To overcome the generated regressor problem, I perform bootstrapping to correct the standard errors by using the following code:
Code:
xtprobit y1 x1 x2 x3, vce(bs)
Code:
xtprobit AcquirerStatus LeverageDeficit Size Markettobook Profitability, vce(cluster CompanyNo) Calculating robust standard errors: Random-effects probit regression Number of obs = 2,547 Group variable: CompanyNo Number of groups = 248 Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 10.3 max = 23 Integration method: mvaghermite Integration pts. = 12 Wald chi2(4) = 67.27 Log pseudolikelihood = -668.40718 Prob > chi2 = 0.0000 (Std. Err. adjusted for 248 clusters in CompanyNo) --------------------------------------------------------------------------------- | Robust AcquirerStatus | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------+---------------------------------------------------------------- LeverageDeficit | -.4867099 .2280932 -2.13 0.033 -.9337643 -.0396555 Size | .2556389 .091086 2.81 0.005 .0771137 .4341641 Markettobook | -.0000113 1.82e-06 -6.20 0.000 -.0000148 -7.70e-06 Profitability | .14997 .3930542 0.38 0.703 -.6204021 .9203421 _cons | -3.788979 .7845155 -4.83 0.000 -5.326601 -2.251357 ----------------+---------------------------------------------------------------- /lnsig2u | -1.676469 .3295443 -2.322364 -1.030574 ----------------+---------------------------------------------------------------- sigma_u | .4324734 .0712596 .3131159 .5973292 rho | .1575636 .0437428 .0892877 .2629729 ---------------------------------------------------------------------------------
Code:
. xtprobit AcquirerStatus LeverageDeficit Size Markettobook Profitability, vce(bs) Random-effects probit regression Number of obs = 2,547 Group variable: CompanyNo Number of groups = 248 Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 10.3 max = 23 Integration method: mvaghermite Integration pts. = 12 Wald chi2(4) = 22.16 Log likelihood = -668.40718 Prob > chi2 = 0.0002 (Replications based on 248 clusters in CompanyNo) --------------------------------------------------------------------------------- | Observed Bootstrap Normal-based AcquirerStatus | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------+---------------------------------------------------------------- LeverageDeficit | -.4867099 .2300963 -2.12 0.034 -.9376904 -.0357294 Size | .2556389 .0833188 3.07 0.002 .0923371 .4189407 Markettobook | -.0000113 .086467 -0.00 1.000 -.1694835 .169461 Profitability | .14997 .3159822 0.47 0.635 -.4693438 .7692838 _cons | -3.788979 .7239035 -5.23 0.000 -5.207804 -2.370154 ----------------+---------------------------------------------------------------- /lnsig2u | -1.676469 .3515036 -2.365403 -.9875343 ----------------+---------------------------------------------------------------- sigma_u | .4324734 .076008 .3064497 .6103229 rho | .1575636 .0466577 .0858492 .2713994 --------------------------------------------------------------------------------- LR test of rho=0: chibar2(01) = 22.76 Prob >= chibar2 = 0.000
Thanks for the comments in advance!
0 Response to Bootstrapping standard errors moves the p-value of a variable from 0.000 to 1.000
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