Abstract
Panel studies in econometrics as well as longitudinal studies in biomedical applications provide data from a sample of individual units where each unit is observed repeatedly over time (age, etc.). In this context, mixed effect models are often applied to analyze the behavior of a response variable in dependence of a number of covariates. In some important applications it is necessary to assume that individual effects vary over time (age, etc.).
In the paper it is shown that in many situations a sensible analysis may be based on a semiparametric approach relying on tools from functional data analysis. The basic idea is that time-varying individual effects may be represented as a a sample of smooth functions which can be characterized by its Karhunen-Loève decomposition. An important application is the estimation of time-varying technical inefficiencies of individual firms in stochastic frontier analysis.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kneip, A., Sickles, R.C., Song, W. (2004). Functional Data Analysis and Mixed Effect Models. In: Antoch, J. (eds) COMPSTAT 2004 — Proceedings in Computational Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2656-2_25
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DOI: https://doi.org/10.1007/978-3-7908-2656-2_25
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1554-2
Online ISBN: 978-3-7908-2656-2
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