Arbeitspapier

Empirical likelihood-based inference in linear errors-in-covariables models with validation data

Linear errors-in-covariables models are considered, assuming the availability of independent validation data on the covariables in addition to primary data on the response variable and surrogate covariables. We first develop an estimated empirical log-likelihood with the help of validation data and prove that its asymptotic distribution is that of a weighted sum of independent standard x random variables with unknown weights. By estimating the unknown weights consistently, an estimated empirical likelihood confidence region on the regression parameter vector is constructed. We also suggest an adjusted empirical log-likelihood and prove that its asymptotic distribution is a standard X To avoid estimating the unknown weights or the adjustment factor, we propose a partially smoothed bootstrap empirical log- likelihood to construct a confidence region which has asymptotically correct coverage probability. A simulation study is conducted to compare the proposed methods with a normal approximation based method in term of coverage accuracy and average length of the confidence interval.

Language
Englisch

Bibliographic citation
Series: SFB 373 Discussion Paper ; No. 2001,64

Classification
Wirtschaft
Subject
Bootstrap empirical likelihood
Confidence region
Measurement error
Surrogate variables

Event
Geistige Schöpfung
(who)
Wang, Qihua
Rao, J. N. K.
Event
Veröffentlichung
(who)
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
(where)
Berlin
(when)
2001

Handle
URN
urn:nbn:de:kobv:11-10050195
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Wang, Qihua
  • Rao, J. N. K.
  • Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

Time of origin

  • 2001

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