Estimates and inferences in accounting panel data sets : comparing approaches

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Felix Canitz
  • Panagiotis Ballis-Papanastasiou
  • Christian Fieberg
  • Armin Varmaz
  • Thomas Walker

Detail(s)

Original languageEnglish
Pages (from-to)268-283
Journal / PublicationJournal of Risk Finance
Volume18
Issue number3
Online published15 May 2017
Publication statusPublished - 2017
Externally publishedYes

Abstract

Purpose - The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.
Design/methodology/approach - The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.
Findings - The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.
Originality/value - The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

Research Area(s)

  • Cointegration, Nonstationarity, Regression estimates, Regression inferences, Stationarity

Citation Format(s)

Estimates and inferences in accounting panel data sets: comparing approaches. / Canitz, Felix; Ballis-Papanastasiou, Panagiotis; Fieberg, Christian et al.
In: Journal of Risk Finance, Vol. 18, No. 3, 2017, p. 268-283.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review