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Set Identified Linear Models
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DOI：10.3982/ECTA7637.png)
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Abstract
En 中文
We analyze the identification and estimation of parameters beta satisfying the incomplete linear moment restrictions E(z(inverted perpendicular)(x beta-y)) = E(z(inverted perpendicular)u(z)), where z is a set of instruments and u(z) an unknown bounded scalar function. We first provide empirically relevant examples of such a setup. Second, we show that these conditions set identify beta where the identified set B is bounded and convex. We provide a sharp characterization of the identified set not only when the number of moment conditions is equal to the number of parameters of interest, but also in the case in which the number of conditions is strictly larger than the number of parameters. We derive a necessary and sufficient condition of the validity of supernumerary restrictions which generalizes the familiar Sargan condition. Third, we provide new results on the asymptotics of analog estimates constructed from the identification results. When B is a strictly convex set, we also construct a test of the null hypothesis, beta(0)is an element of B, whose size is asymptotically correct and which relies on the minimization of the support function of the set B- {beta 0}. Results of some Monte Carlo experiments are presented.
Keywords:
Partial identification
linear prediction
support function
test
Journal
IF:
7.1
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3.0K
Citations:
4.3W