Uniform Nonparametric Inference for Spatially Dependent Panel Data

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.

本文提出了一种适用于具有一般未知形式时空依赖性的面板数据（panel-data）环境下的非参数回归（nonparametric regressions）一致泛函推断方法。该方法要求具备较长的时间跨度，但未对截面维度或空间相关性强度施加任何限制。上述一致推断的合理性可通过一种面向时空相依面板的新型高维增长型高斯耦合（Gaussian coupling）理论得以证明。我们将该方法应用于两类实证场景：其一聚焦于由分布混合假说（mixture of distribution hypothesis）所刻画的资产价格波动率（asset price volatility）与交易量（trading volume）之间的非参数关系；其二用于检验基于调查的预测（survey-based forecasts）的合理性，在此场景中我们验证了专业预测者存在信息刚性（information rigidity）的非参数证据，为宏观经济学中的粘性信息（sticky-information）与噪音信息（noisy-information）模型提供了新的支撑依据。