Estimates and inferences in accounting panel data sets: comparing approaches

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.