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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1080/07350015.2023.2219283

Included in

Econometrics Commons

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