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Estimation in Conditionally Herteroscedastic Time Series Models [Paperback]

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Item description for Estimation in Conditionally Herteroscedastic Time Series Models by Daniel Straumann...

In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).

This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.

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Item Specifications...

Pages   250
Est. Packaging Dimensions:   Length: 9.21" Width: 6.14" Height: 0.55"
Weight:   0.88 lbs.
Binding  Softcover
Release Date   Dec 22, 2004
Publisher   Springer
ISBN  3540211357  
ISBN13  9783540211358  

Availability  140 units.
Availability accurate as of Oct 23, 2016 09:59.
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1Books > Special Features > New & Used Textbooks > Business & Finance > Economics
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3Books > Subjects > Business & Investing > Economics > General
4Books > Subjects > Business & Investing > Economics > Statistics
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7Books > Subjects > Professional & Technical > Professional Science > Mathematics > Applied > Statistics
8Books > Subjects > Science > Mathematics > Applied > Probability & Statistics
9Books > Subjects > Science > Mathematics > General

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