Time series modelling with unobserved components

Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community. Time Series Modelling with Unobserved Components...

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Bibliographic Details
Main Author: Pelagatti, Matteo M. (Author)
Format: Book
Language:English
Published: Boca Raton, FL CRC Press LLC [2016]
Subjects:
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Call Number :QA 280 .P45 2016

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100 1 |a Pelagatti, Matteo M.  |e author 
245 1 0 |a Time series modelling with unobserved components  |c Matte M. Pelagatti 
264 1 |a Boca Raton, FL  |b CRC Press LLC  |c [2016] 
264 4 |c ©2016 
300 |a xvii, 257 pages  |b illustrations  |c 24 cm 
336 |a text  |2 rdacontent 
337 |a unmediated  |2 rdamedia 
338 |a volume  |2 rdacarrier 
504 |a Includes bibliographical references and index 
505 0 |a Part 1 Statistical prediction and time series -- Statistical Prediction -- Time Series Concepts -- Part 2 Unobserved components -- Unobserved Components Model -- Regressors and Interventions -- Estimation -- Modelling -- Multivariate Models -- Part 3 Applications -- Business Cycle Analysis with UCM -- Case Studies -- Software for UCM 
520 |a Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community. Time Series Modelling with Unobserved Components rectifies this deficiency by giving a practical overview of the UCM approach, covering some theoretical details, several applications, and the software for implementing UCMs.The book's first part discusses introductory time series and prediction theory 
592 |a 32587  |b 8/12/2016  |c RM 358.80  |h Bookline 
650 0 |a Time-series analysis 
650 0 |a Missing observations (Statistics) 
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