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.

本文提出一种适用于存在一般未知形式时空相依性的面板数据（panel-data）场景下的非参数回归（nonparametric regressions）一致泛函推断方法。该方法仅需较长的时间跨度，未对截面维度或空间相关强度施加任何约束条件。我们通过一套面向时空相依面板数据的新型高维高斯耦合（Gaussian coupling）理论，论证了该一致推断方法的合理性。我们将该方法应用于两类实证场景：其一为借助分布混合假说（mixture of distribution hypothesis）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；其二为检验基于调查的预测（survey-based forecasts）的合理性，在此场景中我们记录了职业预测者群体中存在信息刚性（information rigidity）的非参数证据，为宏观经济学（macroeconomics）领域的粘性信息与噪声信息模型提供了全新的支撑依据。