In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.
In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of what I deem to be important but not readily available, or accessible to economists. For this reason I have excluded from the notes many results on various estimation methods or their statistical properties because they are amply discussed in many standard texts on time series or on statistics.
Dynamic Analysis of Open Economies focuses on the dynamic behavior of open economies in general, and dynamic interactions among several interconnected economies in particular. The emphasis is on the techniques of dynamic analysis and on the dynamic responses of models of open economies. This book is organized into three sections and consists of 15 chapters that examine how macroeconomic policy instruments affect open economies under flexible exchange rate regimes and the extent to which interdependence of national economies affects assessment of national policy effectiveness in a dynamic context. The behavior of open economies is analyzed not only at the instant of exogenous shocks or changes in instruments, but also after some time has elapsed since the last impacts. In considering the importance of dynamics, the book describes the behavior of a wide range of models and draws general conclusions. A set of techniques associated with variational analysis and perturbation theory is developed and systematically applied to models of open economies. This section also offers an analytical innovation for dealing with models of the world that are composed of several countries and demonstrates the usefulness of path controllability. The remaining chapters are devoted to models of small open economies and two- and multiple-country models of the world, paying particular attention to monetary policy and its distributional effects. Students and practitioners of applied mathematics and econometrics will find this book extremely helpful.
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