Estimates and inferences in accounting panel data sets: comparing approaches

Felix Canitz (Carl von Ossietzky Universitat Oldenburg, Oldenburg, Germany)
Panagiotis Ballis-Papanastasiou (Department of Finance, Universitat Bremen Fachbereich 07 Wirtschaftswissenschaft, Bremen, Germany)
Christian Fieberg (Bremer Landesbank, Bremen, Germany)
Kerstin Lopatta (Department of Business Administration, Economics and Law, University of Oldenburg, Oldenburg, Germany)
Armin Varmaz (School of International Business, Bremen, Germany)
Thomas Walker (Concordia University, Montreal, Canada)

Journal of Risk Finance

ISSN: 1526-5943

Publication date: 15 May 2017

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.

Keywords

Citation

Canitz, F., Ballis-Papanastasiou, P., Fieberg, C., Lopatta, K., Varmaz, A. and Walker, T. (2017), "Estimates and inferences in accounting panel data sets: comparing approaches", Journal of Risk Finance, Vol. 18 No. 3, pp. 268-283. https://doi.org/10.1108/JRF-11-2016-0145

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Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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