Description: In this edition which has been reprinted with corrections, Nerlove and his co-authors illustrate techniques of spectral analysis and methods based on parametric models in the analysis of economic time series. The book provides a means and a method for incorporating economic intuition and theory in the formulation of time-series models useful in forecasting, in the formulation and estimation of distributed lag models, and in other applications, such as seasonal adjustment. < B> Analysis of Economic Tim Series< /B> is a useful primary text for graduate students and an attractive reference for researchers. Key Features* Presents a self-contained treatment of Fourier Analysis and complex variables, as well as Spectral Analysis of time series* Includes a detailed treatment of unobserved-components (UC) models and their time-series properties by means of covariance-generating transforms* Provides the formulation and maximum-likelihood estimation of ARMA and UC models in both time and frequency domainsIntegrates several topics in time-series analysis: * The formulation and estimation of distributed-lag models of dynamic economic behavior* The application of the techniques of spectral analysis in the study of behavior of economic time series* Unobserved-components models for economic time series and the closely related problem of seasonal adjustment* The complimentarities between time-domain and frequency-domain approaches to the analysis of economic time series* Historical contributions extending from the time of Charles Babbage and the < I> Edinburgh Review< /I> to the present* Treats spectral analysis and Box-Jenkins models for an intuitive but rigorous point of view* Showshow these two types of analysis may be synthesized so that they complement one another* Describes a new type of model, based on a superposition of Box-Jenkins models, that captures the essential idea of the unobserved-components models long used in the analysis of economic time series* Applies multiple time-series techniques to the estimation of a novel dynamic model of the US cattle industry

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