A Localised Neural network with Dependent Data: Estimation and Inference

Jiti Gao, Bin Peng () and Yayi Yan ()

No 15/23, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and crosssectional dependence. In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions. We further demonstrate the usefulness of these theoretical results by explicitly investigating a panel data model with interactive effects which nests many traditional panel data models as special cases. Finally, we show the superiority of our approach over several natural competitors using extensive numerical studies.

Keywords: dependent wild bootstrap; edgeworth expansion; fund performance evaluation (search for similar items in EconPapers)
JEL-codes: C14 C32 E44 (search for similar items in EconPapers)
Pages: 73
Date: 2023
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