The material contained in this book originated in interrogations
about modern practice in time series analysis. * Why do we use
models optimized with respect to one-step ahead foreca- ing
performances for applications involving multi-step ahead forecasts?
* Why do we infer 'long-term' properties (unit-roots) of an unknown
process from statistics essentially based on short-term one-step
ahead forecasting performances of particular time series models? *
Are we able to detect turning-points of trend components earlier
than with traditional signal extraction procedures? The link
between 'signal extraction' and the first two questions above is
not immediate at first sight. Signal extraction problems are often
solved by su- ably designed symmetric filters. Towards the
boundaries (t = 1 or t = N) of a time series a particular symmetric
filter must be approximated by asymm- ric filters. The time series
literature proposes an intuitively straightforward solution for
solving this problem: * Stretch the observed time series by
forecasts generated by a model. * Apply the symmetric filter to the
extended time series. This approach is called 'model-based'.
Obviously, the forecast-horizon grows with the length of the
symmetric filter. Model-identification and estimation of unknown
parameters are then related to the above first two questions. One
may further ask, if this approximation problem and the way it is
solved by model-based approaches are important topics for practical
purposes? Consider some 'prominent' estimation problems: * The
determination of the seasonally adjusted actual unemployment rate.
General
Imprint: |
Springer-Verlag
|
Country of origin: |
Germany |
Series: |
Lecture Notes in Economics and Mathematical Systems, 547 |
Release date: |
October 2004 |
First published: |
2005 |
Authors: |
Marc Wildi
|
Dimensions: |
235 x 155 x 15mm (L x W x T) |
Format: |
Paperback
|
Pages: |
279 |
Edition: |
2005 ed. |
ISBN-13: |
978-3-540-22935-3 |
Categories: |
Books >
Business & Economics >
Economics >
Economic forecasting
Promotions
|
LSN: |
3-540-22935-3 |
Barcode: |
9783540229353 |
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