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.

Monte Carlo simulations, robust statistics, statistical methods, time series, unit roots
Econometric and Statistical Methods: General (jel C10), Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Time-Series Models; Dynamic Quantile Regressions (jel C32)
T. Kloek (Teun)
Erasmus University Rotterdam
Kloek, Prof. Dr. T. (promotor)
Erasmus School of Economics

Lucas, A. (1996, January 25). Outlier robust unit root analysis. Erasmus University Rotterdam. Retrieved from

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