Serial dependence robust bootstrap test for cross-sectional correlation
提出一种新的自助法检验，用于面板模型中的截面相关检验，能稳健处理序列依赖问题，方法简单且理论有效，模拟和实证均表现良好。
Summary We propose a novel bootstrap-based test for cross-sectional dependence (CD) in panel models, maintaining robustness against serial dependence. While serial dependence is common in panel data, existing tests often assume serial independence. Our cluster wild bootstrap CD test procedure mirrors Pesaran’s original CD test and is very simple to implement. This procedure preserves serial dependence while testing for cross-sectional independence. Theoretical validity is established for our bootstrap-based test, with simulations highlighting its performance in finite samples. Using R&D investment panel data, we illustrate the utility of our bootstrap methods.