
handle: 10419/22431
Der Beitrag gibt einen Überblick über Methoden zur Paneldatenanalyse, wobei neuere Methoden im Vordergrund stehen. Insbesondere werden lineare Mehrebenenmodelle unter Berücksichtigung einer neuen Variante didkutiert. Außerdem ist die Analyse auf nichtlineare, nicht- und semiparametrische Verfahren ausgerichtet. Im Gegensatz zu linearen Modellen existiert bei nichtlinearen Ansätzen keine einheitliche Schätzstrategie. Im Falle von Fixed-Effects-Modellen dominiert die bedingte ML-Methode. Unter den Annahmen eines Random-Effects-Ansatzes ist es oft möglich, die ML-Methode direkt zu nutzen. Alternativen bilden GMM-Schätzer und simulierte Schätzer. Wenn die nichtlineare Funktion nicht genau bekannt ist, sind nicht- oder semiparametrische Schätzer zu präferieren.
This paper presents a survey on panel data methods in which I emphasize new developements. In particular, linear multilevel models with a new variant are discussed. Furthermore, non-linear, nonparametric and semiparametric models are analyzed. In contrast to linear models there do not exist unified methods for nonlinear approaches. In this case FEM are dominated by CML methods. Under REM assumptions it is often possible to use the ML method directly. As alternatives GMM and simulated estimators exist. If the nonlinear function is not exactly known, nonparametric or semiparametric methods should be preferred.
ddc:330, linear multilevel, panel data, linear multilevel, nonlinear, non- and semiparametric models, C24, nonlinear, C25, C14, non- and semiparametric models, C23, panel Data, jel: jel:C23, jel: jel:C24, jel: jel:C14, jel: jel:C25
ddc:330, linear multilevel, panel data, linear multilevel, nonlinear, non- and semiparametric models, C24, nonlinear, C25, C14, non- and semiparametric models, C23, panel Data, jel: jel:C23, jel: jel:C24, jel: jel:C14, jel: jel:C25
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