Jia Chen
University of York
55 Papers
161 Citations
Jia Chen is an academic researcher from University of York. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 15, co-authored 51 publications. Previous affiliations of Jia Chen include University of Queensland & Zhejiang University.
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Papers
Non-parametric time-varying coefficient panel data models with fixed effects
TL;DR: In this article, a non-parametric time-varying coefficient model with fixed effects was developed to characterize non-stationarity and trending phenomenon in a nonlinear panel data model, and two methods to estimate the trend function and the coefficient function without taking the first difference to eliminate the fixed effects were developed.
152
Semiparametric trending panel data models with cross-sectional dependence
TL;DR: In this article, a semi-parametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals.
108
Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series
TL;DR: In this paper, the authors proposed two semi-parametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto-regressive lags.
Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects
TL;DR: In this article, a semi-parametric minimum average variance estimation (SMAVE) based on a dummy variable method was proposed to obtain consistent estimators for both the parameters and the unknown link function.
A new diagnostic test for cross-section uncorrelatedness in nonparametric panel data models
TL;DR: In this paper, the authors proposed a nonparametric residual cross-section uncorrelatedness (CU) test for nonlinear multilayer data models, where the power function of the proposed test was analyzed under a sequence of local alternatives that involve a nonlinear multifactor model.