Abstract
The paper explores the effect of multiplicative measurement errors on the estimation of a multiple linear panel data model. The conventional fixed effects estimator of the slope parameter vector,which ignores measurement errors, is biased. By correcting for the bias one can construct a consistent and asymptotically normal estimator. In addition, we find a consistent estimate of the asymptotic covariance matrix of this estimator. Measurement errors are sometimes deliberately added to the data in order to minimize their disclosure risk, and then it is often multiplicative errors that are used instead of the more conventional additive errors. Multiplicative errors can be analyzed in a similar way as additive errors, but with some important and consequential differences.
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Acknowledgments
Financial support by Bundesministerium für Bildung und Forschung (project ”Wirtschaftsstatistische Paneldaten und faktische Anonymisierung”) is gratefully acknowledged.
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Schneeweiß, H., Ronning, G. (2010). Multiple Linear Panel Regression with Multiplicative Random Noise. In: Kneib, T., Tutz, G. (eds) Statistical Modelling and Regression Structures. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2413-1_21
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DOI: https://doi.org/10.1007/978-3-7908-2413-1_21
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