Timothy B. Armstrong
On the Asymptotic Distribution of Variance Weighted KS Statistics.
Thu, 02/02/2012 - 15:01 — SomNambulAuthors: Timothy B. Armstrong
Subjects: Methodology
AbstractThis paper derives the asymptotic distribution of variance weighted
Kolmogorov-Smirnov statistics for conditional moment inequality models for the
case of a one dimensional covariate. The asymptotic distribution depends on the
data generating process only through the variance of a single random variable,
leading to critical values that can be calculated analytically. By arguments in
Armstrong (2011b), the resulting tests achieve the best minimax rate for local
alternatives out of available approaches in a broad class of settings.Weighted KS Statistics for Inference on Conditional Moment Inequalities.
Tue, 12/06/2011 - 15:01 — SomNambulAuthors: Timothy B. Armstrong
Subjects: Applications
AbstractThis paper proposes confidence regions for the identified set in conditional
moment inequality models using Kolmogorov-Smirnov statistics with a truncated
inverse variance weighting with increasing truncation points. The new weighting
differs from those proposed in the literature in two important ways. First,
confidence regions based on KS tests with the weighting function I propose
converge to the identified set at a faster rate than existing procedures based
on bounded weight functions in a broad class of models.- Read more
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Asymptotically Exact Inference in Conditional Moment Inequality Models.
Tue, 12/06/2011 - 15:01 — SomNambulAuthors: Timothy B. Armstrong
Subjects: Applications
AbstractThis paper derives the rate of convergence and asymptotic distribution for a
class of Kolmogorov-Smirnov style test statistics for conditional moment
inequality models for parameters on the boundary of the identified set under
general conditions. In contrast to other moment inequality settings, the rate
of convergence is faster than root-$n$, and the asymptotic distribution depends
entirely on nonbinding moments. The results require the development of new
techniques that draw a connection between moment selection, irregular
identification, bandwidth selection and nonstandard M-estimation.
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