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Citation link: https://media.suub.uni-bremen.de/handle/elib/5283

Publisher DOI: https://doi.org/10.1108/JRF-11-2016-0145
 
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Estimates and inferences in accounting panel data sets: comparing approaches


Other Titles: Schätzungen und Schlussfolgerungen in Rechnungslegungs-Paneldatensätzen: Vergleich der Ansätze
Authors: Canitz, Felix 
Ballis-Papanastasiou, Panagiotis  
Fieberg, Christian  
Lopatta, Kerstin  
Varmaz, Armin  
Walker, Thomas 
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: Cointegration; Stationarity; Nonstationarity; Regression estimates; Regression inferences
Issue Date: 20-Feb-2017
Journal/Edited collection: The Journal of Risk Finance 
Start page: 268
End page: 283
Note: 3
Band: 18
Type: Artikel/Aufsatz
ISSN: 1526-5943
Institution: Hochschule Bremen 
Faculty: Hochschule Bremen / Fakultät Wirtschaftswissenschaften 
Institute: School of International Business (SiB) 
Appears in Collections:Bibliographie HS Bremen

  

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