Linear Models with Correlated Disturbances: Lecture Notes in Economics and Mathematical Systems, cartea 358
Autor Paul Knottnerusen Limba Engleză Paperback – 7 mai 1991
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Specificații
ISBN-13: 9783540539018
ISBN-10: 3540539018
Pagini: 212
Ilustrații: VIII, 196 p. 1 illus.
Dimensiuni: 170 x 244 x 11 mm
Greutate: 0.35 kg
Ediția:Softcover reprint of the original 1st ed. 1991
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Economics and Mathematical Systems
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540539018
Pagini: 212
Ilustrații: VIII, 196 p. 1 illus.
Dimensiuni: 170 x 244 x 11 mm
Greutate: 0.35 kg
Ediția:Softcover reprint of the original 1st ed. 1991
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Economics and Mathematical Systems
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
I Introduction.- II Transformation Matrices and Maximum Likelihood Estimation of Regression Models with Correlated Disturbances.- 2.1 Introduction.- 2.2 The algebraic problem.- 2.3 A dual problem.- 2.4 Recursive methods for calculating the transformation matrix P.- 2.5 The matrix P in the case of MA(1) disturbances.- 2.6 The matrix P in the case of MA(q) disturbances.- 2.7 The matrix P in the case of ARMA(p,q) disturbances.- Appendix 2. A Linear vector spaces.- Appendix 2.B The formula for ßtj if t is small.- III Computational Aspects of data Transformations and Ansley’s Algorithm.- 3.1 Introduction.- 3.2 Recursive computations for models with MA(q) disturbances.- 3.3 Recursive computations for models with ARMA(p,q) disturbances.- 3.4 Ansley’s method.- IV GLS Estimation by Kalman Filtering.- 4.1 Introduction.- 4.2 Some results from multivariate analysis.- 4.3 The Kaiman filter equations.- 4.4 The likelihood function.- 4.5 Estimation of linear models with ARMA(p,q) disturbances by means of Kaiman filtering.- 4.6 The exact likelihood function for models with ARMA(p,q) disturbances.- 4.7 Predictions and prediction intervals by using Kaiman filtering.- V Estimation of Regression Models with Missing Observations and Serially Correlated Disturbances.- 5.1 Introduction.- 5.2 The model.- 5.3 Derivation of the transformation matrix.- 5.4 Estimation and test procedures.- 5.5 Kaiman filtering with missing observations.- Appendix 5.A Stationarity conditions for an AR(2) process.- VI Distributed lag Models and Correlated Disturbances.- 6.1 Introduction.- 6.2 The geometric distributed lag model.- 6.3 Estimation methods.- 6.4 A simple formula for Koyck’s consistent two-step estimator.- 6.5 Efficient estimation of dynamic models.- 6.6 Dynamic models with several geometricdistributed lags.- 6.7 The Cramér-Rao inequality and the Pythagorean theorem.- VII Test Strategies for Discriminating Between Autocorrelation and Misspecification.- 7.1 Introduction.- 7.2 Thursby’s test strategy.- 7.3 Comments on Thursby’s test strategy.- 7.4 Godfrey’s test strategy.- 7.5 Comments on Godfrey’s test strategy.- References.- Author Index.