Effciency Gain of System GMM and MDE over Individual Equation Estimation
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...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2004
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| Online Access: | https://ink.library.smu.edu.sg/soe_research/490 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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?2) estimation (MDE).JEL Classification Number: C30. [ABSTRACT FROM AUTHOR] |
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