Asymptotic Efficiency in Parametric Structural Models with Parameter-Dependent Support

By Keisuke Hirano, J. Porter. 2003

100 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/267eab5c8e0af678dfa8747788f9ed6051bbac1b

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In Management Science and Operations Research

Citations in the corpus

Monte Carlo Confidence Sets for Identified Sets (2016, background): “Parameter-dependent support is a feature of certain auction models (e.g., Hirano and Porter (2003), Chernozhukov and Hong (2004)) and some structural models in labor economics (e.g., Flinn and Heckman (1982)).”
Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks (2009, unknown): “For instance, in procurement auction models, the key information about structural parameters is contained in the extreme or near-extreme conditional quantiles of bids given bidder and auction characteristics (see e.g. Chernozhukov and Hong (2004) and Hirano and Porter (2003)).”
Bootstrap Methods in Econometrics (2018, unknown): “Chernozhukov and Hong (2004) and Hirano and Porter (2003) discuss some of the theoretical issues that are involved.”
Likelihood estimation and inference in threshold regression (2012, methodology): “1 of Hirano and Porter (2003), the limit likelihood ratio process is exp  D(v)  ≡ exp {−v} 1 (v ≥ −T1) for yq and is exp  D(v)  ≡ exp{v}1 (v ≤ T2) for (1 − y) q.”
Deciding Between Competition and Collusion (2003, unknown): “ci = di + dt + eci where (24) eci F (zi) (25)”
Bayesian likelihood methods for estimating the end point of a distribution (2005, background): “…variables Vi, say, are available in addition to the ‘responses’ Xi, the density f at expression (1.1) might depend on Vi. (For problems of this general type, see, for example, Donald and Paarsch (1993), Hirano and Porter (2003), Jofre-Bonet and Pesendorfer (2003) and Chernozhukov and Hong (2004).)”
Threshold Regression With a Threshold Boundary (2020, background): “Different from Chernozhukov and Hong (2003a) and Jun, Pinkse, and Wan (2015) where the original objective function is smoothed, we did not change our objective function in Algorithm M.”