The Amemiya and MaCurdy (1986) model is similar to the Hausman-Taylor model. Following the development in the section Hausman-Taylor Estimation, estimation is identical up to the final 2SLS instrumental variables regression. In addition to the set of instruments used by the Hausman-Taylor estimator, use the following:
For each observation in the ith cross section, you use the data on the time-varying exogenous regressors for the entire cross section. Because of the structure of the added instruments, the Amemiya-MaCurdy estimator can be applied only to balanced data.
The Amemiya-MaCurdy model attempts to gain efficiency over Hausman-Taylor by adding instruments. This comes at a price of a more stringent assumption on the exogeneity of the variables. Although the Hausman-Taylor model requires only that the cross-sectional means of be orthogonal to , the Amemiya-MaCurdy estimation requires orthogonality at every point in time; see Baltagi (2008, sec. 7.4).
A Hausman specification test is provided to test the validity of the added assumption. Define , its Hausman-Taylor estimate as , and its Amemiya-MaCurdy estimate as . Under the null hypothesis, both estimators are consistent and is efficient. The Hausman test statistic is then
where and are variance-covariance estimates of and , respectively. Under the null hypothesis, m is distributed as with degrees of freedom equal to the rank of .