I am using a 2step heckman regression model and I want to evaluate that probit looks okay and that the model converges and that there are no "red" flags.
One of the estimators that I get is the inverse Mills ration (I am using R). Is this supposed to be statistically significant or not?
I am using an example from the book:
> summary( heckit( lfp ~ age + I( age^2 ) + faminc + kids + educ,
+ wage ~ exper + I( exper^2 ) + educ + city, Mroz87 ) )
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Tobit 2 model (sample selection model)
2-step Heckman / heckit estimation
753 observations (325 censored and 428 observed)
14 free parameters (df = 740)
Probit selection equation:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.156806923 1.402085958 -2.965 0.003127 **
age 0.185395096 0.065966659 2.810 0.005078 **
I(age^2) -0.002425897 0.000773540 -3.136 0.001780 **
faminc 0.000004580 0.000004206 1.089 0.276544
kidsTRUE -0.448986740 0.130911496 -3.430 0.000638 ***
educ 0.098182281 0.022984120 4.272 0.0000219 ***
Outcome equation:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9712003 2.0593505 -0.472 0.637
exper 0.0210610 0.0624646 0.337 0.736
I(exper^2) 0.0001371 0.0018782 0.073 0.942
educ 0.4170174 0.1002497 4.160 0.0000356 ***
city 0.4438379 0.3158984 1.405 0.160
Multiple R-Squared:0.1264, Adjusted R-Squared:0.116
Error terms:
Estimate Std. Error t value Pr(>|t|)
invMillsRatio -1.098 1.266 -0.867 0.386
sigma 3.200 NA NA NA
rho -0.343 NA NA NA
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