Spurious weather effects
研究发现，由于降雨的空间相关性，使用降雨作为外生工具变量可能产生虚假关系，例如挪威某地降雨对选举投票率看似有强预测力，标准检验在99%情况下错误拒绝原假设，建议用多维多项式控制时空趋势。
Abstract Rainfall is exogenous to human actions and hence popular as an exogenous source of variation. But it is also spatially correlated. This can generate spurious relationships between rainfall and other spatially correlated outcomes. As an illustration, rainfall on almost any day of the year has seemingly high predictive power of electoral turnout in Norwegian municipalities. In Monte Carlo analyses, I find that standard tests reject true null hypotheses in as much as 99% of cases. Standard approaches to estimating consistent standard errors do not solve the problem. Instead, I suggest controlling for spatial and spatiotemporal trends using multidimensional polynomials.