Regression using GMM estimation
| Independent variable | FD GMM | SYS GMM | ||||
|---|---|---|---|---|---|---|
| ROA | Tobin’s q | ROE | ROA | Tobin’s q | ROE | |
| ROA L1 | 0.134* (0.065) | 0.111* (0.086) | ||||
| Tobin’s q L1 | −0.032* (0.083) | 0.129* (0.057) | ||||
| ROE L1 | 0.658* (0.075) | 0.819*** (0.000) | ||||
| ACSIZE | 0.073 (0.209) | −0.145 (0.327) | 0.907 (0.399) | 0.049 (0.286) | −0.121 (0.475) | 0.541 (0.533) |
| ACMEET | 0.002 (0.361) | −0.032** (0.045) | −0.035 (0.541) | 0.001 (0.683) | −0.004* (0.079) | −0.031 (0.633) |
| ACEXPERTISE | 0.179* (0.055) | 0.271** (0.029) | 0.360* (0.061) | 0.007* (0.070) | 0.215** (0.041) | 0.881* (0.094) |
| NRC | 0.030** (0.035) | 0.363*** (0.004) | 0.229** (0.022) | 0.038* (0.061) | 0.373** (0.046) | 0.105** (0.018) |
| IO | 0.041 (0.180) | 0.043 (0.686) | 0.199 (0.560) | 0.021 (0.580) | 0.034 (0.751) | 0.144 (0.693) |
| FO | 0.165* (0.057) | 0.082** (0.033) | 0.590* (0.080) | 0.151* (0.094) | 0.187* (0.061) | 0.013* (0.091) |
| ACSIZE × COV | −0.056 (0.205) | 0.092* (0.085) | −0.028 (0.235) | −0.075 (0.355) | 0.098* (0.092) | −0.032 (0.352) |
| ACMEET × COV | −0.006 (0.751) | −0.003* (0.073) | −0.007 (0.765) | −0.003 (0.855) | −0.006* (0.053) | −0.005 (0.812) |
| ACEXPERTISE × COV | 0.012* (0.076) | 0.183** (0.041) | 0.443* (0.074) | 0.024* (0.081) | 0.172** (0.021) | 0.439* (0.094) |
| NRC × COV | −0.020** (0.041) | −0.040* (0.065) | −0.080** (0.025) | −0.009* (0.061) | −0.029** (0.046) | −0.039* (0.083) |
| IO × COV | 0.040 (0.187) | 0.047 (0.657) | 0.202 (0.552) | 0.020 (0.591) | 0.038 (0.721) | 0.136 (0.707) |
| FO × COV | −0.028* (0.074) | −0.013* (0.092) | −0.178* (0.084) | −0.004* (0.091) | −0.045* (0.050) | −0.019* (0.094) |
| LEV | −0.398*** (0.005) | −0.690*** (0.006) | −0.328 (0.791) | −0.234* (0.091) | −0.652** (0.011) | −0.263 (0.747) |
| GROWTH | 0.045 (0.318) | 0.488*** (0.000) | 0.101 (0.578) | 0.025 (0.566) | 0.493*** (0.000) | 0.085 (0.665) |
| COV | −0.018 (0.519) | −0.175 (0.184) | −0.474* (0.085) | −0.031 (0.315) | −0.175 (0.264) | −0.303* (0.047) |
| Industry | Yes | Yes | Yes | Yes | Yes | Yes |
| Prob > chi2 | 0.000 | 0.000 | 0.009 | 0.000 | 0.000 | 0.000 |
| AR (1) | −1.7868 (0.174) | −1.172 (0.241) | 0.674 (0.501) | −1.827 (0.167) | −1.730 (0.183) | 0.582 (0.560) |
| AR (2) | −1.578 (0.157) | 1.052 (0.224) | 0.475 (0.445) | −1.678 (0.159 | 1.225 (0.255) | 0.492 (0.483) |
| Number of obs | 217 | 217 | 217 | 326 | 326 | 326 |
| Independent variable | FD GMM | SYS GMM | ||||
|---|---|---|---|---|---|---|
| ROA | Tobin’s q | ROE | ROA | Tobin’s q | ROE | |
| ROA L1 | 0.134* (0.065) | 0.111* (0.086) | ||||
| Tobin’s q L1 | −0.032* (0.083) | 0.129* (0.057) | ||||
| ROE L1 | 0.658* (0.075) | 0.819*** (0.000) | ||||
| ACSIZE | 0.073 (0.209) | −0.145 (0.327) | 0.907 (0.399) | 0.049 (0.286) | −0.121 (0.475) | 0.541 (0.533) |
| ACMEET | 0.002 (0.361) | −0.032** (0.045) | −0.035 (0.541) | 0.001 (0.683) | −0.004* (0.079) | −0.031 (0.633) |
| ACEXPERTISE | 0.179* (0.055) | 0.271** (0.029) | 0.360* (0.061) | 0.007* (0.070) | 0.215** (0.041) | 0.881* (0.094) |
| NRC | 0.030** (0.035) | 0.363*** (0.004) | 0.229** (0.022) | 0.038* (0.061) | 0.373** (0.046) | 0.105** (0.018) |
| IO | 0.041 (0.180) | 0.043 (0.686) | 0.199 (0.560) | 0.021 (0.580) | 0.034 (0.751) | 0.144 (0.693) |
| FO | 0.165* (0.057) | 0.082** (0.033) | 0.590* (0.080) | 0.151* (0.094) | 0.187* (0.061) | 0.013* (0.091) |
| ACSIZE × COV | −0.056 (0.205) | 0.092* (0.085) | −0.028 (0.235) | −0.075 (0.355) | 0.098* (0.092) | −0.032 (0.352) |
| ACMEET × COV | −0.006 (0.751) | −0.003* (0.073) | −0.007 (0.765) | −0.003 (0.855) | −0.006* (0.053) | −0.005 (0.812) |
| ACEXPERTISE × COV | 0.012* (0.076) | 0.183** (0.041) | 0.443* (0.074) | 0.024* (0.081) | 0.172** (0.021) | 0.439* (0.094) |
| NRC × COV | −0.020** (0.041) | −0.040* (0.065) | −0.080** (0.025) | −0.009* (0.061) | −0.029** (0.046) | −0.039* (0.083) |
| IO × COV | 0.040 (0.187) | 0.047 (0.657) | 0.202 (0.552) | 0.020 (0.591) | 0.038 (0.721) | 0.136 (0.707) |
| FO × COV | −0.028* (0.074) | −0.013* (0.092) | −0.178* (0.084) | −0.004* (0.091) | −0.045* (0.050) | −0.019* (0.094) |
| LEV | −0.398*** (0.005) | −0.690*** (0.006) | −0.328 (0.791) | −0.234* (0.091) | −0.652** (0.011) | −0.263 (0.747) |
| GROWTH | 0.045 (0.318) | 0.488*** (0.000) | 0.101 (0.578) | 0.025 (0.566) | 0.493*** (0.000) | 0.085 (0.665) |
| COV | −0.018 (0.519) | −0.175 (0.184) | −0.474* (0.085) | −0.031 (0.315) | −0.175 (0.264) | −0.303* (0.047) |
| Industry | Yes | Yes | Yes | Yes | Yes | Yes |
| Prob > chi2 | 0.000 | 0.000 | 0.009 | 0.000 | 0.000 | 0.000 |
| AR (1) | −1.7868 (0.174) | −1.172 (0.241) | 0.674 (0.501) | −1.827 (0.167) | −1.730 (0.183) | 0.582 (0.560) |
| AR (2) | −1.578 (0.157) | 1.052 (0.224) | 0.475 (0.445) | −1.678 (0.159 | 1.225 (0.255) | 0.492 (0.483) |
| Number of obs | 217 | 217 | 217 | 326 | 326 | 326 |
Note(s): ***p < 0.01, **p < 0.05, *p < 0.1 The two-step GMM technique uses robust standard errors to control for heteroscedasticity and autocorrelation
Source(s): The data was processed by the STATA application, and the table were created by the author