Many economic theories depend on the presence or absence of a unit root for their validity, and econometric and statistical theory undergo considerable changes when unit roots are present. Thus, knowledge on unit roots has become so important, necessitating an extensive, compact, and nontechnical book on this subject. This book is rested on this motivation and introduces the literature on unit roots in a comprehensive manner to both empirical and theoretical researchers in economics and other areas. By providing a clear, complete, and critical discussion of unit root literature, In Choi covers a wide range of topics, including uniform confidence interval construction, unit root tests allowing structural breaks, mildly explosive processes, exuberance testing, fractionally integrated processes, seasonal unit roots and panel unit root testing. Extensive, up to date, and readily accessible, this book is a comprehensive reference source on unit roots for both students and applied workers.
In Choi is a Professor of Economics at Sogang University in Seoul, South Korea. His research focus has been on time series and panel data analysis, and he has published numerous articles in leading journals in economics and statistics. He is a Fellow of the Journal of Econometrics and has received the Plura Scripsit Award from Econometric Theory and the Chongram Award from the Korean Economic Association. He is currently an associate editor of the Journal of Business and Economic Statistics.
1. Introduction; 2. Inference on unit roots: basic methods; 3. Unit root tests under various model specifications; 4. Alternative approaches to inference on unit roots; 5. Other issues related to unit roots; 6. Seasonal unit roots; 7. Panel unit roots.
'Choi's monograph provides an authoritative introduction to and survey of the literature on unit roots, which spans economics, statistics and other fields. Its handbook style and clear treatment make it an extremely convenient graduate-level reference for those working with highly persistent time series data.' J. Isaac Miller, University of Missouri
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