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16:00 - 17:30 27 October 2011
“Inference for best linear approximations to set identified functions”, joint work with Arun Chandrasekhar, Victor Chernozhukov, and Paul Schrimpf.
Drayton Ricardo Lecture Theatre |
30 Gordon Street | London | WC1H 0AN | United Kingdom
Francesca Molinari, Cornell University
This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to be any functions, including ones carrying an index, which can be estimated parametrically or non-parametrically. The identification region of the parameters of the best linear approximation is characterized via its support function, and limit theory is developed for the latter. It is proved that the support function approximately converges to a Gaussian process, and validity of the Bayesian bootstrap is established. The paper nests as special cases the canonical examples in the literature: mean regression with interval valued outcome data and interval valued regressor data.
Department of Economics - Reception
020 7679 5888 |
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