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Multiplicative Measurement Error and the Simulation Extrapolation Method

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Author Info
Elena Biewen ()
Sandra Nolte ()
Martin Rosemann ()

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Abstract

Whereas the literature on additive measurement error has known a considerable treatment, less work has been done for multiplicative noise. In this paper we concentrate on multiplicative measurement error in the covariates, which contrary to additive error not only modi es proportionally the original value, but also conserves the structural zeros. This paper compares three variants to specify the multiplicative measurement error model in the simulation step of the Simulation-Extrapolation (SIMEX) method originally proposed by Cook and Stefanski (1994): i) as an additive one without using a logarithmic transformation, ii) as the well-known logarithmic transformation of the multiplicative error model, and iii) as an approach using the multiplicative measurement error model as such. The aim of the paper is to analyze how well these three approaches reduce the bias caused by the multiplicative measurement error. We apply three variants to the case of data masking by multiplicative measurement error, in order to obtain parameter estimates of the true data generating process. We produce Monte Carlo evidence on how the reduction of data quality can be minimized.

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Publisher Info
Paper provided by Institut für Angewandte Wirtschaftsforschung (IAW) in its series IAW Discussion Papers with number 39.

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Length: 24 pages
Date of creation: Jan 2008
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Handle: RePEc:iaw:iawdip:39

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Related research
Keywords: Errors-in-variables in nonlinear models; disclosure limitation methods; multiplicative error;

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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  1. Lin, An-loh, 1989. "Estimation of multiplicative measurement-error models and some simulation results," Economics Letters, Elsevier, vol. 31(1), pages 13-20. [Downloadable!] (restricted)
  2. Stephen J. Iturria & Raymond J. Carroll & David Firth, 1999. "Polynomial Regression and Estimating Functions in the Presence of Multiplicative Measurement Error," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 547-561. [Downloadable!] (restricted)
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