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Estimation in Binary Choice Models with Measurement Errors

Jochumzen, Peter LU (2003) In Working Papers, Department of Economics, Lund University
Abstract
In this paper we develop a simple maximum likelihood estimator for probit models where the regressors have measurement error. We first assume precise information about the reliability ratios (or, equivalently, the proxy correlations) of the regressors. We then show how reasonable bounds for the parameter estimates can be obtained when only imprecise information is available. The analysis is also extended to situations where the measurement error has non-zero mean and is correlated with the true values of the regressors. An extensive simulation study shows that the estimator works very well, even in quite small samples. Finally the method is applied to data explaining sick leave in Sweden
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Working paper/Preprint
publication status
unpublished
subject
keywords
Measurement error, errors-in-variables, probit, binary choice, bounds
in
Working Papers, Department of Economics, Lund University
issue
4
publisher
Department of Economics, Lund University
language
English
LU publication?
yes
id
3c11ab8c-bdcb-430e-beb2-c3efaccadc0e (old id 1386380)
alternative location
http://swopec.hhs.se/lunewp/abs/lunewp2003_004.htm
date added to LUP
2016-04-04 10:51:26
date last changed
2018-11-21 21:01:12
@misc{3c11ab8c-bdcb-430e-beb2-c3efaccadc0e,
  abstract     = {{In this paper we develop a simple maximum likelihood estimator for probit models where the regressors have measurement error. We first assume precise information about the reliability ratios (or, equivalently, the proxy correlations) of the regressors. We then show how reasonable bounds for the parameter estimates can be obtained when only imprecise information is available. The analysis is also extended to situations where the measurement error has non-zero mean and is correlated with the true values of the regressors. An extensive simulation study shows that the estimator works very well, even in quite small samples. Finally the method is applied to data explaining sick leave in Sweden}},
  author       = {{Jochumzen, Peter}},
  keywords     = {{Measurement error; errors-in-variables; probit; binary choice; bounds}},
  language     = {{eng}},
  note         = {{Working Paper}},
  number       = {{4}},
  publisher    = {{Department of Economics, Lund University}},
  series       = {{Working Papers, Department of Economics, Lund University}},
  title        = {{Estimation in Binary Choice Models with Measurement Errors}},
  url          = {{https://lup.lub.lu.se/search/files/5637346/2060140}},
  year         = {{2003}},
}