2022
DOI: 10.1016/j.jcorpfin.2022.102166
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Nondisclosure and analyst behavior: Evidence from redaction of proprietary information from public filings

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Cited by 13 publications
(7 citation statements)
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“…Second, we employed the entropy balancing method (Hainmueller, 2012), which is commonly used to reduce the potential for self‐selection bias that can be caused by observable characteristics (Fei, 2022; Hainmueller, 2012; Treepongkaruna et al, 2022). We created binary variables with the treatment and control groups based on the top quartiles of the carbon emissions measures as the testing variables of interest.…”
Section: Resultsmentioning
confidence: 99%
“…Second, we employed the entropy balancing method (Hainmueller, 2012), which is commonly used to reduce the potential for self‐selection bias that can be caused by observable characteristics (Fei, 2022; Hainmueller, 2012; Treepongkaruna et al, 2022). We created binary variables with the treatment and control groups based on the top quartiles of the carbon emissions measures as the testing variables of interest.…”
Section: Resultsmentioning
confidence: 99%
“…Endogeneity check: We examined the potential endogeneity concern, which may be caused by the sample selection bias, by incorporating two crucial analyses, including PSM and entropy balancing methods. Initially, we used the entropy balancing approach to reduce the self-selection bias that may arise from the observable characteristics (Fei, 2022; Hainmueller, 2012; Treepongkaruna et al , 2022), which allowed us to avoid observable selection bias (Treepongkaruna et al , 2022). The entropy balancing approach creates a balanced sample that can translate into a lower approximation error and minimize model dependency in finite samples (Hainmueller, 2012).…”
Section: Empirical Results and Discussionmentioning
confidence: 99%
“…Alternatively, to address the endogeneity concern, we employ another approach using entropy balancing (Hainmueller, 2012), which creates a balanced sample that can translate into a lower approximation error and reduced model dependency in finite samples. The entropy balancing method can reduce the potential self‐selection bias that may arise from observable characteristics (Fei, 2022; Hainmueller, 2012; Treepongkaruna et al, 2022), ultimately avoiding observable selection bias (Treepongkaruna et al, 2022).…”
Section: Resultsmentioning
confidence: 99%