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Estimates and inferences in accounting panel data sets: comparing approaches

Estimates and inferences in accounting panel data sets: comparing approaches <jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>The authors conducted Monte Carlo simulations according to Baltagi <jats:italic>et al.</jats:italic> (2011), Petersen (2009) and Gow <jats:italic>et al.</jats:italic> (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>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 <jats:italic>t</jats:italic>-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>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.</jats:p> </jats:sec> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Risk Finance CrossRef

Estimates and inferences in accounting panel data sets: comparing approaches

The Journal of Risk Finance , Volume 18 (3): 268-283 – May 15, 2017

Estimates and inferences in accounting panel data sets: comparing approaches


Abstract

<jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>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.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>The authors conducted Monte Carlo simulations according to Baltagi <jats:italic>et al.</jats:italic> (2011), Petersen (2009) and Gow <jats:italic>et al.</jats:italic> (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>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 <jats:italic>t</jats:italic>-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>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.</jats:p>
</jats:sec>

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/lp/crossref/estimates-and-inferences-in-accounting-panel-data-sets-comparing-frvV1py8Pl
Publisher
CrossRef
ISSN
1526-5943
DOI
10.1108/jrf-11-2016-0145
Publisher site
See Article on Publisher Site

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>The authors conducted Monte Carlo simulations according to Baltagi <jats:italic>et al.</jats:italic> (2011), Petersen (2009) and Gow <jats:italic>et al.</jats:italic> (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>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 <jats:italic>t</jats:italic>-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>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.</jats:p> </jats:sec>

Journal

The Journal of Risk FinanceCrossRef

Published: May 15, 2017

References