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Higher-order Expansions and Inference for Panel Data Models

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posted on 2025-04-09, 00:02 authored by Jiti Gao, Bin Peng, Yayi Yan

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.

History

Classification-JEL

C14, C32, E44

Creation date

2023-06-09

Working Paper Series Number

15/23

Length

73 pp

File-Format

application/pdf

Handle

RePEc:msh:ebswps:2023-15

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