Buch, Englisch, Band 15, 183 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1050 g
Numerical Methods and Estimation Problems
Buch, Englisch, Band 15, 183 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1050 g
Reihe: Advances in Computational Economics
ISBN: 978-0-7923-7720-7
Verlag: Springer US
The monograph is in two parts. The first part consists of four chapters and deals with the computational aspects for solving linear models that have applicability in diverse areas. The remaining two chapters form the second part, which concentrates on numerical and computational methods for solving various problems associated with seemingly unrelated regression equations (SURE) and simultaneous equations models.
The practical issues of the parallel algorithms and the theoretical aspects of the numerical methods will be of interest to a broad range of researchers working in the areas of numerical and computational methods in statistics and econometrics, parallel numerical algorithms, parallel computing and numerical linear algebra. The aim of this monograph is to promote research in the interface of econometrics, computational statistics, numerical linear algebra and parallelism.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftstheorie, Wirtschaftsphilosophie
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
Weitere Infos & Material
1. Linear Models and QR Decomposition.- 1 Introduction.- 2 Linear model specification.- 3 Forming the QR decomposition.- 4 Data parallel algorithms for computing the QR decomposition.- 5 QRD of large and skinny matrices.- 6 QRD of a set of matrices.- 2. Olm Not of Full Rank.- 1 Introduction.- 2 The QLD of the coefficient matrix.- 3 Triangularizing the lower trapezoid.- 4 Computing the orthogonal matrices.- 5 Discussion.- 3. Updating and Downdating The Olm.- 1 Introduction.- 2 Adding observations.- 3 Adding exogenous variables.- 4 Deleting observations.- 5 Deleting exogenous variables.- 4. The General Linear Model.- 1 Introduction.- 2 Parallel algorithms.- 3 Implementation and performance analysis.- 5. Sure Models.- 1 Introduction.- 2 The generalized linear least squares method.- 3 Triangular SURE models.- 4 Covariance restrictions.- 6. Simultaneous Equations Models.- 1 Generalized linear least squares.- 2 Modifying the SEM.- 3 Linear Equality Constraints.- 4 Computational Strategies.- References.- Author Index.