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The Misuse of Regression-Based x-Scores as Dependent Variables

https://doi.org/10.2139/ssrn.3836969
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The 14 references without a DOI — listed, not checked
no DOI — not checkedref4
no DOI — not checkedDid government regulations lead to inflated credit ratings?
no DOI — not checkedIncorporating bad versus good news and the earnings-return asymmetry
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no DOI — not checkedref31
no DOI — not checkedref33
no DOI — not checkedSeparating information about cash flows from information about risk in losses
no DOI — not checkedManaging innovation: the role of collateral
no DOI — not checkedC-Score in this artificial best-case scenario perfectly captures all variation in asymmetric timeliness, and therefore it should yield unbiased and consistent estimates of the effect of X. As expected, for both the basic and the extended C-Score models, there is no discernible bias in the point estimates. However, 79-82% of these estimates differ significantly from the true value, much higher than the expected rejection rate of 5%, because of Chen et al.'s (2023) standard-error bias (which we illustrated for the parallel type-I error results). Thus, even in this best-case scenario, hypothesis tests for C-Score are distorted. In summary, the Basu model performs well across all the simulation scenarios in Figures D1 and D2. In contrast, the C-Score model has inconsistent performance. In type-I error simulations in Figure D1, C-Score analysis does not have a systematic coefficient bias, but only because the data under the null hypothesis do not contain any new variation that could confound C-Score. 47 Even with these unbiased point estimates, C-Score analysis leads to frequent false findings of a significant effect of X because of standard-error bias in two-stage estimation
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no DOI — not checkedThe geographic decentralization of audit firms and audit quality
no DOI — not checkedFinancial innovation and financial intermediation: evidence from credit default swaps
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no DOI — not checkedDiscriminatory pricing of over-the-counter derivatives
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