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Daniel Straumann - Estimation in Conditionally Heteroscedastic Time Series Models - 9783540211358 - V9783540211358
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Estimation in Conditionally Heteroscedastic Time Series Models

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Description for Estimation in Conditionally Heteroscedastic Time Series Models Paperback. Series: Lecture Notes in Statistics. Num Pages: 244 pages, biography. BIC Classification: PBWL. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 13. Weight in Grams: 780.

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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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, ... Read more

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Product Details

Format
Paperback
Publication date
2004
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
244
Condition
New
Series
Lecture Notes in Statistics
Number of Pages
228
Place of Publication
Berlin, Germany
ISBN
9783540211358
SKU
V9783540211358
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

Reviews for Estimation in Conditionally Heteroscedastic Time Series Models
From the reviews of the first edition: "The book deals with conditionally heteroscedastic time series models. It covers classical and new topics of parameter estimation in such models. … There are a lot of various examples and remarks which clarify the presented general results. Some numerical examples and simulations are given. Detailed discussions and comparisons ... Read more

Goodreads reviews for Estimation in Conditionally Heteroscedastic Time Series Models


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