| This paper studies a new class of semiparametric varying coefficient dynamic panel data models with a multifactor error structure.To estimate both parametric and nonparametric coefficient functions,a three-stage estimation method is proposed.In the first and second steps,we proposed a nonparametric generalized method of moments(NPGMM)is adopted to estimate the unknown coefficient,and adopted average method to obtain the estimator of parametric coefficients in the third step.Specifically,in the first step,we project the factor loads from the covariates and construct instrumental variables to deal with the model’s endogeneity problem,and then estimate the unknown coefficients of the model using the method of NPGMM.The second step is to estimate the unknown factors in the error term by using the residual obtained in the first step,and then eliminate the unknown factors from the model by projection,and then estimate the unknown coefficients in the model by using the method of NPGMM.Under appropriate conditions,the consistency and asymptotic normality of the estimators are derived.Finally,Monte Carlo simulations are conducted to verify the theoretical results and demonstrate that the proposed estimators perform well in a finite sample.This article is organized as follows.In Chapter 1,we introduce the knowledge of the partially linear varying coefficient dynamic panel data model with a multifactor error structure.The main research work of this paper,instrumental variable method and the method of generalized moment estimation are introduced.In Chapter 2,we introduce the model and estimation process.In Chapter 3,we introduce the model assumptions and give the consistency and asymptotic normality of the estimators.In Chapter 4,numerical simulation is conducted to explore the performance of the proposed estimation method under finite samples.Chapter 5 is the detailed proof of the theorems.In Chapter 6,we give the conclusion and prospect. |
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