Time-Series Regression and Generalized Least Squares Appendix to An R and S-PLUS Companion to Applied Regression
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چکیده
with covariance matrix V (bOLS) = σ (XX) Let us, however, assume more generally that ε ∼ Nn(0,Σ), where the error-covariance matrix Σ is symmetric and positive-definite. Different diagonal entries in Σ correspond to non-constant error variances, while nonzero off-diagonal entries correspond to correlated errors. Suppose, for the time-being, that Σ is known. Then, the log-likelihood for the model is
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Time-Series Regression and Generalized Least Squares in R An Appendix to An R Companion to Applied Regression, Second Edition
Generalized least-squares (GLS ) regression extends ordinary least-squares (OLS) estimation of the normal linear model by providing for possibly unequal error variances and for correlations between different errors. A common application of GLS estimation is to time-series regression, in which it is generally implausible to assume that errors are independent. This appendix to Fox and Weisberg (2...
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