TY  - JOUR
AB  - 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.
VL  - 18
IS  - 3
SN  - 1526-5943
DO  - 10.1108/JRF-11-2016-0145
UR  - https://doi.org/10.1108/JRF-11-2016-0145
AU  - Canitz Felix
AU  - Ballis-Papanastasiou Panagiotis
AU  - Fieberg Christian
AU  - Lopatta Kerstin
AU  - Varmaz Armin
AU  - Walker Thomas
PY  - 2017
Y1  - 2017/01/01
TI  - Estimates and inferences in accounting panel data sets: comparing approaches
T2  - The Journal of Risk Finance
PB  - Emerald Publishing Limited
SP  - 268
EP  - 283
Y2  - 2024/04/25
ER  -