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TOPIC:
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DETECTING IDENTIFICATION FAILURE IN MOMENT CONDITION MODELS
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ABSTRACT
This paper develops an approach to detect identification failures in a large class of moment condition models. This is achieved by introducing a quasi-Jacobian matrix which is asymptotically singular under higher-order local identification as well as weak/set identification; in these settings, standard asymptotics are not valid. Under (semi)-strong identification, where standard asymptotics are valid, this matrix is asymptotically equivalent to the usual Jacobian matrix. After re-scaling, it is thus asymptotically non-singular. Together, these results imply that the eigenvalues of the quasi-Jacobian can detect potential local and global identification failures. Furthermore, the quasi-Jacobian is informative about the span of the identification failure. This information permits two-step identification robust subvector inference without any a priori knowledge of the underlying identification structure. Monte-Carlo simulations and empirical applications illustrate the results.
Keywords: GMM, robust subvector inference, higher-order/weak/set identification.
JEL Codes: C11, C12, C13, C32, C36.
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Click here to view the CV.
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PRESENTER
Jean-Jacques Forneron
Boston University
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RESEARCH FIELDS
Econometrics
Macroeconometrics
Industrial Organization
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DATE:
25 October 2019 (Friday)
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TIME:
4pm - 5.30pm
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VENUE:
Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903
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