Estimates and inferences in accounting panel data sets: comparing approaches

Felix Canitz, Panagiotis Ballis-Papanastasiou, Christian Fieberg, Kerstin Lopatta, Armin Varmaz and Thomas Walker

Journal of Risk Finance, 2017, vol. 18, issue 3, 268-283

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 Baltagiet al.(2011), Petersen (2009) and Gowet 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–MacBetht-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: Cointegration; Stationarity; Nonstationarity; Regression estimates; Regression inferences (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:jrfpps:jrf-11-2016-0145

DOI: 10.1108/JRF-11-2016-0145

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