linear models correlated disturbances (20 Ergebnisse)

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Taschenbuch. Zustand: Neu. Linear Models with Correlated Disturbances | Paul Knottnerus | Taschenbuch | viii | Englisch | 1991 | Springer | EAN 9783540539018 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In each chapter of this volume some specific topics in the econometric analysis of time series data are studied. All topics have in common the statistical inference in linear models with correlated disturbances. The main aim of the study is to give a survey of new and old estimation techniques for regression models with disturbances that follow an autoregressive-moving average process. In the final chapter also several test strategies for discriminating between various types of autocorrelation are discussed. In nearly all chapters it is demonstrated how useful the simple geometric interpretation of the well-known ordinary least squares (OLS) method is. By applying these geometric concepts to linear spaces spanned by scalar stochastic variables, it emerges that well-known as well as new results can be derived in a simple geometric manner, sometimes without the limiting restrictions of the usual derivations, e. g. , the conditional normal distribution, the Kalman filter equations and the Cramer-Rao inequality. The outline of the book is as follows. In Chapter 2 attention is paid to a generalization of the well-known first order autocorrelation transformation of a linear regression model with disturbances that follow a first order Markov scheme. Firstly, the appropriate lower triangular transformation matrix is derived for the case that the disturbances follow a moving average process of order q (MA(q'. It turns out that the calculations can be carried out either analytically or in a recursive manner.

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the Present Book Chapter-I is an introductory one. It contains the general introduction about the problem of autocorrelation . Chapter-II presents statistical inferential problems in linear models. It explains the specification of classical linear regression model together with its estimation. Chapter-III describes the review about inferential methods in linear models under the problem of autocorrelation. Chapter-IV proposes some alternative inferential methods for linear model with autocorrelated disturbances. It uses the various types of residuals such as ordinary least squares, studentized and predicted residuals to develop alternative iterative estimation methods and tests for the autocorrelation. Chapter-V depicts the conclusions. Several selected references for the present book are given under the title 'BIBLIOGRAPHY' 144 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chalapathi Rao M.V.He is Presently working as a Reader in Jawahar Bharati Degree/P.G College (NACC-A Grade & CPE),Kavali.He has 27 years Teaching & Research Experience in the Field of Statistics.He Presented Several Research Papers.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the Present Book Chapter-I is an introductory one. It contains the general introduction about the problem of autocorrelation . Chapter-II presents statistical inferential problems in linear models. It explains the specification of classical linear regression model together with its estimation. Chapter-III describes the review about inferential methods in linear models under the problem of autocorrelation. Chapter-IV proposes some alternative inferential methods for linear model with autocorrelated disturbances. It uses the various types of residuals such as ordinary least squares, studentized and predicted residuals to develop alternative iterative estimation methods and tests for the autocorrelation. Chapter-V depicts the conclusions. Several selected references for the present book are given under the title 'BIBLIOGRAPHY'Books on Demand GmbH, Überseering 33, 22297 Hamburg 144 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the Present Book Chapter-I is an introductory one. It contains the general introduction about the problem of autocorrelation . Chapter-II presents statistical inferential problems in linear models. It explains the specification of classical linear regression model together with its estimation. Chapter-III describes the review about inferential methods in linear models under the problem of autocorrelation. Chapter-IV proposes some alternative inferential methods for linear model with autocorrelated disturbances. It uses the various types of residuals such as ordinary least squares, studentized and predicted residuals to develop alternative iterative estimation methods and tests for the autocorrelation. Chapter-V depicts the conclusions. Several selected references for the present book are given under the title 'BIBLIOGRAPHY'.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In each chapter of this volume some specific topics in the econometric analysis of time series data are studied. All topics have in common the statistical inference in linear models with correlated disturbances. The main aim of the study is to give a survey of new and old estimation techniques for regression models with disturbances that follow an autoregressive-moving average process. In the final chapter also several test strategies for discriminating between various types of autocorrelation are discussed. In nearly all chapters it is demonstrated how useful the simple geometric interpretation of the well-known ordinary least squares (OLS) method is. By applying these geometric concepts to linear spaces spanned by scalar stochastic variables, it emerges that well-known as well as new results can be derived in a simple geometric manner, sometimes without the limiting restrictions of the usual derivations, e. g. , the conditional normal distribution, the Kalman filter equations and the Cramer-Rao inequality. The outline of the book is as follows. In Chapter 2 attention is paid to a generalization of the well-known first order autocorrelation transformation of a linear regression model with disturbances that follow a first order Markov scheme. Firstly, the appropriate lower triangular transformation matrix is derived for the case that the disturbances follow a moving average process of order q (MA(q'. It turns out that the calculations can be carried out either analytically or in a recursive manner. 212 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In each chapter of this volume some specific topics in the econometric analysis of time series data are studied. All topics have in common the statistical inference in linear models with correlated disturbances. The main aim of the study is to give a survey.

Zustand: New. Print on Demand pp. 212 67:B&W 6.69 x 9.61 in or 244 x 170 mm (Pinched Crown) Perfect Bound on White w/Gloss Lam.

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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In each chapter of this volume some specific topics in the econometric analysis of time series data are studied. All topics have in common the statistical inference in linear models with correlated disturbances. The main aim of the study is to give a survey of new and old estimation techniques for regression models with disturbances that follow an autoregressive-moving average process. In the final chapter also several test strategies for discriminating between various types of autocorrelation are discussed. In nearly all chapters it is demonstrated how useful the simple geometric interpretation of the well-known ordinary least squares (OLS) method is. By applying these geometric concepts to linear spaces spanned by scalar stochastic variables, it emerges that well-known as well as new results can be derived in a simple geometric manner, sometimes without the limiting restrictions of the usual derivations, e. g. , the conditional normal distribution, the Kalman filter equations and the Cramer-Rao inequality. The outline of the book is as follows. In Chapter 2 attention is paid to a generalization of the well-known first order autocorrelation transformation of a linear regression model with disturbances that follow a first order Markov scheme. Firstly, the appropriate lower triangular transformation matrix is derived for the case that the disturbances follow a moving average process of order q (MA(q». It turns out that the calculations can be carried out either analytically or in a recursive manner.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 212 pp. Englisch.