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Is completeness necessary? Estimation in nonidentified linear models

Andrii Babii and Jean-Pierre Florens

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Abstract: This paper documents the consequences of the identification failures for a class of linear ill-posed inverse models. The Tikhonov-regularized estimator converges to a well-defined limit equal to the best approximation of the structural parameter in the orthogonal complement to the null space of the operator. We illustrate that in many cases the best approximation may coincide with the structural parameter or at least may reasonably approximate it. We characterize the nonasymptotic Hilbert space norm and the uniform norm convergence rates for the best approximation. Nonidentification has important implications for the large sample distribution of the Tikhonov-regularized estimator, and we document the transition between the Gaussian and the weighted chi-squared limits. The theoretical results are illustrated for the nonparametric IV and the functional linear IV regressions and are further supported by the Monte Carlo experiments.

Date: 2017-09, Revised 2020-04
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Working Paper: Is completeness necessary? Estimation in nonidentified linear models (2020) Downloads
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