Effect of bank stress tests on IRS attention
| Variable | All relevant banks | MSE optimal | ||
|---|---|---|---|---|
| Linear | Quadratic | Linear | Quadratic | |
| IRS attention | 15.076*** | 2.068 | 11.282* | 7.695 |
| (4.038) | (5.859) | (5.881) | (11.420) | |
| [129] | [129] | [58] | [58] | |
| Controls | Y | Y | Y | Y |
| Fixed-effects | Y | Y | Y | Y |
| Variable | All relevant banks | MSE optimal | ||
|---|---|---|---|---|
| Linear | Quadratic | Linear | Quadratic | |
| IRS attention | 15.076*** | 2.068 | 11.282* | 7.695 |
| (4.038) | (5.859) | (5.881) | (11.420) | |
| [129] | [129] | [58] | [58] | |
| Controls | Y | Y | Y | Y |
| Fixed-effects | Y | Y | Y | Y |
Note(s): This table reports the regression discontinuity coefficients for IRS attention. The table reports both the local linear and quadratic regression discontinuity coefficients first by specifying a sub-sample of “All Relevant Banks,” which includes banks between $10B and $200B in total asset size, and second, by using the MSE optimal bandwidth measure. The table reports the results for the IRS attention received by banks, the measure is obtained from Bozanic et al. (2017). Regressions are triangular kernel weighted and are allowed to capture more banks in a larger bandwidth in the first two columns and are restricted to having at least 65 observations within the selected optimal bandwidth in the last two columns. Control variables include the first 12 bank characteristics shown in Table 2. All regressions include bank and year-fixed effects. Standard errors are clustered at the bank level and are reported in parentheses while observation counts are reported in brackets. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors’ own work
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