined version of MAVE, and both methods can provide √ n-consistent estimators for the singleindex β0.”
The EFM approach for single-index models (2011, methodology): “We consider the following model similar to that used in Xia (2006): E(Y |β⊤X) = g(β⊤X), g(β⊤X) = (β⊤X)2 exp(β⊤X); (3.3) Var(Y |β⊤X) = σ2, σ = 0.1.”
Functional single index models for longitudinal data (2011, methodology): “Thus, there are two distinctive features in the functional single-index model (1.2), as compared to the traditional single-index model (1.1) considered in Xia (2006) and Xia et al. (2002).”
A Lasso-Type Approach for Estimation and Variable Selection in Single Index Models (2012, methodology): “In what follows, we propose to add 1 penalty to the OPG method by Xia et al. (2002) and use the resulting estimate of θ as the initial vector.”
Semiparametric estimation of binary response models with endogenous regressors (2009, methodology): “In a recent paper, Xia (2006) shows that the computationally much simpler rMAVE procedure of Xia, Tong, Li, and Zhu (2002) achieves the same asymptotic variance as the Klein and Spady estimator when applied to a standard binary choice model without endogenous regressors.”
A Multiple-Index Model and Dimension Reduction (2008, methodology): “However, the estimation combining the two ideas is not as efficient as the method introduced in Xia et al. (2002); see the discussion in Xia (2006).”
A constructive approach to the estimation of dimension reduction directions (2007, background): “In a very special case when q = 1 and the CS is in the regression mean, Xia (2006a) proved that dMAVE is more efficient than dOPG.”