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Linear Models with Correlated Disturbances (Paperback, Softcover reprint of the original 1st ed. 1991)
Loot Price: R2,639
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Linear Models with Correlated Disturbances (Paperback, Softcover reprint of the original 1st ed. 1991)
Series: Lecture Notes in Economics and Mathematical Systems, 358
Expected to ship within 18 - 22 working days
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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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