IV-GLSInstrumental variables and generalized least squares
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Repeat the Hausman-Taylor test, replacing random effect with IV-GLS estimates.
as an instrument, then perform a Durbin-Wu-Hausman test comparing the IV-GLS estimates to this estimate.
The specification tests support the choice of IV-GLS as the preferred model for our study (see Table 4).
Recall that the instruments in our IV-GLS specification include all of the time-varying right-hand-side variables measured in deviations from the mean plus age mother died and age father died.
The other form of overidentification test suggested by Hausman and Taylor reveals that the null hypothesis of consistency of the IV-GLS cannot be rejected (p = 0.17).
Finally, the Durbin-Wu-Hausman test compares the IV-GLS estimates with and without expectations measured in deviations from the mean as instruments.
Before focusing on the preferred specification (IV-GLS), we note how the parameter estimates change across specifications; these changes reinforce the rationale for preferring IV-GLS (see Table 5).
Second, the IV-GLS estimator should be more efficient than the fixed effects estimator.
Fourth, comparing the preferred IV-GLS estimates to the OLS and random effects estimates, most of the coefficients are similar, with the exception of the expectation variables.
Marital status, age, education, and the number of children are all statistically significant in the preferred IV-GLS specification.