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Time Series Modelling with Unobserved Components (Paperback)
Loot Price: R1,477
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Time Series Modelling with Unobserved Components (Paperback)
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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. Unlike most other
books on time series, this text includes a chapter on prediction at
the beginning because the problem of predicting is not limited to
the field of time series analysis. The second part introduces the
UCM, the state space form, and related algorithms. It also provides
practical modeling strategies to build and select the UCM that best
fits the needs of time series analysts. The third part presents
real-world applications, with a chapter focusing on business cycle
analysis and the construction of band-pass filters using UCMs. The
book also reviews software packages that offer ready-to-use
procedures for UCMs as well as systems popular among statisticians
and econometricians that allow general estimation of models in
state space form. This book demonstrates the numerous benefits of
using UCMs to model time series data. UCMs are simple to specify,
their results are easy to visualize and communicate to
non-specialists, and their forecasting performance is competitive.
Moreover, various types of outliers can easily be identified,
missing values are effortlessly managed, and working
contemporaneously with time series observed at different
frequencies poses no problem.
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