'This book will be an excellent text for advanced undergraduate and postgraduate courses in econometric time series. The statistical theory is clearly presented and the many examples make the techniques readily accessible and illustrate their practical importance.' Andrew Harvey, University of Cambridge
'This book takes an important step forward relative to existing time-series econometrics texts, with, for example, significant coverage of numerical optimization, quasi-maximum-likelihood estimation, nonparametric and simulation-based estimation, latent-factor models, and volatility models. In addition, readers will benefit immensely from the complete sets of included R and Matlab routines. Well done!' Francis X. Diebold, University of Pennsylvania
'This book is exceptionally well done. The blending of theory, application and computation is sublimely done throughout. [It] will be a must-have for advanced graduate students working with economic and financial time series data, and will also form a definitive and up-to-date reference source for both academic and academic-related researchers in the field.' Robert Taylor, University of Nottingham
'This book gave me excitement and sensations similar to visiting Australian wineries: tantalizing vitality, pronounced yet balanced flavours, exposing exhilarating progressive developments, produced by excellent and tasteful craftsmanship, and well-matured and extremely consumer-friendly with its many recipes in various computer codes, thus it is strongly recommended to both young graduates and experienced connoisseurs.' Jan F. Kiviet, Nanyang Technological University and University of Amsterdam
'This textbook strikes an excellent balance between explaining the underlying concepts and intuition, containing the requisite amount of rigor, and providing sufficient guidance for students to be able to apply the methods described to a variety of time-series situations. It is extremely clearly written and should instantly find a wide audience. The book's emphasis on maximum-likelihood as a unifying guiding principle is well-justified, and provides the right context for students to understand how seemingly disparate econometric methods are fundamentally related.' Yacine Ait-Sahalia, Princeton University
