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.

Additional Metadata
Keywords Monte Carlo simulations, robust statistics, statistical methods, time series, unit roots
JEL Econometric and Statistical Methods: General (jel C10), Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Time-Series Models; Dynamic Quantile Regressions (jel C32)
Promotor T. Kloek (Teun)
Publisher Erasmus University Rotterdam
Sponsor Kloek, Prof. Dr. T. (promotor)
Persistent URL hdl.handle.net/1765/10454
Citation
Lucas, A. (1996, January 25). Outlier robust unit root analysis. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/10454

Additional Files
chap7.pdf Final Version , 823kb
chap8.pdf Final Version , 694kb
chap3.pdf Final Version , 406kb
chap2.pdf Final Version , 328kb
chap6.pdf Final Version , 326kb
chap5.pdf Final Version , 304kb
chap1.pdf Final Version , 210kb
biblio.pdf Final Version , 118kb
chap9.pdf Final Version , 100kb
stelnl.pdf Final Version , 57kb
steluk.pdf Final Version , 55kb
contents.pdf Final Version , 44kb
preface.pdf Final Version , 34kb