System Estimation of Panel Data Models Under Long-Range Dependence
研究了一个包含个体和交互固定效应的动态面板数据模型，允许创新项存在长程依赖，无需预先进行单位根检验，估计方法基于条件平方和准则，并通过模拟和债务与GDP关系的实证验证了可靠性。
A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic trends can be featured. Estimations are performed using conditional-sum-of-squares criteria based on projected series by which latent characteristics are proxied. Resulting estimates are consistent and asymptotically normal at standard parametric rates. A simulation study provides reliability on the estimation method. The method is then applied to the long-run relationship between debt and GDP. Supplementary materials for this article are available online.