In the econometric literature it is known that, under certain conditions, estimating a system of equations together is more efficient than estimating each equation separately. This finding has been proved, however, only under the assumption of a known parametric form of heteroskedasticity (including homoskedasticity) or non-random regressors/instruments. This note shows that an analogous finding holds for GMM under heteroskedasticity of unknown form and random regressors/instruments. Specifically, I provide a necessary condition for the efficiency gain of the system GMM over the single-equation GMM. An analogous necessary condition for the efficiency gain is also shown to hold for minimum-distance (or X2) estimation (MDE).
|Number of pages||9|
|Journal||Japanese Economic Review|
|Publication status||Published - 2004 Dec|
ASJC Scopus subject areas
- Economics and Econometrics