This book focuses on statistical methods for discriminating between competing models for
the long-run behavior of economic time series. Traditional methods that are used in this
context are sensitive to outliers in the data. Therefore, this book considers alternative
methods that take into account the possibility that not all observations are generated by
the postulated model. These methods are called outlier robust. The basic principle
underlying outlier robust methods is that discordant observations are downweighted
automatically. The use of weights has important consequences for the statistical
properties of the methods discussed. These consequences are studied by means of asymptotic
theory, Monte-Carlo simulations, and empirical illustrations. Based on the results of this
study, it is argued that outlier robust methods provide useful tools for applied
researchers as the methods disclose valuable additional information about the long-run
behavior of economic processes.
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