Spatial Unit Roots and Spurious Regression
提出空间单位根模型，发现强空间依赖会导致伪回归，即使使用聚类标准误或空间HAC修正也无法避免，并开发了检验空间单位根的方法，以Chetty等人（2014）的回归为例说明问题与对策。
This paper proposes a model for, and investigates the consequences of, strong spatial dependence in economic variables. Our findings echo those of the corresponding “unit root” time series literature: Spatial unit root processes induce spuriously significant regression results, even with clustered standard errors or spatial HAC corrections. We develop large‐sample valid unit root and stationarity tests that can detect such strong spatial dependence. Finally, we use simulations to study strategies for valid inference in regressions with persistent spatial data, such as spatial analogues of first‐differencing transformations. Regressions from Chetty, Hendren, Kline, and Saez (2014) are used to illustrate the issues and methods.