Tucker S. McElroy, Dimitris N. Politis (2020): Time series: a first course with bootstrap starter
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Tucker S. McElroy, Dimitris N. Politis (2020): Time series: a first course with bootstrap starter CRC Press, Boca Raton, 586 pp. $ 79.96 (Hardcover), ISBN 978-1-4398-7651-0 Marco Meyer1 Received: 6 September 2020 / Revised: 15 September 2020 © The Author(s) 2020
Modern Time Series Analysis has been an important branch within the field of statistics for about the last 100 years, and a considerable number of classical textbooks has been written about this subject. Some of these books dating back to the 1970s are still widely considered as valuable sources of information for researchers and lecturers alike. These monographs can mostly be divided into two groups. The first is primarily directed at practitioners and usually focuses on a description of various methods to work on time series data while providing merely basic introduction to the theoretical background (if at all). The second usually contains a sound mathematical introduction to the subject which requires knowledge in probability theory, particularly in measure theory. Some textbooks in this second group are therefore excellent choices for researchers and for instructors of PhD level courses on time series, while being hardly accessible for undergraduate students or practitioners. This dilemma was the starting point for McElroy and Politis to design a textbook that fits right in between these two groups. As the authors put it in the Preface, “the challenge that we decided to undertake was to produce a text that satisfies the triptych: (i) mathematical completeness – albeit at a slightly lower level than [Brockwell and Davis (1991)], (ii) computational illustration and implementation, and (iii) conciseness and accessibility to upper-level undergraduate and M.S. students”. The result is a book that clearly achieves this ambitious goal. Its more than 500 pages are well spent as this presentation of time series analysis is almost completely self-contained, solely requiring basic knowledge in mathematical statistics. The authors take their time to motivate and explain basic concepts in a way that in many instances does not require measure theory or complex analysis. It has to be emphasized that this kind of accessibility does not come at the price of a lack of mathematical depth; this is one notable achievement that sets this textbook apart. Especially in the sections about
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Marco Meyer [email protected] TU Braunschweig, Braunschweig, Germany
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M. Meyer
spectral representation and entropy, some rather deep results are presented including their proofs which are left out in many other textbooks. Chapters and subsections with advanced content are clearly marked, and I particularly like the clear organization within the chapters. Each chapter ends with an overview which summarizes the most important concepts and “take home messages” from this section. This is followed up by a remarkable collection of exercises which are marked with different symbols as easy/difficult as well as theoretical/ computational. Also, almost all chapters contain examples abo
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