Uniform Nonparametric Inference for Spatially Dependent Panel Data
提出一种适用于时空相依面板数据的均匀非参数推断方法，无需限制截面大小或空间相关性强度，并应用于资产价格波动与交易量的非参数关系以及调查预测理性检验。
This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a long time span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method in two empirical settings. One concerns the nonparametric relationship between asset price volatility and trading volume as depicted by the mixture of distribution hypothesis. The other pertains to testing the rationality of survey-based forecasts, in which we document nonparametric evidence for information rigidity among professional forecasters, offering new support for sticky-information and noisy-information models in macroeconomics.