Paul Knottnerus

Linear Models with Correlated Disturbances (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book is primarily concerned with the estimation of regression models with correlated disturbances. Topics discussed include maximum likelihood, test strategies, Kalman filtering, conditional normal distributions, the Cramér-Rao inequality, Cholesky decomposition, missing observations and numerical optimization. A simple geometrical approach is used.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 196
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783642483837
• Artikelnr.: 53099698

Inhaltsangabe

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.