Nonparametric panel data regression with parametric cross-sectional dependence
针对存在截面依赖的非参数面板数据模型，提出广义最小二乘型估计量以提高效率，并通过蒙特卡洛模拟和欧洲货币联盟实证分析其表现。
Summary In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A generalised least squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterising the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analysing the implications of the European Monetary Union for its member countries.