Testing for shifts in a time trend panel data model with serially correlated error component disturbances

Badi H. Baltagi, Chihwa Kao, Long Liu

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

This paper studies testing of shifts in a time trend panel data model with serially correlated error component disturbances, without any prior knowledge of whether the error term is stationary or nonstationary. This is done in case the shift is known as well as unknown. Following the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator. The proposed test has a chi-square limiting distribution and is valid for both I(0) and I(1) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations.

Original languageEnglish (US)
Pages (from-to)745-762
Number of pages18
JournalEconometric Reviews
Volume39
Issue number8
DOIs
StatePublished - Sep 13 2020

Keywords

  • Non-Stationary Panels
  • Serial Correlation
  • Time Trends
  • Wald Type Tests

ASJC Scopus subject areas

  • Economics and Econometrics

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