ASYMPTOTIC F-TEST IN A GMM FRAMEWORK WITH CROSS-SECTIONAL DEPENDENCE

• 成果类型: Article
• 署名作者: Sun, Yixiao; Kim, Min Seong
• 署名单位: University of California System; University of California San Diego; Toronto Metropolitan University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00441
• 发表日期: 2015-03
• 页码: 210-223
• 关键词: hac estimation; heteroskedasticity; series

摘要

The paper develops an asymptotically valid F-test that is robust to spatial autocorrelation in a GMM framework. The validity of the F-test is established under mild conditions that can accommodate a wide range of spatial processes. The proposed F-test is very easy to implement, as critical values are from a standard F-distribution. The F-test achieves triple robustness: it is asymptotically valid regardless of the spatial autocorrelation, the sampling region, and the limiting behavior of the smoothing parameter. Simulation also shows that the F-test has good size and power properties in finite samples.