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.
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.
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.
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.
This research was conducted while Christian Fieberg was visiting scientist at the Concordia University (Montreal) and lecturer at the FOM Hochschule. The authors wish to thank anonymous referees for helpful comments.
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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