A Nonparametric Poolability Test for Panel Data Models with Cross Section Dependence
提出一种基于筛估计的非参数检验，用于判断大维半参数面板数据模型是否可合并，并处理横截面依赖问题，通过蒙特卡洛模拟验证了有限样本下的良好表现。
In this article we propose a nonparametric test for poolability in large dimensional semiparametric panel data models with cross-section dependence based on the sieve estimation technique. To construct the test statistic, we only need to estimate the model under the alternative. We establish the asymptotic normal distributions of our test statistic under the null hypothesis of poolability and a sequence of local alternatives, and prove the consistency of our test. We also suggest a bootstrap method as an alternative way to obtain the critical values. A small set of Monte Carlo simulations indicate the test performs reasonably well in finite samples.