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Efficient nonparametric three-stage estimation of fixed effects varying coefficient panel data models
Statistica Sinica ( IF 1.4 ) Pub Date : 2021-01-01 , DOI: 10.5705/ss.202018.0382
Juan M. Rodriguez-Póo , Alexandra Soberón

This paper is concerned with the estimation of a fixed effects panel data model that adopts a partially linear form, in which the coefficients of some variables are restricted to be constant but the coefficients of other variables are assumed to be varying, depending on some exogenous continuous variables. Moreover, we allow for the existence of endogeneity in the structural equation. Conditional moment restrictions on first differences are imposed to identify the structural equation. Based on these restrictions we propose a three stage estimation procedure. The asymptotic properties of these proposed estimators are established. Moreover, as a result of the first differences transformation, to estimate the unknown varying coefficient functions, two alternative backfitting estimators are obtained. As a novelty, we propose a minimum distance estimator that, combining both estimators, is more efficient and achieves the optimal rate of convergence. The feasibility and possible gains of this new procedure are shown by estimating a Life-cycle hypothesis panel data model and a Monte Carlo study is implemented.

中文翻译:

固定效应变系数面板数据模型的高效非参数三阶段估计

本文关注的是采用部分线性形式的固定效应面板数据模型的估计,其中一些变量的系数被限制为常数,而其他变量的系数被假定为变化的,这取决于一些外生连续变量。此外,我们允许结构方程中存在内生性。对一阶差分施加条件矩限制以识别结构方程。基于这些限制,我们提出了一个三阶段估计程序。建立了这些提议的估计量的渐近特性。此外,作为一阶差分变换的结果,为了估计未知的变系数函数,获得了两个备选的回装估计量。作为新品,我们提出了一个最小距离估计器,它结合了两个估计器,效率更高并达到最佳收敛速度。通过估计生命周期假设面板数据模型并实施蒙特卡罗研究,显示了这种新程序的可行性和可能的​​收益。
更新日期:2021-01-01
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