Nonstationary heterogeneous panels with multiple structural changes
研究了非平稳异质面板中斜率与误差因子载荷的多重结构变化，提出基于最小二乘的估计方法，并通过蒙特卡洛模拟和OECD国家R&D溢出效应实例验证。
Nonstationary panels have been widely used in empirical studies in macroeconomics and finance. This article considers multiple structural changes in nonstationary heterogeneous panels with common factors. Kapetanios, Pesaran, and Yamagata (Citation2011) showed that unobserved nonstationary factors can be proxied by cross-sectional averages of observable data. This means that unobserved error factors can be treated as additional regressors, and different break points in slopes and error factor loadings can be considered as multiple breaks in linear regression models with panel data. We generalize the least squares approach by Bai and Perron (Citation1998) to nonstationary panels and show that the break points in both slopes and error factor loadings can be consistently estimated for two important cases involving (i) nonstationary factors and (ii) nonstationary regressors. Monte Carlo simulations are conducted to verify the main results in finite samples. Finally, we illustrate our methods with an empirical example examining the effect of international R&D spillovers on domestic total factor productivity in OECD countries. A common break in 1992 is detected and attributed to the acceleration of globalization that began in the early 1990s.