Frontier estimation in the presence of measurement error with unknown variance

DOI10.1016/j.jeconom.2014.09.012zbMath1331.62478OpenAlexW1972238797MaRDI QIDQ2343753

Alois Kneip, Léopold Simar, Ingrid Van Keilegom

Publication date: 6 May 2015

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://lirias.kuleuven.be/handle/123456789/569712

zbMATH Keywords

deconvolutionnonparametric estimationpenalized likelihoodstochastic frontier estimation

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Statistical methods; economic indices and measures (91B82)

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Asymptotic and finite sample properties of Hill-type estimators in the presence of errors in observations, On relaxing the distributional assumption of stochastic frontier models, Unobserved heterogeneity and endogeneity in nonparametric frontier estimation, Statistical inference on restricted linear regression models with partial distortion measurement errors, Robust estimation in stochastic frontier models, Bounded rationality and thick frontiers in stochastic frontier analysis, Endpoint estimation for observations with normal measurement errors, Nonparametric estimation of the determinants of inefficiency in the presence of firm heterogeneity, A specification test for the composed error term in the stochastic frontier model, Consistency of the Hill Estimator for Time Series Observed with Measurement Errors, Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs, Nonparametric estimation of stochastic frontier models with weak separability, Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis, Estimation of the Boundary of a Variable Observed With Symmetric Error, Estimating stochastic production frontiers: a one-stage multivariate semiparametric Bayesian concave regression method, ROBUSTIFIED EXPECTED MAXIMUM PRODUCTION FRONTIERS

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