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The robust variance estimator for two-stage models AgEcon
Hardin, James W..
This article discusses estimates of variance for two-stage models. We present the sandwich estimate of variance as an alternative to the Murphy–Topel estimate. The sandwich estimator has a simple formula that is similar to the formula for the Murphy–Topel estimator, and the two estimators are asymptotically equal when the assumed model distributions are true. The advantages of the sandwich estimate of variance are that it may be calculated for the complete parameter vector, and that it requires estimating equations instead of fully specified log likelihoods.
Tipo: Journal Article Palavras-chave: Robust variance estimator; Murphy–Topel estimator; Two-stage estimation; Estimating equation; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/116000
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The simulation extrapolation method for fitting generalized linear models with additive measurement error AgEcon
Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J..
We discuss and illustrate the method of simulation extrapolation for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). As in Hardin, Schmiediche, and Carroll (2003), our discussion includes specified measurement error and measurement error estimated by replicate error-prone proxies. In addition, we discuss and illustrate three extrapolant functions.
Tipo: Journal Article Palavras-chave: Simulation extrapolation; Measurement error; Instrumental variables; Replicate measures; Generalized linear models; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116184
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Instrumental variables, bootstrapping, and generalized linear models AgEcon
Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J..
This paper discusses and illustrates the qvf command for fitting generalized linear models. The differences between this new command and Stata’s glm command are highlighted. One of the most notable features of the qvf command is its ability to include instrumental variables. This functionality was added specifically to address measurement error but may be utilized by the user for other purposes. The qvf command was developed in the C-language using Stata’s new plugin features and executes much faster than the glm ado-file.
Tipo: Journal Article Palavras-chave: Measurement error; Instrumental variables; Murphy–Topel; Bootstrap; Generalized linear models; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116178
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Variance estimation for the instrumental variables approach to measurement error in generalized linear models AgEcon
Hardin, James W.; Carroll, Raymond J..
This paper derives and gives explicit formulas for a derived sandwich variance estimate. This variance estimate is appropriate for generalized linear additive measurement error models fitted using instrumental variables. We also generalize the known results for linear regression. As such, this article explains the theoretical justification for the sandwich estimate of variance utilized in the software for measurement error developed under the Small Business Innovation Research Grant (SBIR) by StataCorp. The results admit estimation of variance matrices for measurement error models where there is an instrument for the unknown covariate.
Tipo: Journal Article Palavras-chave: Sandwich estimate of variance; Measurement error; White's estimator; Robust variance; Generalized linear models; Instrumental variables; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116177
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The regression-calibration method for fitting generalized linear models with additive measurement error AgEcon
Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J..
This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on software developed as part of a small business innovation research (SBIR) grant from the National Institutes of Health (NIH).
Tipo: Journal Article Palavras-chave: Regression calibration; Measurement error; Instrumental variables; Replicate measures; Generalized linear models; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116180
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Measurement error, GLMs, and notational conventions AgEcon
Hardin, James W.; Carroll, Raymond J..
This paper introduces additive measurement error in a generalized linear-model context. We discuss the types of measurement error along with their effects on fitted models. In addition, we present the notational conventions to be used in this and the accompanying papers.
Tipo: Journal Article Palavras-chave: Generalized linear models; Transportability; Measurement error; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116175
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