Functional Data Analysis and Mixed Effect Models

Alois Kneip (), Robin Sickles and Wonho Song
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Alois Kneip: Universität Mainz, Fachbereich Rechts- und Wirtschaftswissenschaften
Wonho Song: Rice University, Department of Economics - MS 22

A chapter in COMPSTAT 2004 — Proceedings in Computational Statistics, 2004, pp 315-326 from Springer

Abstract: 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.

Keywords: Mixed effects model; functional principal component analysis; nonparametric regression (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2656-2_25

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DOI: 10.1007/978-3-7908-2656-2_25

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