Publication Type
Journal Article
Version
submittedVersion
Publication Date
7-2023
Abstract
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.
Keywords
coupling, series estimation, spatial dependence, uniform confidence band
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Business and Economic Statistics
First Page
1
Last Page
11
ISSN
0162-1459
Identifier
10.1080/07350015.2023.2219283
Publisher
Taylor & Francis
Citation
LI, Jia; LIAO, Zhipeng; and ZHOU, Wenyu.
Uniform nonparametric inference for spatially dependent panel data. (2023). Journal of Business and Economic Statistics. 1-11.
Available at: https://ink.library.smu.edu.sg/soe_research/2557
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07350015.2023.2219283