Linear regression limit theory for nonstationary panel data

• 成果类型: Article; Proceedings Paper
• 署名作者: Phillips, PCB; Moon, HR
• 署名单位: Yale University; University of California System; University of California Santa Barbara
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/1468-0262.00070
• 发表日期: 1999
• 页码: 1057-1111
• 关键词: inference

摘要

This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section (n) and time series (T) observations. The limit theory allows for both sequential limits, wherein T --> infinity followed by n --> infinity, and joint limits where T, n --> infinity simultaneously; and the relationship between these multidimensional limits is explored. The panel structures considered allow for no time series cointegration, heterogeneous cointegration, homogeneous cointegration, and near-homogeneous cointegration. The paper explores the existence of long-run average relations between integrated panel vectors when there is no individual time series cointegration and when there is heterogeneous cointegration. These relations are parameterized in terms of the matrix regression coefficient of the long-run average covariance matrix. In the case of homogeneous and near homogeneous cointegrating panels, a panel fully modified regression estimator is developed and studied. The limit theory enables us to test hypotheses about the long run average parameters both within and between subgroups of the full population.