State-Space Methods for Time Series Analysis (PDF)
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Exploring the advantages of the state-space approach, this book presents numerous computational procedures that can be applied to a previously specified linear model in state-space form. It discusses model estimation and signal extraction; describes many procedures to combine, decompose, aggregate, and disaggregate a state-space form; and covers the connection between mainstream time series models and the state-space representation. Source code, a complete user manual, and other materials related to the authors' MATLAB® toolbox are available on a supplementary website.
Alfredo Garcia-Hiernaux is an associate professor of econometrics at Universidad Complutense de Madrid and a freelance consultant.
Miguel Jerez is an associate professor of econometrics at Universidad Complutense de Madrid and a freelance consultant. He was previously executive vice-president at Caja de Madrid for six years.
Sonia Sotoca is an associate professor of econometrics at Universidad Complutense de Madrid.
Drs. Casals, Garcia-Hiernaux, Jerez, and Sotoca are all engaged in a long-term research project to apply state-space techniques to standard econometric problems. Their common research interests include state-space methods and time series econometrics.
A. Alexandre (Alex) Trindade is a professor of statistics in the Department of Mathematics and Statistics at Texas Tech University and an adjunct professor in the Graduate School of Biomedical Sciences at Texas Tech University Health Sciences Center. His research spans a broad swath of theoretical and computational statistics.
- Autoren: Jose Casals , Alfredo Garcia-Hiernaux , Miguel Jerez , Sonia Sotoca , A. Alexandre Trindade
- 2018, 298 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1315362600
- ISBN-13: 9781315362601
- Erscheinungsdatum: 03.09.2018
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- Dateiformat: PDF
- Größe: 6.82 MB
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