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      Staatstätigkeiten, Parteien und Demokratie 

      Bildung in Deutschland im Vergleich. Drei Skizzen zur interaktionsorientierten und materiellen Policy-Analyse in Fortentwicklung des Schmidt’schen Ansatzes

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      Springer Fachmedien Wiesbaden

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          What To Do (and Not to Do) with Time-Series Cross-Section Data

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            An Introduction to Varieties of Capitalism

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              Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

              In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.
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                Book Chapter
                2013
                July 2 2013
                : 163-184
                10.1007/978-3-658-01853-5_12
                37962cfa-36ec-45bf-bdb1-5bd4d075a7aa
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